├── LICENSE ├── LearnItYourself ├── Beginners │ ├── Chapter1.md │ ├── Chapter2.md │ ├── Chapter3.md │ ├── Chapter5.md │ ├── README.md │ └── __future__ │ │ ├── Chapter6 │ │ ├── ChapterX.md │ │ ├── ChapterY.md │ │ └── ChapterZ.md └── Readme.md ├── Python └── Data Manipulation and Viz with Dplyr and Ggplot │ ├── README.md │ ├── dplyr.ipynb │ ├── dplyr_participants.ipynb │ ├── file.csv │ ├── ggplot2.ipynb │ ├── ggplot2_participants.ipynb │ └── train.csv ├── R └── Introduction to R │ ├── R for data cleaning.pdf │ ├── README.md │ ├── iris.csv │ └── iris_2.csv └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 DataScience SG 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /LearnItYourself/Beginners/Chapter1.md: -------------------------------------------------------------------------------- 1 | # Overview 2 | 3 | - **Chapter 1: Introduction to Deep Learning** 4 | - 1.1: *Linear Models and Gradient Descent* 5 | - Binary Classification 6 | - Logistic Regression 7 | - Gradient Descent from Scratch 8 | 9 | - 1.2: *Machine Learning Frameworks* 10 | - Deep Learning 11 | - Machine Learning 12 | - Probablistic Learning 13 | 14 | - 1.3: *Deep Learning (for real)* 15 | 16 | ---- 17 | 18 | ### 1.1: Linear Models and Gradient Descent 19 | 20 | - *Binary Classification* 21 | - https://machinelearningmastery.com/binary-classification-tutorial-with-the-keras-deep-learning-library/ 22 | - https://www.digitalocean.com/community/tutorials/how-to-build-a-machine-learning-classifier-in-python-with-scikit-learn 23 | - https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py 24 | - https://www.tensorflow.org/tutorials/keras/basic_text_classification 25 | - https://www.youtube.com/watch?v=eqEc66RFY0I (Andrew Ng's Deep Learning AI Course materials) 26 | 27 | - *Logistic Regression* 28 | - [Machine Learning: Regression](https://www.coursera.org/learn/ml-regression) by University of Washington on Coursera 29 | - https://www.machinelearningplus.com/machine-learning/logistic-regression-tutorial-examples-r/ 30 | - https://machinelearningmastery.com/logistic-regression-tutorial-for-machine-learning/ 31 | - https://towardsdatascience.com/building-a-logistic-regression-in-python-step-by-step-becd4d56c9c8 32 | - https://towardsdatascience.com/logistic-regression-using-python-sklearn-numpy-mnist-handwriting-recognition-matplotlib-a6b31e2b166a 33 | 34 | - *Gradient Descent from Scratch* 35 | - https://gluon.mxnet.io/chapter06_optimization/gd-sgd-scratch.html 36 | - https://machinelearningmastery.com/implement-linear-regression-stochastic-gradient-descent-scratch-python/ 37 | - http://datacognizant.com/wp-content/uploads/2017/03/LogisticRegression_loglikelihood_derivative_gradientdescent-1.html (althogh `graphlab` is no longer maintained the exercise for gradient descent is valid) 38 | 39 | 40 | **References:** 41 | 42 | - [Introduction to Machine Learning with Python](http://shop.oreilly.com/product/0636920030515.do) by Sarah Guido, Andreas Müller 43 | - [Hands-On Machine Learning with Scikit-Learn and TensorFlow](http://shop.oreilly.com/product/0636920052289.do) by Aurélien Géron 44 | - [Data Science from Scratch](http://shop.oreilly.com/product/0636920033400.do) by Joel Grus 45 | - [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas 46 | - [Programming Collective Intelligence](http://shop.oreilly.com/product/9780596529321.do) by Toby Segaran (a little outdated by if you like to code by example, this is perfect) 47 | - [Machine Learning in Action](https://www.manning.com/books/machine-learning-in-action) by Peter Harrington 48 | 49 | **Words of advice:** 50 | 51 | - If you haven't coded a gradient descent from scratch before you should really try. 52 | - If you can, join the University of Washington's regression course reference above, it's really good. If you can't afford it, try "audit the course" button or apply for the financial aid to enroll 53 | - If you like phyiscal books, start with the reference books above, you don't really have to buy them if you're residing locally, the national library board (in Singapore) have most them. If not you can download the NLB app and recommend the librarians to get it. =) 54 | 55 | ---- 56 | 57 | ### 1.2: Introduction to Machine Learning Frameworks 58 | 59 | - *Deep Learning* 60 | - [Keras] https://web.stanford.edu/class/cs20si/lectures/march9guestlecture.pdf 61 | - [Keras] https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/ 62 | - [Tensorflow] https://towardsdatascience.com/a-beginner-introduction-to-tensorflow-part-1-6d139e038278 63 | - [Tensorflow] https://cs230-stanford.github.io/tensorflow-getting-started.html 64 | - [Tensorboard] https://itnext.io/how-to-use-tensorboard-5d82f8654496 65 | - [Tensorboard] https://www.youtube.com/watch?v=eBbEDRsCmv4 66 | - [PyTorch] https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html 67 | - [PyTorch] https://towardsdatascience.com/pytorch-tutorial-distilled-95ce8781a89c 68 | - [PyTorch] https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html (specific to Computer Vision) 69 | - [FastAI] https://www.fast.ai/ 70 | - [DyNet] https://github.com/clab/dynet_tutorial_examples (specific to NLP) 71 | - *Machine Learning* 72 | - [scikit-learn] https://scikit-learn.org/stable/tutorial/index.html 73 | - [Vowpal Wabbit] https://github.com/VowpalWabbit/vowpal_wabbit/wiki 74 | - There's a couple on this list too http://www.erogol.com/broad-view-machine-learning-libraries/ 75 | - *Probabilistic Learning* 76 | - [PyMC3] https://docs.pymc.io/ 77 | - [Stan] https://mc-stan.org/ 78 | - [Statsmodels] http://www.statsmodels.org/stable/index.html 79 | - [Tensorflow Probability] https://www.tensorflow.org/probability/ , see also http://dustintran.com/talks/Tran_Edward.pdf 80 | 81 | **References** 82 | 83 | - Tensorflow course on Udacity: https://www.udacity.com/course/deep-learning--ud730 (Knowledge is free, certification needs payment) 84 | - PyTorch course on Udacity https://in.udacity.com/course/deep-learning-pytorch--ud188 85 | - Fast AI https://www.fast.ai/ 86 | - Bayesian analysis https://sites.google.com/site/doingbayesiandataanalysis/what-s-new-in-2nd-ed 87 | - Neural Networks and Deep Learning http://neuralnetworksanddeeplearning.com/ 88 | 89 | **Words of advice:** 90 | 91 | - Best help on the framework you get would be the dev-users communication channels that are in place for the specific frameworks 92 | - [PyTorch] https://discuss.pytorch.org/ 93 | - [Tensorflow/Keras] https://www.tensorflow.org/community/ (mainly stackoverflow, just tag your question with `tensorflow` tag) 94 | - [Fast AI] https://forums.fast.ai/ 95 | - Knowing tensorboard will get you far, do take a look at https://www.youtube.com/watch?v=eBbEDRsCmv4 96 | - The video/book tutorials in the references above are great start to the frameworks, investing time in them will save you time learning the way of the hard knocks later. 97 | 98 | ---- 99 | 100 | 101 | ### 1.3: Deep Learning (for real) 102 | 103 | Honestly, if you're only learning how to use frameworks and knowing which models/layers to use to specific tasks, it's cool and most probably you get something working. 104 | 105 | Knowing how to debug when training the model, how to evaluate models and how to improve the models has little to do with whether you know the syntax of the frameworks or stack the layers properly. 106 | 107 | My suggestion is to go through the introduction courses listed in the reference list in 1.2 tediously. 108 | 109 | - Don't just watch the videos and `shift`+`enter` through the notebooks 110 | - Do change some values in the notebooks and see how and understand why the function/layer/outputs reacts to the changes 111 | - Don't keep to yourself if you're stuck, go to the respective forums of the framework to look for help 112 | - Do be nice and descriptive when asking for help =) 113 | 114 | 115 | If you want a real deep dive to "Deep Learning (for real)", many good universities have their course materials freely available, there's no need to really pay someone to regurgitate them to you ;p 116 | 117 | - Stanford Deep Learning Course http://cs230.stanford.edu/syllabus.html 118 | - MIT Deep Learning Course http://introtodeeplearning.com/2018/index.html 119 | - CMU Deep Learning Course http://deeplearning.cs.cmu.edu/ 120 | - Berkley Deep Learning Course https://berkeley-deep-learning.github.io/ 121 | - [Dive into Deep Learning](http://d2l.ai/index.html) interactive book 122 | - Princeton Deep Learning Course https://www.cs.princeton.edu/courses/archive/spring16/cos495/ 123 | - UPenn Machine Learning Course: https://www.seas.upenn.edu/~cis519/fall2018/syllabus.html 124 | 125 | 126 | -------------------------------------------------------------------------------- /LearnItYourself/Beginners/Chapter2.md: -------------------------------------------------------------------------------- 1 | # Overview 2 | 3 | - **Chapter 2: Computer Vision and Convolutional Neural Net** 4 | - 2.1: *Introduction to Computer Vision* 5 | 6 | - 2.2: *Convolutional Neural Net* 7 | - Blogposts that presents CNN 8 | - Show me the Code 9 | - Show me the Math 10 | 11 | - 2.3 *Visualizing CNN* 12 | 13 | - 2.4: *Convolutional Neural Net (for real)* 14 | 15 | ---- 16 | 17 | ### 2.1: Introduction to Computer Vision 18 | 19 | - Georgia Tech Course: https://www.cc.gatech.edu/~hays/compvision/ 20 | - Yet another Georgia Tech Course: https://samyak-268.github.io/F18CS4476/ 21 | - Udacity Intro to Computer Vision: https://classroom.udacity.com/courses/ud810 22 | - KAIST Course: http://vclab.kaist.ac.kr/cs484/index.html 23 | - MIT computer Vision class: https://www.youtube.com/watch?v=CLOAswsxudo (part of [self-driving cars course](https://selfdrivingcars.mit.edu/)) 24 | - Stanford course http://cs231n.stanford.edu/ 25 | 26 | ---- 27 | 28 | 29 | ### 2.2: Convolutional Neural Nets (CNN) 30 | 31 | - *Blogposts that presents CNN* 32 | - https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050 33 | - https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ 34 | - https://skymind.ai/wiki/convolutional-network 35 | - https://towardsdatascience.com/convolutional-neural-networks-from-the-ground-up-c67bb41454e1 36 | - https://www.analyticsvidhya.com/blog/2018/12/guide-convolutional-neural-network-cnn/ (Quite a lot of ads on the page though) 37 | - https://medium.com/@RaghavPrabhu/understanding-of-convolutional-neural-network-cnn-deep-learning-99760835f148 38 | - https://developer.nvidia.com/discover/convolutional-neural-network (at the end of the post, there's a few links that are really good for further readings about CNN) 39 | 40 | - *Show me the Code* 41 | - https://www.tensorflow.org/tutorials/estimators/cnn 42 | - http://adventuresinmachinelearning.com/keras-tutorial-cnn-11-lines/ (the pop-up advertises his book though...) 43 | - https://www.kaggle.com/cdeotte/how-to-choose-cnn-architecture-mnist 44 | - https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py 45 | - https://medium.com/tensorflow/hello-deep-learning-fashion-mnist-with-keras-50fcff8cd74a 46 | - https://github.com/MorvanZhou/PyTorch-Tutorial/blob/master/tutorial-contents/401_CNN.py 47 | 48 | - *Show me the Math* (CNN Arithmetic) 49 | - https://arxiv.org/pdf/1603.07285v1.pdf 50 | 51 | **Reference** 52 | - Udacity PyTorch course: https://classroom.udacity.com/courses/ud188 53 | - Udacity Tensorflow course: https://classroom.udacity.com/courses/ud730 54 | - Stanford Course http://cs231n.github.io/convolutional-networks/ 55 | - Kaggle Learn https://www.kaggle.com/learn/deep-learning 56 | 57 | ---- 58 | 59 | 60 | ### 2.3: Visualizing Convolutional Neual Net 61 | 62 | - Visualizing Convolution Filters 63 | - https://hackernoon.com/visualizing-parts-of-convolutional-neural-networks-using-keras-and-cats-5cc01b214e59 64 | - https://www.youtube.com/watch?v=McgxRxi2Jqo 65 | - https://distill.pub/2017/feature-visualization/ 66 | - http://cs231n.github.io/understanding-cnn/ 67 | 68 | - Visualizing Intermediate Activation 69 | - https://towardsdatascience.com/visualizing-intermediate-activation-in-convolutional-neural-networks-with-keras-260b36d60d0 70 | - https://www.youtube.com/watch?v=ghEmQSxT6tw 71 | - http://yosinski.com/deepvis and https://github.com/yosinski/deep-visualization-toolbox 72 | - https://github.com/utkuozbulak/pytorch-cnn-visualizations 73 | 74 | - Visualizing Class Activation Maps 75 | - http://cnnlocalization.csail.mit.edu/ 76 | - https://jacobgil.github.io/deeplearning/class-activation-maps 77 | - https://github.com/TheShadow29/FAI-notes/blob/master/notebooks/Using-CAM-for-CNN-Visualization.ipynb 78 | - https://github.com/trancept/deep_learning_tests/blob/master/015-HeatMap.ipynb 79 | 80 | ---- 81 | 82 | ### 2.4: Convolutional Neural Net (for real) 83 | 84 | Frankly, blogposts are nice as an idea introducer. After that one should either dive into the code or get onto the free classes (in the 2.2 reference list above) to understand the details of why and how CNNs work. CNN are powerful tools that can solve many problems (even outside of computer vision). Usually, any of the courses listed above should cover: 85 | 86 | - What is the covolution filter? 87 | - What is a fully connected layer? What is pooling? What is the difference? 88 | - What are the different "dimensions" (1D, 2D, 3D, nD convnets), "channels" in convolution neural nets? 89 | - How to not overfit? (the measures to prevent overfit is aka regularization) 90 | - How to use dropout as a regularization measure? 91 | - How to apply simple convolution neural nets to image classification task? 92 | 93 | Sometimes, there'll be more practical knowledge in the classes above too: 94 | 95 | - How to apply deeper CNN architectures to image classification task? 96 | - How to use pre-trained models from other people to do tansfer learning? 97 | 98 | After knowing what CNN layers does and what convolutional filters are and CNN arithmetics in details, you might not go far. To really understand CNNs and make them work, most probably you need some understanding about state of art variants of CNNs and how CNNs are stacked to form bigger architectures, e.g. Resnet, VGGnet, Inception, YOLO etc. should be the end goal one should seek. And to understand these new architectures, normally Googling these terms with your deep learning framework of choice would point to good tutorial/code that you can read, e.g. "Resnet pytorch", "inception keras". 99 | 100 | My suggestion is to go through the CNN class in the courses listed in the reference list in 2.2 tediously. And here's some pointers: 101 | 102 | - Do learn about *transfer learning* to save you on GPU computation cost, your precious time and sanity =) 103 | - FYI, *transfer learning* in CNN and computer vision is aka freeze some/most layers, unfreeze usually the last one or few layers 104 | - Do try to learn about new variants on top of CNN and architectures other than CNN through arixv or new reseach papers 105 | - Usually when most bootcamps/coding courses prepare the materials for a certain well know architecture, you can most probably learn about it by googling the architecture name with the DL framework of your choice. 106 | - Interpretability is the next hot thing in machine learning because of our need to know why things works and explain it (so that we don't cause robo-apocalypes ;P). So in terms of CNN and Computer Vision, naturally model and its component visualization is important. 107 | 108 | P/S: 109 | 110 | I do see some DL training courses touch of state-of-the-art or fresh out of the oven architectures and those usually leave me impressed. Otherwise if it's simply introducing CNN, there's more than enough information out there to learn for free =) 111 | 112 | Also, sometimes you see coding courses/bootcamps giving a lot of new terminology in their syllabus. Most of those times, they are jargons that can be explained simply, don't be afraid to ask on the Data Science SG Facebook about what these terms are or how to find resources that can help explain those terms. For example, the course syllabus might throw you a list of jargon `["Adam", "Adagrad", "AdaDelta", "AdaMax", "NAdam", "RMSprop", "SGD", "WAdam"]`, indeed there are important differences between these names but they achieve the same purpose of updating gradients so that the model can update the parameters, i.e. optimizing the model (and that's why they're called optimizer). If the course briefly explains the difference and simply just goes through the fact that they exist in the deep learning library that you're using and show you the following images, then you're better off (i) learning to code gradient descent from scratch, then (ii) just reading [Sebastian Ruder's blogpost](http://ruder.io/optimizing-gradient-descent/index.html#visualizationofalgorithms): 113 | 114 | ![](http://ruder.io/content/images/2016/09/contours_evaluation_optimizers.gif) 115 | 116 | ![](http://ruder.io/content/images/2016/09/saddle_point_evaluation_optimizers.gif) 117 | 118 | 119 | 120 | -------------------------------------------------------------------------------- /LearnItYourself/Beginners/Chapter3.md: -------------------------------------------------------------------------------- 1 | # Overview 2 | 3 | - **Chapter 3: Natural Language Processing, Recurrent Neural Nets and Self-Attention Nets** 4 | 5 | - 3.1: *Introduction to Natural Language Processing* 6 | - The Get Things Done Way 7 | - The Slow and Steady Way 8 | 9 | 10 | - 3.2 *Recurrent Neural Nets* 11 | - Blogposts that presents RNN 12 | - Show me the Code 13 | - Show me the Math 14 | 15 | - 3.3 *Self-Attention Nets* (aka Transformer) 16 | - Blogposts that presents Transformer 17 | - Show me the Code 18 | - Show me the Math 19 | 20 | - 3.4: *Natural Language Processing (for real)* 21 | 22 | 23 | **Words of advice:** 24 | 25 | - NLP is kind of niche but still a pretty wide subfield, knowing little goes a long way but understanding the basics goes a longer way. 26 | - The free courses listed in Secion 3.1 require quite a lot of committment, if you're looking for a quick way to break-in to NLP, pick one of the tools from `The Get Things Done Way` section and start from there, while you work through one of the courses listed above. 27 | - P/S: You don't have to know everything in NLP or all the tools but it's good to know that alternative tools exists 28 | - Read the `NLP (for real)` section =) 29 | 30 | 31 | ---- 32 | 33 | 34 | ### 3.1: **Introduction to Natural Language Processing** 35 | 36 | **The Get Things Done Way** 37 | - PyTorch Tutorials: 38 | - https://pytorch.org/tutorials/beginner/deep_learning_nlp_tutorial.html (take a look at others under the `Text` section on the left panel too) 39 | - https://github.com/pytorch/text 40 | - https://github.com/pytorch/fairseq 41 | - http://mlexplained.com/2018/02/08/a-comprehensive-tutorial-to-torchtext/ 42 | - https://github.com/ritchieng/the-incredible-pytorch 43 | - AllenNLP Tutorials: 44 | - https://allennlp.org/tutorials and [EMNLP 2018 tutorial](https://github.com/allenai/writing-code-for-nlp-research-emnlp2018/blob/master/writing_code_for_nlp_research.pdf) 45 | - FastAI Tutorials: 46 | - http://nlp.fast.ai/ 47 | - Keras NLP Tutorials: 48 | - https://realpython.com/python-keras-text-classification/ 49 | - https://nlpforhackers.io/keras-intro/ 50 | - https://machinelearningmastery.com/develop-word-based-neural-language-models-python-keras/ 51 | - DyNet Tutorials: 52 | - https://dynet.readthedocs.io/en/latest/tutorial.html 53 | - [EMNLP 2016 Tutorial](https://github.com/clab/dynet_tutorial_examples) 54 | - SpaCy Tutorials 55 | - https://spacy.io/usage/spacy-101 56 | - https://explosion.ai/blog/deep-learning-formula-nlp 57 | - https://www.youtube.com/watch?v=vrtTAeBLElw 58 | - https://nlpforhackers.io/complete-guide-to-spacy/ 59 | - Gensim Tutorials 60 | - https://radimrehurek.com/gensim/tutorial.html 61 | - https://lilianweng.github.io/lil-log/2017/10/15/learning-word-embedding.html 62 | - https://machinelearningmastery.com/develop-word-embeddings-python-gensim/ 63 | - https://towardsdatascience.com/topic-modelling-in-python-with-nltk-and-gensim-4ef03213cd21 64 | - NLTK Basics (the useful bits): 65 | - http://www.nltk.org/howto/ 66 | - https://www.kaggle.com/alvations/basic-nlp-with-nltk 67 | 68 | **The Slow and Steady Way** 69 | - Stanford Course: https://web.stanford.edu/class/cs224n/ 70 | - Berkley Course: http://people.ischool.berkeley.edu/~dbamman/nlp18.html 71 | - CMU Course: http://phontron.com/class/nn4nlp2017/ 72 | - Oxford Course: https://www.cs.ox.ac.uk/teaching/courses/2016-2017/dl/ 73 | - [Lisbon Machine Learning School (LxMLS 2018)](http://lxmls.it.pt/2018/?page_id=19) (focuses a lot on NLP in 2018) 74 | - Saarland University [Foundations in Language Sciencea and Technology (FLST) course](http://www.coli.uni-saarland.de/courses/FLST/2018/FLST.html) 75 | - National University of Singapore (NUS) [Deep Learning for NLP Course](https://www.comp.nus.edu.sg/~kanmy/courses/6101_1810/) 76 | 77 | 78 | **References** 79 | - Books / Articles 80 | - [Speech and Language Processing](https://web.stanford.edu/~jurafsky/slp3/) (Jurafsky and Martin's Book) 81 | - [Foundations of Statistical Natural Language Processing](https://nlp.stanford.edu/fsnlp/) (Chris Manning's book, hardcore math) 82 | - [Computational Linguistics and Deep Learning](https://www.mitpressjournals.org/doi/pdf/10.1162/COLI_a_00239) (Chris Manning's words in 2015) 83 | - [A Primer on Neural Network Models for Natural Language Processing](https://u.cs.biu.ac.il/~yogo/nnlp.pdf) (Yoav Goldberg's book/chapter) 84 | - physical book version on https://www.morganclaypool.com/doi/abs/10.2200/S00762ED1V01Y201703HLT037 85 | - [Jacob Einstein's notes on NLP and DL](https://github.com/jacobeisenstein/gt-nlp-class/blob/master/notes/eisenstein-nlp-notes.pdf) 86 | - [Natural Language Processing with PyTorch](http://shop.oreilly.com/product/0636920063445.do) 87 | - [Deep Learning with Text](http://shop.oreilly.com/product/0636920076063.do) 88 | - Related but not directly 89 | - [Artificial Intelligence: A Modern Approach](http://aima.cs.berkeley.edu/) (Russell and Norvig's book) 90 | - Borrow from national library, click [here](https://catalogue.nlb.gov.sg/cgi-bin/spydus.exe/FULL/WPAC/BIBENQ/13461273/269039522,1) =) 91 | - [Deep Learning](https://www.deeplearningbook.org/) (Goodfellow et al. book) 92 | 93 | 94 | 95 | ---- 96 | 97 | 98 | ### 3.2: **Recurrent Neural Nets** 99 | 100 | - Blogpost that explains RNN 101 | - http://colah.github.io/posts/2015-08-Understanding-LSTMs/ 102 | - http://karpathy.github.io/2015/05/21/rnn-effectiveness/ 103 | - http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/ 104 | - http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/ 105 | - https://medium.com/@erikhallstrm/hello-world-rnn-83cd7105b767 106 | - https://skymind.ai/wiki/lstm 107 | - **On Attention** 108 | - https://medium.com/syncedreview/a-brief-overview-of-attention-mechanism-13c578ba9129 109 | - http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/ 110 | - https://skymind.ai/wiki/attention-mechanism-memory-network 111 | - https://machinelearningmastery.com/attention-long-short-term-memory-recurrent-neural-networks/ 112 | 113 | - Show me the code 114 | - https://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/ 115 | - http://www.jessicayung.com/lstms-for-time-series-in-pytorch/ 116 | - https://medium.com/@shivambansal36/language-modelling-text-generation-using-lstms-deep-learning-for-nlp-ed36b224b275 117 | - https://adventuresinmachinelearning.com/keras-lstm-tutorial/ 118 | - https://www.depends-on-the-definition.com/attention-lstm-relation-classification/ 119 | - For more, see above `The Get Things Done Way` (Section 3.1) 120 | 121 | - Show me the math 122 | - https://www.coursera.org/lecture/nlp-sequence-models/why-sequence-models-0h7gT 123 | - http://arunmallya.github.io/writeups/nn/lstm/index.html#/ (move forward the page by clicking the arrow on bottom right) 124 | - https://www.cs.toronto.edu/~tingwuwang/rnn_tutorial.pdf 125 | - https://brilliant.org/wiki/recurrent-neural-network/ 126 | - http://www.cs.bham.ac.uk/~jxb/INC/l12.pdf 127 | - https://www.kth.se/polopoly_fs/1.801971!/deep%20learning.pdf 128 | - https://arxiv.org/pdf/1803.06396.pdf 129 | - Independent RNN https://arxiv.org/abs/1803.04831 (Not really for beginners but this is like the AlexNet of RNNs) 130 | - RNN Paper: https://www.mitpressjournals.org/doi/abs/10.1162/neco.1997.9.8.1735 131 | - LSTM Paper: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.676.4320&rep=rep1&type=pdf 132 | - GRU Paper: https://arxiv.org/pdf/1502.02367v4.pdf 133 | - Alex Grave's Thesis: http://www.cs.toronto.edu/~graves/phd.pdf 134 | 135 | ---- 136 | 137 | 138 | ### 3.3: **Self-Attention Nets** (aka Transformer) 139 | 140 | - Blogpost that explains Self-Attention Nets 141 | - http://jalammar.github.io/illustrated-transformer/ 142 | - https://ai.googleblog.com/2017/08/transformer-novel-neural-network.htm 143 | - https://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html 144 | - https://mchromiak.github.io/articles/2017/Sep/12/Transformer-Attentionis-all-you-need/ 145 | - https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html (Different attention) 146 | 147 | 148 | - Show me the code 149 | - Original Tensor2Tensor library: https://github.com/tensorflow/tensor2tensor 150 | - http://nlp.seas.harvard.edu/2018/04/03/attention.html 151 | - https://medium.com/@kolloldas/building-the-mighty-transformer-for-sequence-tagging-in-pytorch-part-i-a1815655cd8 152 | - https://towardsdatascience.com/how-to-code-the-transformer-in-pytorch-24db27c8f9ec 153 | 154 | 155 | - Show me the math 156 | - Attention Is All You Need paper: https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf 157 | 158 | 159 | **Words of advice:** 160 | 161 | - Why are there so few resources on "Transformer" for beginners? *Because it's rather new =)* 162 | - If you want to see the influence of "Transformer" architectures in NLP, check out the papers from NAACL 2018, ACL 2018, EMNLP 2018 from https://aclanthology.coli.uni-saarland.de/ 163 | 164 | ---- 165 | 166 | 167 | ### 3.4: Natural Language Processing (*for real*) 168 | 169 | **You might ask**, "Hey, I saw this ['throw away your RNN' blogpost](https://towardsdatascience.com/the-fall-of-rnn-lstm-2d1594c74ce0). Does that mean whatever above is not relevant?" 170 | 171 | **Answer:** Natural Language Processing evolves really fast and as a field that mostly adopts/applies the "hottest" trend from mainstream machine/deep learning, every ~2-3 years the underlying state-of-art architecture for NLP changes (quite drastically). 172 | 173 | The only way to keep up is not to chase the trends. Know that the lastest/hottest algorithm or pre-trained models exists, understand how they work and find ways to incorporate them into whichever project/method you're using/researching on. 174 | 175 | There's really no point in chasing any method (in NLP or any other sub-fields of computing) because 176 | 177 | - if you're in the industry, what usually happens is that you try every possible method until you find that fits your needs 178 | - if you're in academia, chasing state-of-art is going to drain you out and make your work mediorce/common, focus on the story and why things work more than how to make model X work on task Y. 179 | 180 | In 2018, here's some recent trends: https://arxiv.org/pdf/1708.02709.pdf 181 | 182 | Best way to keep up with NLP is to look at proceedings from these conferences (there's a lot but here's a few curated ones): 183 | - Association of Computational Lingusitics (ACL) and related conferences: https://aclanthology.coli.uni-saarland.de/ 184 | - Text Analysis Conference (TAC): http://tac.nist.gov/publications/index.html 185 | - *Due to a lapse in government funding, this and almost all NIST-affiliated websites will be unavailable until further notice.* -_-||| 186 | - Interspeech proceedings on https://www.isca-speech.org/iscaweb/index.php/online-archive 187 | - Text, Speech and Dialogue conferences: https://link.springer.com/conference/tsd 188 | - SIGIR conferences: http://sigir.org/conferences/sponsored-conferences/ 189 | - AAAI conferences: http://www.aaai.org/Conferences/conferences.php 190 | - WWW conferences: http://www.iw3c2.org/ 191 | 192 | 193 | As an NLP researcher/scientist, I encourage you to read and re-read these slides from Prof. Min-Yen Kan every once in a while http://coling2018.org/wp-content/uploads/2018/08/180824-researchFastAndSlow-1.pdf 194 | 195 | 196 | -------------------------------------------------------------------------------- /LearnItYourself/Beginners/Chapter5.md: -------------------------------------------------------------------------------- 1 | # Overview 2 | 3 | - **Chapter 5: Nuts and Bolts in Machine Learning** 4 | - 5.1: *Bias and Variance* 5 | - 5.2: *Regularlization* 6 | - Why is my Neural Network not working? 7 | - Weights Regularization and Dropout 8 | - 5.3: *Knowledge Distilation* 9 | 10 | **Note:** 11 | 12 | This chapter doesn't have the "for real" section because every point in here seems to be the for real part when we train models with realistic datasets. We always asks ourselves these questions: 13 | 14 | - Why isn't our models working? 15 | - Even if our models are working, how to improve it? 16 | - Why doesn't the same model work for another similar dataset? 17 | 18 | 19 | The answer to the above are usually (i) check bias-variance then (ii) Regularize? Get more data? Bigger models? 20 | 21 | Sometimes we also ask these questions: 22 | 23 | - Why is the model so slow? 24 | - How to make the model faster during inference (i.e. when using it after training is finished)? 25 | - How to make training faster? 26 | 27 | The answer to the above are usually (i) profile your code, (ii) make model smaller / distill model (iii) choose another model, (iv) create something totally new. 28 | 29 | ---- 30 | 31 | 32 | ### 5.1: Bias and Variance 33 | 34 | - Andrew Ng's ML Course https://www.coursera.org/lecture/deep-neural-network/bias-variance-ZhclI 35 | - see also the NIPS 2016 tutorial https://www.youtube.com/watch?v=wjqaz6m42wU (slides: https://media.nips.cc/Conferences/2016/Slides/6203-Slides.pdf) 36 | - see also https://www.youtube.com/watch?v=F1ka6a13S9I 37 | - and if you're a fan, here's Andrew Ng on the same topic a longer time ago https://www.courses.com/stanford-university/machine-learning/9 38 | - Emily Fox's ML Course https://www.coursera.org/lecture/ml-regression/variance-and-the-bias-variance-tradeoff-ZvP40 39 | - Yaser Abu-Mostafa's Caltech Course https://www.youtube.com/watch?v=zrEyxfl2-a8 (a little dense but if you come from the math/stats path, you might like it) 40 | - Sebastian Thrun explaining causually when in a self-driving car https://www.youtube.com/watch?v=W5uUYnSHDhM 41 | - https://www.ics.uci.edu/~smyth/courses/cs274/readings/xing_singh_CMU_bias_variance.pdf (hardcore math but good) 42 | - [A Modern Take on the Bias-Variance Tradeoff in NN](https://arxiv.org/pdf/1810.08591.pdf) 43 | - https://www.learnopencv.com/bias-variance-tradeoff-in-machine-learning/ 44 | - http://www.r2d3.us/visual-intro-to-machine-learning-part-2/ 45 | - https://towardsdatascience.com/understanding-the-bias-variance-tradeoff-165e6942b229 46 | - In terse math: http://www.inf.ed.ac.uk/teaching/courses/mlsc/Notes/Lecture4/BiasVariance.pdf 47 | - https://kevinzakka.github.io/2016/09/26/applying-deep-learning/ 48 | 49 | ---- 50 | 51 | 52 | ### 5.2: Regularization 53 | 54 | - Why is my Neural Network not working? 55 | - https://blog.slavv.com/37-reasons-why-your-neural-network-is-not-working-4020854bd607 56 | - https://machinelearningmastery.com/introduction-to-regularization-to-reduce-overfitting-and-improve-generalization-error/ 57 | - https://keras.rstudio.com/articles/tutorial_overfit_underfit.html 58 | - Be Nice to your Neurons https://www.cl.cam.ac.uk/~pv273/slides/UCLS2.pdf 59 | 60 | - Weights Regularization, Activity Regularization and Dropout 61 | - https://stackoverflow.com/questions/50649831/understanding-regularization-in-keras 62 | - https://machinelearningmastery.com/weight-regularization-to-reduce-overfitting-of-deep-learning-models/ 63 | - https://www.fast.ai/2018/07/02/adam-weight-decay/ 64 | - Practical Keras code snippets for regularization https://www.ics.uci.edu/~mohamadt/keras_DL.pdf 65 | - Weights vs Acvity Regularization: https://github.com/keras-team/keras/issues/1618 66 | - https://robotwealth.com/deep-learning-trading-fighting-overfitting-dropout-regularization/ 67 | - https://machinelearningmastery.com/dropout-regularization-deep-learning-models-keras/ 68 | - Dropout in 3 lines of Python https://iamtrask.github.io/2015/07/28/dropout/ 69 | - The original Dropout paper: https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf 70 | - Keras author on weights regularization vs dropout https://twitter.com/fchollet/status/763085560206340096 71 | 72 | ---- 73 | 74 | ### 5.3: Knowledge Distillation 75 | 76 | Maybe not so beginner but it's good to know distillation to make your models smaller. 77 | 78 | - The paper: https://arxiv.org/abs/1503.02531 79 | - https://github.com/Ujjwal-9/Knowledge-Distillation 80 | - https://jacobgil.github.io/deeplearning/pruning-deep-learning 81 | - https://www.oreilly.com/ideas/compressing-and-regularizing-deep-neural-networks 82 | - https://github.com/kmsravindra/ML-AI-experiments/blob/master/AI/knowledge_distillation/Knowledge%20distillation.ipynb 83 | - https://medium.com/neural-machines/knowledge-distillation-dc241d7c2322 84 | - https://github.com/dkozlov/awesome-knowledge-distillation 85 | - https://github.com/NervanaSystems/distiller/ 86 | 87 | -------------------------------------------------------------------------------- /LearnItYourself/Beginners/README.md: -------------------------------------------------------------------------------- 1 | 2 | # Overview 3 | 4 | - [**Chapter 1: Introduction to Deep Learning**](https://github.com/datasciencesg/workshops/tree/master/LearnItYourself/Beginners/Chapter1.md) 5 | - 1.1: *Linear Models and Gradient Descent* 6 | - Binary Classification 7 | - Logistic Regression 8 | - Gradient Descent from Scratch 9 | - 1.2: *Machine Learning Frameworks* 10 | - Deep Learning 11 | - Machine Learning 12 | - Probablistic Learning 13 | - 1.3: *Deep Learning (for real)* 14 | 15 | - [**Chapter 2: Computer Vision and Convolutional Neural Net**](https://github.com/datasciencesg/workshops/tree/master/LearnItYourself/Beginners/Chapter2.md) 16 | - 2.1: *Introduction to Computer Vision* 17 | - 2.2: *Convolutional Neural Net* 18 | - Blogposts that presents CNN 19 | - Show me the Code 20 | - Show me the Math 21 | - 2.3 *Visualizing CNN* 22 | - 2.4: *Convolutional Neural Net (for real)* 23 | 24 | 25 | - [**Chapter 3: Natural Language Processing, Recurrent Neural Nets and Self-Attention Nets**](https://github.com/datasciencesg/workshops/tree/master/LearnItYourself/Beginners/Chapter3.md) 26 | - 3.1: *Introduction to Natural Language Processing* 27 | - The Get Things Done Way 28 | - The Slow and Steady Way 29 | - 3.2 *Recurrent Neural Nets* 30 | - Blogposts that presents RNN 31 | - Show me the Code 32 | - Show me the Math 33 | - 3.3 *Self-Attention Nets* (aka Transformer) 34 | - Blogposts that presents Transformer 35 | - Show me the Code 36 | - Show me the Math 37 | - 3.4: *Natural Language Processing (for real)* 38 | 39 | 40 | - **Chapter 4: Generative Adversial Nets and Style Transfer** 41 | - (In Progress, eta 03 Mar 2019) 42 | 43 | 44 | - [**Chapter 5: Nuts and Bolts in Machine Learning**](https://github.com/datasciencesg/workshops/tree/master/LearnItYourself/Beginners/Chapter5.md) 45 | - 5.1: *Bias and Variance* 46 | - 5.2: *Regularlization* 47 | - Why is my Neural Network not working? 48 | - Weights Regularization and Dropout 49 | - 5.3: *Knowledge Distilation* 50 | 51 | -------------------------------------------------------------------------------- /LearnItYourself/Beginners/__future__/Chapter6: -------------------------------------------------------------------------------- 1 | 2 | Intermediate... 3 | 4 | 5 | - [**Chapter 6: Natural Language Applications**] 6 | - (In Progress, eta Dec 2019) 7 | - 6.1: *Classic NLP* 8 | - 6.2: *Linguistics Annotations* 9 | - 6.3: *Knowledge/Information Extraction and Retrieval* 10 | - 6.4: *Natural Language Understanding* 11 | - 6.5: *Machine Translation* 12 | - 6.6: *Generation* 13 | - 6.7: *Chatbots* 14 | -------------------------------------------------------------------------------- /LearnItYourself/Beginners/__future__/ChapterX.md: -------------------------------------------------------------------------------- 1 | # Overview 2 | 3 | - **Chapter X: Natural Language Processing Tasks** 4 | 5 | - 1.1: *Tokenization, Segmentation, POS Tagging* 6 | - Tokenization / Segmentation 7 | - POS Tagging 8 | 9 | - 1.2: *Chunking/Parsing* 10 | - Chinking and Chunking 11 | - Syntactic Parsing 12 | - Semantic Parsing 13 | 14 | - 1.3: *Multi-Word Expressions, Keyphrase Extraction, Terminology* 15 | - Multi-Word Expression (MWE) 16 | - http://www.coli.uni-saarland.de/courses/mwe16/page.php?id=schedule 17 | - Keyphrase Extraction 18 | - Terminology Extraction 19 | 20 | 21 | - 1.4 *Speech Processing* 22 | - Audio Signal Feature Extraction 23 | - Automatic Speech Recognition 24 | - Text to Speech 25 | - Speech to Speech Systems 26 | 27 | 28 | - 1.5: *Chatbots and Dialog Systems* 29 | 30 | 31 | 32 | - 1.6: *Machine Translation* 33 | 34 | 35 | 36 | - 1.7 *Sentiment Tasks* 37 | - Resources 38 | 39 | 40 | - 1.8 *Semantic Tasks* 41 | - Information Extraction 42 | - Semantic Role Labeling 43 | - Co-reference Resolution, Entity Linking and Knowledge Base Population 44 | 45 | -------------------------------------------------------------------------------- /LearnItYourself/Beginners/__future__/ChapterY.md: -------------------------------------------------------------------------------- 1 | # Overview 2 | 3 | - **Chapter Y: Computational Linguistics** 4 | - 1.1: *Syntax* 5 | - Dynamic Programming Parsers 6 | - Earley Algorithm 7 | - Chart Parsing 8 | - Cock-Kasami Younger (CKY) Algorithm 9 | - Shift-Reduce Parsing 10 | - Context Free Grammar (CFG) and Probabilistic Context Free Grammar (PCFG) 11 | - Dependency Parsing 12 | - Prague Dependency 13 | - Universal Dependency 14 | - Grammar Formalisms 15 | - Combinatory Categorical Grammar (CCG) 16 | - Head-Driver Phrase Structured Grammar (HPSG) 17 | - Lexical Functional Grammar (LFG) 18 | - Tree Adjoining Grammar (TAG) 19 | - Treebanks 20 | - Penn Treebank 21 | - Universal Dependencies Treebank 22 | - CCG Bank 23 | 24 | - 1.2: *Semantics* 25 | - Formal Semantics and Logics 26 | - Predicate Logic 27 | - Type Theory 28 | - Lambda Calculus 29 | - Generalized Quantifiers 30 | - Event Semantics 31 | - Dynamic Semantics & Discourse Representation Theory 32 | - Presuppositions 33 | - Distributed Situation-state Spaces 34 | 35 | - Semantic Treebanks 36 | - Groningen Meaning Bank: http://gmb.let.rug.nl/ 37 | - Abstract Meaning Representation: https://amr.isi.edu/ 38 | - Deep Bank: http://moin.delph-in.net/DeepBank 39 | 40 | - Vector Semantics 41 | - Semantic Parsing 42 | 43 | - 1.3: *Phonology* 44 | 45 | - 1.4 *Discourse* 46 | - Reading List from Uni Saarland http://www.coli.uni-saarland.de/courses/discourse-parsing-17/page.php?id=papers 47 | 48 | - 1.5: *Regular Expressions* 49 | 50 | - 1.6: *Task Independent NLP* 51 | 52 | - 1.7: *Bayesian NLP* 53 | - Reading List from Uni Saarland: http://www.coli.uni-saarland.de/courses/bayesian-NLP-17/page.php?id=papers 54 | 55 | 56 | - 1.8: Computational Psycholinguistics 57 | - http://www.coli.uni-saarland.de/courses/language-aging/ 58 | -------------------------------------------------------------------------------- /LearnItYourself/Beginners/__future__/ChapterZ.md: -------------------------------------------------------------------------------- 1 | # Overview 2 | 3 | - **Chapter Z: Deep/Reinforcement Learning for Financial Data** 4 | 5 | 6 | http://www.wildml.com/2018/02/introduction-to-learning-to-trade-with-reinforcement-learning/ 7 | https://www.datacamp.com/community/tutorials/lstm-python-stock-market 8 | https://lilianweng.github.io/lil-log/2017/07/08/predict-stock-prices-using-RNN-part-1.html 9 | -------------------------------------------------------------------------------- /LearnItYourself/Readme.md: -------------------------------------------------------------------------------- 1 | What is this list of materials? 2 | ==== 3 | 4 | There are many bootcamps and coding courses on data science and machine/deep learning. This list contains links to FREE (at moment of addition) resources for specific topics that often comes up in these courses/bootcamps. 5 | 6 | Why create this list of materials? 7 | ==== 8 | 9 | We at DataScience SG (DSSG) believes everyone has their preferred learning methods. In the Data Science (DS), Machine Learning (ML) field or if you choose to Artificial Intelligence (AI), most often the knowledge, from basic to cutting edge, can be learnt from technical conferences, university materials, open source documentations/forums and more. These resources are online, freely available and can be found by the passionate. DSSG put up these materials to help individuals understand what's needed for DataScience and Artificial Intelligence. They can use these free knowledge gained to choose suitable workshops / learning paths for themselves. There are great paid workshops, we hope these list can help you identify them. 10 | 11 | Technology, esp. machine/deep learning moves really fast. By our observations, many practitioners are "online-educated". In fact,we can boldly say that most practitioners following Andrew Ng, Jeremy Howard, Yoshua Bengio, Ian Goodfellow, Geoffrey Hinton and the likes are MOOC/online-educated. 12 | 13 | Hopefully, this list would serve the DSSG community as a gauge on whether it's enough for you to simply go through the materials linked to topics or decides to SPEND on bootcamps and coding courses. We understand that some of them are free, but they will definitely require you to take time away from family and friends, so its best to select carefully. 14 | 15 | Many times it's not the learning materials or code that's important but the journey of learning and self-exploration that's important, through understanding and implementing the concepts. For some, a simple lead from this list is sufficient to spark a journey of continuous self-learning =) 16 | 17 | 18 | How to use this list of materials? 19 | ==== 20 | 21 | Simply navigate to https://github.com/datasciencesg/workshops/tree/master/LearnItYourself/Beginners 22 | 23 | **Note:** We only have the beginner topics ready for now. But do also note that while some courses/bootcamps might think that some topics are advance, we might have them in the beginner topics. 24 | -------------------------------------------------------------------------------- /Python/Data Manipulation and Viz with Dplyr and Ggplot/README.md: -------------------------------------------------------------------------------- 1 | # Data Manipulation and Viz with Dplyr and Ggplot 2 | - Designed by: @fauzibajuri 3 | - Refer to dplyr_participants.ipynb and ggplot2_participants.ipynb to get started 4 | -------------------------------------------------------------------------------- /Python/Data Manipulation and Viz with Dplyr and Ggplot/dplyr.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "metadata": {}, 5 | "cell_type": "markdown", 6 | "source": "# Data Transformation with dplyr in R\nBy: Fauzi Bajuri for DataScience SG Youth Wing" 7 | }, 8 | { 9 | "metadata": { 10 | "collapsed": true 11 | }, 12 | "cell_type": "markdown", 13 | "source": "## Why use dplyr?\n\n- Intuitive to write and easy to read, especially when using \"chaining\" syntax" 14 | }, 15 | { 16 | "metadata": {}, 17 | "cell_type": "markdown", 18 | "source": "### Useful shortcuts and tips for using Jupyter Notebook\nhttps://medium.com/ibm-data-science-experience/markdown-for-jupyter-notebooks-cheatsheet-386c05aeebed\n\n### Resources:\n\n- R for Data Science http://r4ds.had.co.nz/transform.html\n- Hands-on video tutorial https://www.youtube.com/watch?v=jWjqLW-u3hc and http://rpubs.com/justmarkham/dplyr-tutorial" 19 | }, 20 | { 21 | "metadata": {}, 22 | "cell_type": "markdown", 23 | "source": "## About dplyr\n\n- Five basic verbs : filter, select, arrange, mutate, summarise (plus 'group_by')\n- Joins (not convered)\n- dplyr will mask a few base functions\n- previous package is plyr\n- dplyr approach is simpler to write and read\n\n- **Command structure (for all dplyr verbs)**:\n - first argument is a data frame\n - return value is a data frame\n - nothing is modified in place" 24 | }, 25 | { 26 | "metadata": {}, 27 | "cell_type": "markdown", 28 | "source": "## Comparison with Microsoft Excel\n- This training will make comparisons with functions & features in Microsoft Excel to help partcipants understand how the verbs work!\n- Particpants recommended to open file.csv in Microsoft Excel during the session!" 29 | }, 30 | { 31 | "metadata": { 32 | "trusted": true 33 | }, 34 | "cell_type": "code", 35 | "source": "#install & load packages\n#install.packages(\"dplyr\")\nlibrary(dplyr)", 36 | "execution_count": null, 37 | "outputs": [] 38 | }, 39 | { 40 | "metadata": { 41 | "trusted": true 42 | }, 43 | "cell_type": "code", 44 | "source": "flights <- read.csv(\"file.csv\")\nflights <- tbl_df(flights)", 45 | "execution_count": null, 46 | "outputs": [] 47 | }, 48 | { 49 | "metadata": { 50 | "scrolled": true, 51 | "trusted": true 52 | }, 53 | "cell_type": "code", 54 | "source": "str(flights)", 55 | "execution_count": null, 56 | "outputs": [] 57 | }, 58 | { 59 | "metadata": {}, 60 | "cell_type": "markdown", 61 | "source": "## About Dataset: New York City Flights 13\n\nThis data contains information on all arriving and departing flights from NYC in 2013. The variables in this dataset are:\n\n- **year, month, day** - Date of departure\n- **dep_time,arr_time** - Actual departure and arrival times.\n- **sched_dep_time, sched_arr_time** - Scheduled departure and arrival times.\n- **dep_delay, arr_delay** - delays in minutes\n- **hour, minute** - Time of scheduled departure\n- **carrier** - carrier abbreviation (See: https://www.census.gov/foreign-trade/reference/codes/aircarrier/acname.txt)\n- **tailnum** - Tail number of plane.\n- **flight** - flight number.\n- **origin, dest** - Origin and Destination\n- **air_time** - Time spent in air.\n- **distance** - Distance flown.\n- **time_hour** - scheduled date and hour of flight.\n\nSource: http://statseducation.com/Introduction-to-R/modules/graphics/ggplot2/" 62 | }, 63 | { 64 | "metadata": {}, 65 | "cell_type": "markdown", 66 | "source": "## Verb 1 - filter() using Relational Operators (>, >=, <, <=, !=, ==, &, |)\nFilter similar to Microsoft Excel" 67 | }, 68 | { 69 | "metadata": { 70 | "trusted": true 71 | }, 72 | "cell_type": "code", 73 | "source": "#R base example\n\n#head(flights[flights$month == 11 | flights$month== 12,]) # not modified in place\n\n##dplyr method easier\n\n#OR (|) Operator\n\n#filter to view data in Nov and Dec\n\nfilter(flights, month == 11 | month == 12) #not modified in place\n\n#head(filter(flights, month == 11 | 12)) #wrong!", 74 | "execution_count": null, 75 | "outputs": [] 76 | }, 77 | { 78 | "metadata": { 79 | "trusted": true 80 | }, 81 | "cell_type": "code", 82 | "source": "#filter for string/non-numeric data type\n#filter for both months nov and dec for carriers AA and UA\nfilter(flights, month == 11 | month == 12, carrier == \"AA\" | carrier == \"UA\") %>% summarize(n = n())\n\n#use nrow() or str() to check no. of observations/rows after filter is done\n#can use piping with summarize to compute no. of rows( %>% summarize(n = n()))\n", 83 | "execution_count": null, 84 | "outputs": [] 85 | }, 86 | { 87 | "metadata": { 88 | "trusted": true 89 | }, 90 | "cell_type": "code", 91 | "source": "#AND conditions (&)\n#filter to view data on 1st of November\n\nfilter(flights, month == 11 & day == 1) %>% summarize(n = n())\n\n", 92 | "execution_count": null, 93 | "outputs": [] 94 | }, 95 | { 96 | "metadata": {}, 97 | "cell_type": "markdown", 98 | "source": "## Exercise 1 - Filter" 99 | }, 100 | { 101 | "metadata": { 102 | "trusted": true 103 | }, 104 | "cell_type": "code", 105 | "source": "#1) Filter dataframe for flights on 1st March which departed earlier than scheduled from John F. Kennedy International Airport (176)\n\nfilter(flights, month == 3, day == 1, dep_delay < 0, origin == \"JFK\") %>% summarize(no._obs= n())\n\n#2) Filter dataframe for flights on September by both United Airlines and American Airlines which was scheduled to arrive at Los Angeles International Airport between 12 noon and 6PM(288)\n\nfilter(flights, month == 9, carrier == \"UA\" | carrier == \"AA\", dest == \"LAX\", sched_arr_time >= 1200, sched_arr_time <= 1800) %>% summarize(no._obs= n())", 106 | "execution_count": 1, 107 | "outputs": [ 108 | { 109 | "output_type": "error", 110 | "ename": "ERROR", 111 | "evalue": "Error in filter(flights, month == 3, day == 1, dep_delay < 0, origin == : could not find function \"%>%\"\n", 112 | "traceback": [ 113 | "Error in filter(flights, month == 3, day == 1, dep_delay < 0, origin == : could not find function \"%>%\"\nTraceback:\n" 114 | ] 115 | } 116 | ] 117 | }, 118 | { 119 | "metadata": {}, 120 | "cell_type": "markdown", 121 | "source": "## Verb 2 - arrange()\nArrange is similar to sorting a table in Microsoft Excel" 122 | }, 123 | { 124 | "metadata": { 125 | "trusted": true 126 | }, 127 | "cell_type": "code", 128 | "source": "head(arrange(flights, dep_delay)) #by default, sort by assending order (smallest to largest value)", 129 | "execution_count": null, 130 | "outputs": [] 131 | }, 132 | { 133 | "metadata": { 134 | "scrolled": true, 135 | "trusted": true 136 | }, 137 | "cell_type": "code", 138 | "source": "head(arrange(flights, desc(dep_delay))) #set desc as nested functiont to sort by descending order (largest to smallest value)", 139 | "execution_count": null, 140 | "outputs": [] 141 | }, 142 | { 143 | "metadata": {}, 144 | "cell_type": "markdown", 145 | "source": "## Exercise 2 - Arrange" 146 | }, 147 | { 148 | "metadata": { 149 | "scrolled": true, 150 | "trusted": true 151 | }, 152 | "cell_type": "code", 153 | "source": "#Arrange by arr time and then month (latest months first)\n\nhead(arrange(flights, arr_time, desc(month)))", 154 | "execution_count": null, 155 | "outputs": [] 156 | }, 157 | { 158 | "metadata": { 159 | "scrolled": true, 160 | "trusted": true 161 | }, 162 | "cell_type": "code", 163 | "source": "#sort by descending order for month and day, and ascending order for dep_delay)\n\nhead(arrange(flights, desc(month), desc(day), dep_delay))", 164 | "execution_count": null, 165 | "outputs": [] 166 | }, 167 | { 168 | "metadata": {}, 169 | "cell_type": "markdown", 170 | "source": "## Verb 3 - select()\nSelect is similar to SELECT in SQL and deleting columns in Microsoft Excel. select() allows you to rapidly zoom in on a useful subset using operations based on the names of the variables." 171 | }, 172 | { 173 | "metadata": { 174 | "trusted": true 175 | }, 176 | "cell_type": "code", 177 | "source": "select(flights, year, month, day) %>% head(.,2)", 178 | "execution_count": null, 179 | "outputs": [] 180 | }, 181 | { 182 | "metadata": { 183 | "trusted": true 184 | }, 185 | "cell_type": "code", 186 | "source": "select(flights, year:day) %>% head(.,2) # use : to select columns from:to", 187 | "execution_count": null, 188 | "outputs": [] 189 | }, 190 | { 191 | "metadata": { 192 | "scrolled": true, 193 | "trusted": true 194 | }, 195 | "cell_type": "code", 196 | "source": "select(flights, -(year:day)) %>% head(.,2) #use - to select all except the column names provided in argument", 197 | "execution_count": null, 198 | "outputs": [] 199 | }, 200 | { 201 | "metadata": {}, 202 | "cell_type": "markdown", 203 | "source": "#### Good to know!\n\nThere are a number of helper functions you can use within select():\n\n- **starts_with(\"abc\")**: matches names that begin with “abc”.\n\n- **ends_with(\"xyz\")**: matches names that end with “xyz”.\n\n- **contains(\"ijk\")**: matches names that contain “ijk”.\n\n- **matches(\"(.)\\\\1\")**: selects variables that match a regular expression. This one matches any variables that contain repeated characters.\n\n- **num_range(\"x\", 1:3)**: matches x1, x2 and x3." 204 | }, 205 | { 206 | "metadata": { 207 | "scrolled": true, 208 | "trusted": true 209 | }, 210 | "cell_type": "code", 211 | "source": "select(flights, starts_with(\"dep\")) %>% head(.,2)", 212 | "execution_count": null, 213 | "outputs": [] 214 | }, 215 | { 216 | "metadata": { 217 | "trusted": true 218 | }, 219 | "cell_type": "code", 220 | "source": "select(flights, ends_with(\"time\")) %>% head(.,2)", 221 | "execution_count": null, 222 | "outputs": [] 223 | }, 224 | { 225 | "metadata": {}, 226 | "cell_type": "markdown", 227 | "source": "## Verb 4 - mutate()\nAdd new columns that are functions of existing columns. Functions include +, -, *, /, ^, %/% (integer division) and %% (remainder)" 228 | }, 229 | { 230 | "metadata": { 231 | "trusted": true 232 | }, 233 | "cell_type": "code", 234 | "source": "mutate(flights,\n gain = dep_delay - arr_delay,\n speed = distance / air_time * 60) #%>% head(.,2)\n#distance = speed * time!", 235 | "execution_count": null, 236 | "outputs": [] 237 | }, 238 | { 239 | "metadata": { 240 | "trusted": true 241 | }, 242 | "cell_type": "code", 243 | "source": "#If you only want to keep the new variables, use transmute():\n\ntransmute(flights,\n gain = dep_delay - arr_delay,\n hours = air_time / 60,\n gain_per_hour = gain / hours\n) %>% head(.,2)", 244 | "execution_count": null, 245 | "outputs": [] 246 | }, 247 | { 248 | "metadata": {}, 249 | "cell_type": "markdown", 250 | "source": "## Verb 5 - summarise() with group_by\nThe last key verb is summarise(). It collapses a data frame to a single row" 251 | }, 252 | { 253 | "metadata": { 254 | "trusted": true 255 | }, 256 | "cell_type": "code", 257 | "source": "summarise(flights, delay = mean(dep_delay, na.rm = TRUE)) #computes mean for dep_delay for entire dataframe (remove NA)", 258 | "execution_count": null, 259 | "outputs": [] 260 | }, 261 | { 262 | "metadata": {}, 263 | "cell_type": "markdown", 264 | "source": "Together group_by() and summarise() provide one of the tools that you’ll use most commonly when working with dplyr: grouped summaries. But before we go any further with this, we need to introduce a powerful new idea: the pipe (%>%).\n\nA good way to pronounce %>% when reading code is “then”." 265 | }, 266 | { 267 | "metadata": { 268 | "trusted": true 269 | }, 270 | "cell_type": "code", 271 | "source": "flights %>%\n group_by(carrier) %>% # set how you want to group the dataframe by\n summarize (count = n())#n() is used to count no. of rows/observations", 272 | "execution_count": null, 273 | "outputs": [] 274 | }, 275 | { 276 | "metadata": {}, 277 | "cell_type": "markdown", 278 | "source": "How to read the above:\n\n\"Transform the *flights* dataframe by (1) grouping them by carrier and *then* summarizing by count (no. of carriers)\" \n\n**Note how this is similar to =COUNTIF in Microsoft Excel and also using Pivot Table**\n\nWe can add on more transformations by using %>% " 279 | }, 280 | { 281 | "metadata": { 282 | "scrolled": true, 283 | "trusted": true 284 | }, 285 | "cell_type": "code", 286 | "source": "flights %>%\n group_by(carrier) %>% # set how you want to group the dataframe by\n summarize (count = n(), #n() is used to count no. of rows/observations \n mean_dist = mean(distance, na.rm = TRUE), #compute mean distance per carrier\n median_arr_delay = median(arr_delay, na.rm = TRUE)\n ) %>%\n filter(count >= 1000) %>% #filter to show only carriers with flights >= 1000 \n arrange(median_arr_delay) #arrange/sort in ascending order based on median_arr_delay", 287 | "execution_count": null, 288 | "outputs": [] 289 | }, 290 | { 291 | "metadata": {}, 292 | "cell_type": "markdown", 293 | "source": "How to read the above:\n\n\"Transform the flights dataframe by...\n- **(1)** **grouping them** by carrier and **then**\n- **(2)** **summarizing** by count (no. of carriers), mean distance, median arrival delay and **then**\n- **(3)** **filter** only for carriers with >= 1000 flights and **then** \n- **(4)** **arrange** the dataset in ascending order based on median arrival delay time" 294 | }, 295 | { 296 | "metadata": {}, 297 | "cell_type": "markdown", 298 | "source": "## Exercise 3 - Wrap up dplyr\n\nCreate a dataframe to show the (1) mean distance in km (1 mile = 1.60934 km) and (2) no. of flights for each pair of Origin & Destination (OD) (3) except for HNL (i.e. I do not want HNL data as either origin or destination in my dataframe).\n\nFollowing which, I want to view them to be sorted in (4) alphabetical order based on origin first then by no. of flights (in descending order).\n\n" 299 | }, 300 | { 301 | "metadata": { 302 | "scrolled": true, 303 | "trusted": true 304 | }, 305 | "cell_type": "code", 306 | "source": "#1 mile = 1.60934 km\n\nflights %>%\n group_by(origin, dest) %>%\n summarize(count = n(),\n mean_dist_km = round(mean(distance, na.rm = TRUE)*1.60934)\n ) %>%\n arrange(origin, desc(count)) %>%\n filter(dest != \"HNL\", origin !=\"HNL\")", 307 | "execution_count": null, 308 | "outputs": [] 309 | } 310 | ], 311 | "metadata": { 312 | "kernelspec": { 313 | "name": "r", 314 | "display_name": "R", 315 | "language": "R" 316 | }, 317 | "language_info": { 318 | "mimetype": "text/x-r-source", 319 | "name": "R", 320 | "pygments_lexer": "r", 321 | "version": "3.4.1", 322 | "file_extension": ".r", 323 | "codemirror_mode": "r" 324 | } 325 | }, 326 | "nbformat": 4, 327 | "nbformat_minor": 2 328 | } -------------------------------------------------------------------------------- /Python/Data Manipulation and Viz with Dplyr and Ggplot/train.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 2 | 1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S 3 | 2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C 4 | 3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S 5 | 4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S 6 | 5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S 7 | 6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q 8 | 7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S 9 | 8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S 10 | 9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S 11 | 10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C 12 | 11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S 13 | 12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S 14 | 13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S 15 | 14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S 16 | 15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S 17 | 16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S 18 | 17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q 19 | 18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S 20 | 19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S 21 | 20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C 22 | 21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S 23 | 22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S 24 | 23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q 25 | 24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S 26 | 25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S 27 | 26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S 28 | 27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C 29 | 28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S 30 | 29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q 31 | 30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S 32 | 31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C 33 | 32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C 34 | 33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q 35 | 34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S 36 | 35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C 37 | 36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S 38 | 37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C 39 | 38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S 40 | 39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S 41 | 40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C 42 | 41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S 43 | 42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S 44 | 43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C 45 | 44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C 46 | 45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q 47 | 46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S 48 | 47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q 49 | 48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q 50 | 49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C 51 | 50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S 52 | 51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S 53 | 52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S 54 | 53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C 55 | 54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S 56 | 55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C 57 | 56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S 58 | 57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S 59 | 58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C 60 | 59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S 61 | 60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S 62 | 61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C 63 | 62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28, 64 | 63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S 65 | 64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S 66 | 65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C 67 | 66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C 68 | 67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S 69 | 68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S 70 | 69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S 71 | 70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S 72 | 71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S 73 | 72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S 74 | 73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S 75 | 74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C 76 | 75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S 77 | 76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S 78 | 77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S 79 | 78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S 80 | 79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S 81 | 80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S 82 | 81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S 83 | 82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S 84 | 83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q 85 | 84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S 86 | 85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S 87 | 86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S 88 | 87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S 89 | 88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S 90 | 89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S 91 | 90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S 92 | 91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S 93 | 92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S 94 | 93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S 95 | 94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S 96 | 95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S 97 | 96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S 98 | 97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C 99 | 98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C 100 | 99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S 101 | 100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S 102 | 101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S 103 | 102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S 104 | 103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S 105 | 104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S 106 | 105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S 107 | 106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S 108 | 107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S 109 | 108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S 110 | 109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S 111 | 110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q 112 | 111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S 113 | 112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C 114 | 113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S 115 | 114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S 116 | 115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C 117 | 116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S 118 | 117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q 119 | 118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S 120 | 119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C 121 | 120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S 122 | 121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S 123 | 122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S 124 | 123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C 125 | 124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S 126 | 125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S 127 | 126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C 128 | 127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q 129 | 128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S 130 | 129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C 131 | 130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S 132 | 131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C 133 | 132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S 134 | 133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S 135 | 134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S 136 | 135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S 137 | 136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C 138 | 137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S 139 | 138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S 140 | 139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S 141 | 140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C 142 | 141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C 143 | 142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S 144 | 143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S 145 | 144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q 146 | 145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S 147 | 146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S 148 | 147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S 149 | 148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S 150 | 149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S 151 | 150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S 152 | 151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S 153 | 152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S 154 | 153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S 155 | 154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S 156 | 155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S 157 | 156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C 158 | 157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q 159 | 158,0,3,"Corn, Mr. Harry",male,30,0,0,SOTON/OQ 392090,8.05,,S 160 | 159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S 161 | 160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S 162 | 161,0,3,"Cribb, Mr. John Hatfield",male,44,0,1,371362,16.1,,S 163 | 162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40,0,0,C.A. 33595,15.75,,S 164 | 163,0,3,"Bengtsson, Mr. John Viktor",male,26,0,0,347068,7.775,,S 165 | 164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S 166 | 165,0,3,"Panula, Master. Eino Viljami",male,1,4,1,3101295,39.6875,,S 167 | 166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9,0,2,363291,20.525,,S 168 | 167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55,E33,S 169 | 168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45,1,4,347088,27.9,,S 170 | 169,0,1,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S 171 | 170,0,3,"Ling, Mr. Lee",male,28,0,0,1601,56.4958,,S 172 | 171,0,1,"Van der hoef, Mr. Wyckoff",male,61,0,0,111240,33.5,B19,S 173 | 172,0,3,"Rice, Master. Arthur",male,4,4,1,382652,29.125,,Q 174 | 173,1,3,"Johnson, Miss. Eleanor Ileen",female,1,1,1,347742,11.1333,,S 175 | 174,0,3,"Sivola, Mr. Antti Wilhelm",male,21,0,0,STON/O 2. 3101280,7.925,,S 176 | 175,0,1,"Smith, Mr. James Clinch",male,56,0,0,17764,30.6958,A7,C 177 | 176,0,3,"Klasen, Mr. Klas Albin",male,18,1,1,350404,7.8542,,S 178 | 177,0,3,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S 179 | 178,0,1,"Isham, Miss. Ann Elizabeth",female,50,0,0,PC 17595,28.7125,C49,C 180 | 179,0,2,"Hale, Mr. Reginald",male,30,0,0,250653,13,,S 181 | 180,0,3,"Leonard, Mr. Lionel",male,36,0,0,LINE,0,,S 182 | 181,0,3,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S 183 | 182,0,2,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C 184 | 183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9,4,2,347077,31.3875,,S 185 | 184,1,2,"Becker, Master. Richard F",male,1,2,1,230136,39,F4,S 186 | 185,1,3,"Kink-Heilmann, Miss. Luise Gretchen",female,4,0,2,315153,22.025,,S 187 | 186,0,1,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50,A32,S 188 | 187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,1,0,370365,15.5,,Q 189 | 188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45,0,0,111428,26.55,,S 190 | 189,0,3,"Bourke, Mr. John",male,40,1,1,364849,15.5,,Q 191 | 190,0,3,"Turcin, Mr. Stjepan",male,36,0,0,349247,7.8958,,S 192 | 191,1,2,"Pinsky, Mrs. (Rosa)",female,32,0,0,234604,13,,S 193 | 192,0,2,"Carbines, Mr. William",male,19,0,0,28424,13,,S 194 | 193,1,3,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19,1,0,350046,7.8542,,S 195 | 194,1,2,"Navratil, Master. Michel M",male,3,1,1,230080,26,F2,S 196 | 195,1,1,"Brown, Mrs. James Joseph (Margaret Tobin)",female,44,0,0,PC 17610,27.7208,B4,C 197 | 196,1,1,"Lurette, Miss. Elise",female,58,0,0,PC 17569,146.5208,B80,C 198 | 197,0,3,"Mernagh, Mr. Robert",male,,0,0,368703,7.75,,Q 199 | 198,0,3,"Olsen, Mr. Karl Siegwart Andreas",male,42,0,1,4579,8.4042,,S 200 | 199,1,3,"Madigan, Miss. Margaret ""Maggie""",female,,0,0,370370,7.75,,Q 201 | 200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24,0,0,248747,13,,S 202 | 201,0,3,"Vande Walle, Mr. Nestor Cyriel",male,28,0,0,345770,9.5,,S 203 | 202,0,3,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S 204 | 203,0,3,"Johanson, Mr. Jakob Alfred",male,34,0,0,3101264,6.4958,,S 205 | 204,0,3,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C 206 | 205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18,0,0,A/5 3540,8.05,,S 207 | 206,0,3,"Strom, Miss. Telma Matilda",female,2,0,1,347054,10.4625,G6,S 208 | 207,0,3,"Backstrom, Mr. Karl Alfred",male,32,1,0,3101278,15.85,,S 209 | 208,1,3,"Albimona, Mr. Nassef Cassem",male,26,0,0,2699,18.7875,,C 210 | 209,1,3,"Carr, Miss. Helen ""Ellen""",female,16,0,0,367231,7.75,,Q 211 | 210,1,1,"Blank, Mr. Henry",male,40,0,0,112277,31,A31,C 212 | 211,0,3,"Ali, Mr. Ahmed",male,24,0,0,SOTON/O.Q. 3101311,7.05,,S 213 | 212,1,2,"Cameron, Miss. Clear Annie",female,35,0,0,F.C.C. 13528,21,,S 214 | 213,0,3,"Perkin, Mr. John Henry",male,22,0,0,A/5 21174,7.25,,S 215 | 214,0,2,"Givard, Mr. Hans Kristensen",male,30,0,0,250646,13,,S 216 | 215,0,3,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q 217 | 216,1,1,"Newell, Miss. Madeleine",female,31,1,0,35273,113.275,D36,C 218 | 217,1,3,"Honkanen, Miss. Eliina",female,27,0,0,STON/O2. 3101283,7.925,,S 219 | 218,0,2,"Jacobsohn, Mr. Sidney Samuel",male,42,1,0,243847,27,,S 220 | 219,1,1,"Bazzani, Miss. Albina",female,32,0,0,11813,76.2917,D15,C 221 | 220,0,2,"Harris, Mr. Walter",male,30,0,0,W/C 14208,10.5,,S 222 | 221,1,3,"Sunderland, Mr. Victor Francis",male,16,0,0,SOTON/OQ 392089,8.05,,S 223 | 222,0,2,"Bracken, Mr. James H",male,27,0,0,220367,13,,S 224 | 223,0,3,"Green, Mr. George Henry",male,51,0,0,21440,8.05,,S 225 | 224,0,3,"Nenkoff, Mr. Christo",male,,0,0,349234,7.8958,,S 226 | 225,1,1,"Hoyt, Mr. Frederick Maxfield",male,38,1,0,19943,90,C93,S 227 | 226,0,3,"Berglund, Mr. Karl Ivar Sven",male,22,0,0,PP 4348,9.35,,S 228 | 227,1,2,"Mellors, Mr. William John",male,19,0,0,SW/PP 751,10.5,,S 229 | 228,0,3,"Lovell, Mr. John Hall (""Henry"")",male,20.5,0,0,A/5 21173,7.25,,S 230 | 229,0,2,"Fahlstrom, Mr. Arne Jonas",male,18,0,0,236171,13,,S 231 | 230,0,3,"Lefebre, Miss. Mathilde",female,,3,1,4133,25.4667,,S 232 | 231,1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35,1,0,36973,83.475,C83,S 233 | 232,0,3,"Larsson, Mr. Bengt Edvin",male,29,0,0,347067,7.775,,S 234 | 233,0,2,"Sjostedt, Mr. Ernst Adolf",male,59,0,0,237442,13.5,,S 235 | 234,1,3,"Asplund, Miss. Lillian Gertrud",female,5,4,2,347077,31.3875,,S 236 | 235,0,2,"Leyson, Mr. Robert William Norman",male,24,0,0,C.A. 29566,10.5,,S 237 | 236,0,3,"Harknett, Miss. Alice Phoebe",female,,0,0,W./C. 6609,7.55,,S 238 | 237,0,2,"Hold, Mr. Stephen",male,44,1,0,26707,26,,S 239 | 238,1,2,"Collyer, Miss. Marjorie ""Lottie""",female,8,0,2,C.A. 31921,26.25,,S 240 | 239,0,2,"Pengelly, Mr. Frederick William",male,19,0,0,28665,10.5,,S 241 | 240,0,2,"Hunt, Mr. George Henry",male,33,0,0,SCO/W 1585,12.275,,S 242 | 241,0,3,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C 243 | 242,1,3,"Murphy, Miss. Katherine ""Kate""",female,,1,0,367230,15.5,,Q 244 | 243,0,2,"Coleridge, Mr. Reginald Charles",male,29,0,0,W./C. 14263,10.5,,S 245 | 244,0,3,"Maenpaa, Mr. Matti Alexanteri",male,22,0,0,STON/O 2. 3101275,7.125,,S 246 | 245,0,3,"Attalah, Mr. Sleiman",male,30,0,0,2694,7.225,,C 247 | 246,0,1,"Minahan, Dr. William Edward",male,44,2,0,19928,90,C78,Q 248 | 247,0,3,"Lindahl, Miss. Agda Thorilda Viktoria",female,25,0,0,347071,7.775,,S 249 | 248,1,2,"Hamalainen, Mrs. William (Anna)",female,24,0,2,250649,14.5,,S 250 | 249,1,1,"Beckwith, Mr. Richard Leonard",male,37,1,1,11751,52.5542,D35,S 251 | 250,0,2,"Carter, Rev. Ernest Courtenay",male,54,1,0,244252,26,,S 252 | 251,0,3,"Reed, Mr. James George",male,,0,0,362316,7.25,,S 253 | 252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29,1,1,347054,10.4625,G6,S 254 | 253,0,1,"Stead, Mr. William Thomas",male,62,0,0,113514,26.55,C87,S 255 | 254,0,3,"Lobb, Mr. William Arthur",male,30,1,0,A/5. 3336,16.1,,S 256 | 255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41,0,2,370129,20.2125,,S 257 | 256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29,0,2,2650,15.2458,,C 258 | 257,1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C 259 | 258,1,1,"Cherry, Miss. Gladys",female,30,0,0,110152,86.5,B77,S 260 | 259,1,1,"Ward, Miss. Anna",female,35,0,0,PC 17755,512.3292,,C 261 | 260,1,2,"Parrish, Mrs. (Lutie Davis)",female,50,0,1,230433,26,,S 262 | 261,0,3,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q 263 | 262,1,3,"Asplund, Master. Edvin Rojj Felix",male,3,4,2,347077,31.3875,,S 264 | 263,0,1,"Taussig, Mr. Emil",male,52,1,1,110413,79.65,E67,S 265 | 264,0,1,"Harrison, Mr. William",male,40,0,0,112059,0,B94,S 266 | 265,0,3,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q 267 | 266,0,2,"Reeves, Mr. David",male,36,0,0,C.A. 17248,10.5,,S 268 | 267,0,3,"Panula, Mr. Ernesti Arvid",male,16,4,1,3101295,39.6875,,S 269 | 268,1,3,"Persson, Mr. Ernst Ulrik",male,25,1,0,347083,7.775,,S 270 | 269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58,0,1,PC 17582,153.4625,C125,S 271 | 270,1,1,"Bissette, Miss. Amelia",female,35,0,0,PC 17760,135.6333,C99,S 272 | 271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31,,S 273 | 272,1,3,"Tornquist, Mr. William Henry",male,25,0,0,LINE,0,,S 274 | 273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41,0,1,250644,19.5,,S 275 | 274,0,1,"Natsch, Mr. Charles H",male,37,0,1,PC 17596,29.7,C118,C 276 | 275,1,3,"Healy, Miss. Hanora ""Nora""",female,,0,0,370375,7.75,,Q 277 | 276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S 278 | 277,0,3,"Lindblom, Miss. Augusta Charlotta",female,45,0,0,347073,7.75,,S 279 | 278,0,2,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0,,S 280 | 279,0,3,"Rice, Master. Eric",male,7,4,1,382652,29.125,,Q 281 | 280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35,1,1,C.A. 2673,20.25,,S 282 | 281,0,3,"Duane, Mr. Frank",male,65,0,0,336439,7.75,,Q 283 | 282,0,3,"Olsson, Mr. Nils Johan Goransson",male,28,0,0,347464,7.8542,,S 284 | 283,0,3,"de Pelsmaeker, Mr. Alfons",male,16,0,0,345778,9.5,,S 285 | 284,1,3,"Dorking, Mr. Edward Arthur",male,19,0,0,A/5. 10482,8.05,,S 286 | 285,0,1,"Smith, Mr. Richard William",male,,0,0,113056,26,A19,S 287 | 286,0,3,"Stankovic, Mr. Ivan",male,33,0,0,349239,8.6625,,C 288 | 287,1,3,"de Mulder, Mr. Theodore",male,30,0,0,345774,9.5,,S 289 | 288,0,3,"Naidenoff, Mr. Penko",male,22,0,0,349206,7.8958,,S 290 | 289,1,2,"Hosono, Mr. Masabumi",male,42,0,0,237798,13,,S 291 | 290,1,3,"Connolly, Miss. Kate",female,22,0,0,370373,7.75,,Q 292 | 291,1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.85,,S 293 | 292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19,1,0,11967,91.0792,B49,C 294 | 293,0,2,"Levy, Mr. Rene Jacques",male,36,0,0,SC/Paris 2163,12.875,D,C 295 | 294,0,3,"Haas, Miss. Aloisia",female,24,0,0,349236,8.85,,S 296 | 295,0,3,"Mineff, Mr. Ivan",male,24,0,0,349233,7.8958,,S 297 | 296,0,1,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C 298 | 297,0,3,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C 299 | 298,0,1,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.55,C22 C26,S 300 | 299,1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S 301 | 300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C 302 | 301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q 303 | 302,1,3,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q 304 | 303,0,3,"Johnson, Mr. William Cahoone Jr",male,19,0,0,LINE,0,,S 305 | 304,1,2,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q 306 | 305,0,3,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S 307 | 306,1,1,"Allison, Master. Hudson Trevor",male,0.92,1,2,113781,151.55,C22 C26,S 308 | 307,1,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C 309 | 308,1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17,1,0,PC 17758,108.9,C65,C 310 | 309,0,2,"Abelson, Mr. Samuel",male,30,1,0,P/PP 3381,24,,C 311 | 310,1,1,"Francatelli, Miss. Laura Mabel",female,30,0,0,PC 17485,56.9292,E36,C 312 | 311,1,1,"Hays, Miss. Margaret Bechstein",female,24,0,0,11767,83.1583,C54,C 313 | 312,1,1,"Ryerson, Miss. Emily Borie",female,18,2,2,PC 17608,262.375,B57 B59 B63 B66,C 314 | 313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",female,26,1,1,250651,26,,S 315 | 314,0,3,"Hendekovic, Mr. Ignjac",male,28,0,0,349243,7.8958,,S 316 | 315,0,2,"Hart, Mr. Benjamin",male,43,1,1,F.C.C. 13529,26.25,,S 317 | 316,1,3,"Nilsson, Miss. Helmina Josefina",female,26,0,0,347470,7.8542,,S 318 | 317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24,1,0,244367,26,,S 319 | 318,0,2,"Moraweck, Dr. Ernest",male,54,0,0,29011,14,,S 320 | 319,1,1,"Wick, Miss. Mary Natalie",female,31,0,2,36928,164.8667,C7,S 321 | 320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40,1,1,16966,134.5,E34,C 322 | 321,0,3,"Dennis, Mr. Samuel",male,22,0,0,A/5 21172,7.25,,S 323 | 322,0,3,"Danoff, Mr. Yoto",male,27,0,0,349219,7.8958,,S 324 | 323,1,2,"Slayter, Miss. Hilda Mary",female,30,0,0,234818,12.35,,Q 325 | 324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22,1,1,248738,29,,S 326 | 325,0,3,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S 327 | 326,1,1,"Young, Miss. Marie Grice",female,36,0,0,PC 17760,135.6333,C32,C 328 | 327,0,3,"Nysveen, Mr. Johan Hansen",male,61,0,0,345364,6.2375,,S 329 | 328,1,2,"Ball, Mrs. (Ada E Hall)",female,36,0,0,28551,13,D,S 330 | 329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31,1,1,363291,20.525,,S 331 | 330,1,1,"Hippach, Miss. Jean Gertrude",female,16,0,1,111361,57.9792,B18,C 332 | 331,1,3,"McCoy, Miss. Agnes",female,,2,0,367226,23.25,,Q 333 | 332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S 334 | 333,0,1,"Graham, Mr. George Edward",male,38,0,1,PC 17582,153.4625,C91,S 335 | 334,0,3,"Vander Planke, Mr. Leo Edmondus",male,16,2,0,345764,18,,S 336 | 335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S 337 | 336,0,3,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S 338 | 337,0,1,"Pears, Mr. Thomas Clinton",male,29,1,0,113776,66.6,C2,S 339 | 338,1,1,"Burns, Miss. Elizabeth Margaret",female,41,0,0,16966,134.5,E40,C 340 | 339,1,3,"Dahl, Mr. Karl Edwart",male,45,0,0,7598,8.05,,S 341 | 340,0,1,"Blackwell, Mr. Stephen Weart",male,45,0,0,113784,35.5,T,S 342 | 341,1,2,"Navratil, Master. Edmond Roger",male,2,1,1,230080,26,F2,S 343 | 342,1,1,"Fortune, Miss. Alice Elizabeth",female,24,3,2,19950,263,C23 C25 C27,S 344 | 343,0,2,"Collander, Mr. Erik Gustaf",male,28,0,0,248740,13,,S 345 | 344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25,0,0,244361,13,,S 346 | 345,0,2,"Fox, Mr. Stanley Hubert",male,36,0,0,229236,13,,S 347 | 346,1,2,"Brown, Miss. Amelia ""Mildred""",female,24,0,0,248733,13,F33,S 348 | 347,1,2,"Smith, Miss. Marion Elsie",female,40,0,0,31418,13,,S 349 | 348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S 350 | 349,1,3,"Coutts, Master. William Loch ""William""",male,3,1,1,C.A. 37671,15.9,,S 351 | 350,0,3,"Dimic, Mr. Jovan",male,42,0,0,315088,8.6625,,S 352 | 351,0,3,"Odahl, Mr. Nils Martin",male,23,0,0,7267,9.225,,S 353 | 352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35,C128,S 354 | 353,0,3,"Elias, Mr. Tannous",male,15,1,1,2695,7.2292,,C 355 | 354,0,3,"Arnold-Franchi, Mr. Josef",male,25,1,0,349237,17.8,,S 356 | 355,0,3,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C 357 | 356,0,3,"Vanden Steen, Mr. Leo Peter",male,28,0,0,345783,9.5,,S 358 | 357,1,1,"Bowerman, Miss. Elsie Edith",female,22,0,1,113505,55,E33,S 359 | 358,0,2,"Funk, Miss. Annie Clemmer",female,38,0,0,237671,13,,S 360 | 359,1,3,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q 361 | 360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q 362 | 361,0,3,"Skoog, Mr. Wilhelm",male,40,1,4,347088,27.9,,S 363 | 362,0,2,"del Carlo, Mr. Sebastiano",male,29,1,0,SC/PARIS 2167,27.7208,,C 364 | 363,0,3,"Barbara, Mrs. (Catherine David)",female,45,0,1,2691,14.4542,,C 365 | 364,0,3,"Asim, Mr. Adola",male,35,0,0,SOTON/O.Q. 3101310,7.05,,S 366 | 365,0,3,"O'Brien, Mr. Thomas",male,,1,0,370365,15.5,,Q 367 | 366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30,0,0,C 7076,7.25,,S 368 | 367,1,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",female,60,1,0,110813,75.25,D37,C 369 | 368,1,3,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C 370 | 369,1,3,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q 371 | 370,1,1,"Aubart, Mme. Leontine Pauline",female,24,0,0,PC 17477,69.3,B35,C 372 | 371,1,1,"Harder, Mr. George Achilles",male,25,1,0,11765,55.4417,E50,C 373 | 372,0,3,"Wiklund, Mr. Jakob Alfred",male,18,1,0,3101267,6.4958,,S 374 | 373,0,3,"Beavan, Mr. William Thomas",male,19,0,0,323951,8.05,,S 375 | 374,0,1,"Ringhini, Mr. Sante",male,22,0,0,PC 17760,135.6333,,C 376 | 375,0,3,"Palsson, Miss. Stina Viola",female,3,3,1,349909,21.075,,S 377 | 376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C 378 | 377,1,3,"Landergren, Miss. Aurora Adelia",female,22,0,0,C 7077,7.25,,S 379 | 378,0,1,"Widener, Mr. Harry Elkins",male,27,0,2,113503,211.5,C82,C 380 | 379,0,3,"Betros, Mr. Tannous",male,20,0,0,2648,4.0125,,C 381 | 380,0,3,"Gustafsson, Mr. Karl Gideon",male,19,0,0,347069,7.775,,S 382 | 381,1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.525,,C 383 | 382,1,3,"Nakid, Miss. Maria (""Mary"")",female,1,0,2,2653,15.7417,,C 384 | 383,0,3,"Tikkanen, Mr. Juho",male,32,0,0,STON/O 2. 3101293,7.925,,S 385 | 384,1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35,1,0,113789,52,,S 386 | 385,0,3,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S 387 | 386,0,2,"Davies, Mr. Charles Henry",male,18,0,0,S.O.C. 14879,73.5,,S 388 | 387,0,3,"Goodwin, Master. Sidney Leonard",male,1,5,2,CA 2144,46.9,,S 389 | 388,1,2,"Buss, Miss. Kate",female,36,0,0,27849,13,,S 390 | 389,0,3,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q 391 | 390,1,2,"Lehmann, Miss. Bertha",female,17,0,0,SC 1748,12,,C 392 | 391,1,1,"Carter, Mr. William Ernest",male,36,1,2,113760,120,B96 B98,S 393 | 392,1,3,"Jansson, Mr. Carl Olof",male,21,0,0,350034,7.7958,,S 394 | 393,0,3,"Gustafsson, Mr. Johan Birger",male,28,2,0,3101277,7.925,,S 395 | 394,1,1,"Newell, Miss. Marjorie",female,23,1,0,35273,113.275,D36,C 396 | 395,1,3,"Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)",female,24,0,2,PP 9549,16.7,G6,S 397 | 396,0,3,"Johansson, Mr. Erik",male,22,0,0,350052,7.7958,,S 398 | 397,0,3,"Olsson, Miss. Elina",female,31,0,0,350407,7.8542,,S 399 | 398,0,2,"McKane, Mr. Peter David",male,46,0,0,28403,26,,S 400 | 399,0,2,"Pain, Dr. Alfred",male,23,0,0,244278,10.5,,S 401 | 400,1,2,"Trout, Mrs. William H (Jessie L)",female,28,0,0,240929,12.65,,S 402 | 401,1,3,"Niskanen, Mr. Juha",male,39,0,0,STON/O 2. 3101289,7.925,,S 403 | 402,0,3,"Adams, Mr. John",male,26,0,0,341826,8.05,,S 404 | 403,0,3,"Jussila, Miss. Mari Aina",female,21,1,0,4137,9.825,,S 405 | 404,0,3,"Hakkarainen, Mr. Pekka Pietari",male,28,1,0,STON/O2. 3101279,15.85,,S 406 | 405,0,3,"Oreskovic, Miss. Marija",female,20,0,0,315096,8.6625,,S 407 | 406,0,2,"Gale, Mr. Shadrach",male,34,1,0,28664,21,,S 408 | 407,0,3,"Widegren, Mr. Carl/Charles Peter",male,51,0,0,347064,7.75,,S 409 | 408,1,2,"Richards, Master. William Rowe",male,3,1,1,29106,18.75,,S 410 | 409,0,3,"Birkeland, Mr. Hans Martin Monsen",male,21,0,0,312992,7.775,,S 411 | 410,0,3,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S 412 | 411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S 413 | 412,0,3,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q 414 | 413,1,1,"Minahan, Miss. Daisy E",female,33,1,0,19928,90,C78,Q 415 | 414,0,2,"Cunningham, Mr. Alfred Fleming",male,,0,0,239853,0,,S 416 | 415,1,3,"Sundman, Mr. Johan Julian",male,44,0,0,STON/O 2. 3101269,7.925,,S 417 | 416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S 418 | 417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34,1,1,28220,32.5,,S 419 | 418,1,2,"Silven, Miss. Lyyli Karoliina",female,18,0,2,250652,13,,S 420 | 419,0,2,"Matthews, Mr. William John",male,30,0,0,28228,13,,S 421 | 420,0,3,"Van Impe, Miss. Catharina",female,10,0,2,345773,24.15,,S 422 | 421,0,3,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C 423 | 422,0,3,"Charters, Mr. David",male,21,0,0,A/5. 13032,7.7333,,Q 424 | 423,0,3,"Zimmerman, Mr. Leo",male,29,0,0,315082,7.875,,S 425 | 424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28,1,1,347080,14.4,,S 426 | 425,0,3,"Rosblom, Mr. Viktor Richard",male,18,1,1,370129,20.2125,,S 427 | 426,0,3,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S 428 | 427,1,2,"Clarke, Mrs. Charles V (Ada Maria Winfield)",female,28,1,0,2003,26,,S 429 | 428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19,0,0,250655,26,,S 430 | 429,0,3,"Flynn, Mr. James",male,,0,0,364851,7.75,,Q 431 | 430,1,3,"Pickard, Mr. Berk (Berk Trembisky)",male,32,0,0,SOTON/O.Q. 392078,8.05,E10,S 432 | 431,1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan",male,28,0,0,110564,26.55,C52,S 433 | 432,1,3,"Thorneycroft, Mrs. Percival (Florence Kate White)",female,,1,0,376564,16.1,,S 434 | 433,1,2,"Louch, Mrs. Charles Alexander (Alice Adelaide Slow)",female,42,1,0,SC/AH 3085,26,,S 435 | 434,0,3,"Kallio, Mr. Nikolai Erland",male,17,0,0,STON/O 2. 3101274,7.125,,S 436 | 435,0,1,"Silvey, Mr. William Baird",male,50,1,0,13507,55.9,E44,S 437 | 436,1,1,"Carter, Miss. Lucile Polk",female,14,1,2,113760,120,B96 B98,S 438 | 437,0,3,"Ford, Miss. Doolina Margaret ""Daisy""",female,21,2,2,W./C. 6608,34.375,,S 439 | 438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",female,24,2,3,29106,18.75,,S 440 | 439,0,1,"Fortune, Mr. Mark",male,64,1,4,19950,263,C23 C25 C27,S 441 | 440,0,2,"Kvillner, Mr. Johan Henrik Johannesson",male,31,0,0,C.A. 18723,10.5,,S 442 | 441,1,2,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",female,45,1,1,F.C.C. 13529,26.25,,S 443 | 442,0,3,"Hampe, Mr. Leon",male,20,0,0,345769,9.5,,S 444 | 443,0,3,"Petterson, Mr. Johan Emil",male,25,1,0,347076,7.775,,S 445 | 444,1,2,"Reynaldo, Ms. Encarnacion",female,28,0,0,230434,13,,S 446 | 445,1,3,"Johannesen-Bratthammer, Mr. Bernt",male,,0,0,65306,8.1125,,S 447 | 446,1,1,"Dodge, Master. Washington",male,4,0,2,33638,81.8583,A34,S 448 | 447,1,2,"Mellinger, Miss. Madeleine Violet",female,13,0,1,250644,19.5,,S 449 | 448,1,1,"Seward, Mr. Frederic Kimber",male,34,0,0,113794,26.55,,S 450 | 449,1,3,"Baclini, Miss. Marie Catherine",female,5,2,1,2666,19.2583,,C 451 | 450,1,1,"Peuchen, Major. Arthur Godfrey",male,52,0,0,113786,30.5,C104,S 452 | 451,0,2,"West, Mr. Edwy Arthur",male,36,1,2,C.A. 34651,27.75,,S 453 | 452,0,3,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S 454 | 453,0,1,"Foreman, Mr. Benjamin Laventall",male,30,0,0,113051,27.75,C111,C 455 | 454,1,1,"Goldenberg, Mr. Samuel L",male,49,1,0,17453,89.1042,C92,C 456 | 455,0,3,"Peduzzi, Mr. Joseph",male,,0,0,A/5 2817,8.05,,S 457 | 456,1,3,"Jalsevac, Mr. Ivan",male,29,0,0,349240,7.8958,,C 458 | 457,0,1,"Millet, Mr. Francis Davis",male,65,0,0,13509,26.55,E38,S 459 | 458,1,1,"Kenyon, Mrs. Frederick R (Marion)",female,,1,0,17464,51.8625,D21,S 460 | 459,1,2,"Toomey, Miss. Ellen",female,50,0,0,F.C.C. 13531,10.5,,S 461 | 460,0,3,"O'Connor, Mr. Maurice",male,,0,0,371060,7.75,,Q 462 | 461,1,1,"Anderson, Mr. Harry",male,48,0,0,19952,26.55,E12,S 463 | 462,0,3,"Morley, Mr. William",male,34,0,0,364506,8.05,,S 464 | 463,0,1,"Gee, Mr. Arthur H",male,47,0,0,111320,38.5,E63,S 465 | 464,0,2,"Milling, Mr. Jacob Christian",male,48,0,0,234360,13,,S 466 | 465,0,3,"Maisner, Mr. Simon",male,,0,0,A/S 2816,8.05,,S 467 | 466,0,3,"Goncalves, Mr. Manuel Estanslas",male,38,0,0,SOTON/O.Q. 3101306,7.05,,S 468 | 467,0,2,"Campbell, Mr. William",male,,0,0,239853,0,,S 469 | 468,0,1,"Smart, Mr. John Montgomery",male,56,0,0,113792,26.55,,S 470 | 469,0,3,"Scanlan, Mr. James",male,,0,0,36209,7.725,,Q 471 | 470,1,3,"Baclini, Miss. Helene Barbara",female,0.75,2,1,2666,19.2583,,C 472 | 471,0,3,"Keefe, Mr. Arthur",male,,0,0,323592,7.25,,S 473 | 472,0,3,"Cacic, Mr. Luka",male,38,0,0,315089,8.6625,,S 474 | 473,1,2,"West, Mrs. Edwy Arthur (Ada Mary Worth)",female,33,1,2,C.A. 34651,27.75,,S 475 | 474,1,2,"Jerwan, Mrs. Amin S (Marie Marthe Thuillard)",female,23,0,0,SC/AH Basle 541,13.7917,D,C 476 | 475,0,3,"Strandberg, Miss. Ida Sofia",female,22,0,0,7553,9.8375,,S 477 | 476,0,1,"Clifford, Mr. George Quincy",male,,0,0,110465,52,A14,S 478 | 477,0,2,"Renouf, Mr. Peter Henry",male,34,1,0,31027,21,,S 479 | 478,0,3,"Braund, Mr. Lewis Richard",male,29,1,0,3460,7.0458,,S 480 | 479,0,3,"Karlsson, Mr. Nils August",male,22,0,0,350060,7.5208,,S 481 | 480,1,3,"Hirvonen, Miss. Hildur E",female,2,0,1,3101298,12.2875,,S 482 | 481,0,3,"Goodwin, Master. Harold Victor",male,9,5,2,CA 2144,46.9,,S 483 | 482,0,2,"Frost, Mr. Anthony Wood ""Archie""",male,,0,0,239854,0,,S 484 | 483,0,3,"Rouse, Mr. Richard Henry",male,50,0,0,A/5 3594,8.05,,S 485 | 484,1,3,"Turkula, Mrs. (Hedwig)",female,63,0,0,4134,9.5875,,S 486 | 485,1,1,"Bishop, Mr. Dickinson H",male,25,1,0,11967,91.0792,B49,C 487 | 486,0,3,"Lefebre, Miss. Jeannie",female,,3,1,4133,25.4667,,S 488 | 487,1,1,"Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)",female,35,1,0,19943,90,C93,S 489 | 488,0,1,"Kent, Mr. Edward Austin",male,58,0,0,11771,29.7,B37,C 490 | 489,0,3,"Somerton, Mr. Francis William",male,30,0,0,A.5. 18509,8.05,,S 491 | 490,1,3,"Coutts, Master. Eden Leslie ""Neville""",male,9,1,1,C.A. 37671,15.9,,S 492 | 491,0,3,"Hagland, Mr. Konrad Mathias Reiersen",male,,1,0,65304,19.9667,,S 493 | 492,0,3,"Windelov, Mr. Einar",male,21,0,0,SOTON/OQ 3101317,7.25,,S 494 | 493,0,1,"Molson, Mr. Harry Markland",male,55,0,0,113787,30.5,C30,S 495 | 494,0,1,"Artagaveytia, Mr. Ramon",male,71,0,0,PC 17609,49.5042,,C 496 | 495,0,3,"Stanley, Mr. Edward Roland",male,21,0,0,A/4 45380,8.05,,S 497 | 496,0,3,"Yousseff, Mr. Gerious",male,,0,0,2627,14.4583,,C 498 | 497,1,1,"Eustis, Miss. Elizabeth Mussey",female,54,1,0,36947,78.2667,D20,C 499 | 498,0,3,"Shellard, Mr. Frederick William",male,,0,0,C.A. 6212,15.1,,S 500 | 499,0,1,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25,1,2,113781,151.55,C22 C26,S 501 | 500,0,3,"Svensson, Mr. Olof",male,24,0,0,350035,7.7958,,S 502 | 501,0,3,"Calic, Mr. Petar",male,17,0,0,315086,8.6625,,S 503 | 502,0,3,"Canavan, Miss. Mary",female,21,0,0,364846,7.75,,Q 504 | 503,0,3,"O'Sullivan, Miss. Bridget Mary",female,,0,0,330909,7.6292,,Q 505 | 504,0,3,"Laitinen, Miss. Kristina Sofia",female,37,0,0,4135,9.5875,,S 506 | 505,1,1,"Maioni, Miss. Roberta",female,16,0,0,110152,86.5,B79,S 507 | 506,0,1,"Penasco y Castellana, Mr. Victor de Satode",male,18,1,0,PC 17758,108.9,C65,C 508 | 507,1,2,"Quick, Mrs. Frederick Charles (Jane Richards)",female,33,0,2,26360,26,,S 509 | 508,1,1,"Bradley, Mr. George (""George Arthur Brayton"")",male,,0,0,111427,26.55,,S 510 | 509,0,3,"Olsen, Mr. Henry Margido",male,28,0,0,C 4001,22.525,,S 511 | 510,1,3,"Lang, Mr. Fang",male,26,0,0,1601,56.4958,,S 512 | 511,1,3,"Daly, Mr. Eugene Patrick",male,29,0,0,382651,7.75,,Q 513 | 512,0,3,"Webber, Mr. James",male,,0,0,SOTON/OQ 3101316,8.05,,S 514 | 513,1,1,"McGough, Mr. James Robert",male,36,0,0,PC 17473,26.2875,E25,S 515 | 514,1,1,"Rothschild, Mrs. Martin (Elizabeth L. Barrett)",female,54,1,0,PC 17603,59.4,,C 516 | 515,0,3,"Coleff, Mr. Satio",male,24,0,0,349209,7.4958,,S 517 | 516,0,1,"Walker, Mr. William Anderson",male,47,0,0,36967,34.0208,D46,S 518 | 517,1,2,"Lemore, Mrs. (Amelia Milley)",female,34,0,0,C.A. 34260,10.5,F33,S 519 | 518,0,3,"Ryan, Mr. Patrick",male,,0,0,371110,24.15,,Q 520 | 519,1,2,"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)",female,36,1,0,226875,26,,S 521 | 520,0,3,"Pavlovic, Mr. Stefo",male,32,0,0,349242,7.8958,,S 522 | 521,1,1,"Perreault, Miss. Anne",female,30,0,0,12749,93.5,B73,S 523 | 522,0,3,"Vovk, Mr. Janko",male,22,0,0,349252,7.8958,,S 524 | 523,0,3,"Lahoud, Mr. Sarkis",male,,0,0,2624,7.225,,C 525 | 524,1,1,"Hippach, Mrs. Louis Albert (Ida Sophia Fischer)",female,44,0,1,111361,57.9792,B18,C 526 | 525,0,3,"Kassem, Mr. Fared",male,,0,0,2700,7.2292,,C 527 | 526,0,3,"Farrell, Mr. James",male,40.5,0,0,367232,7.75,,Q 528 | 527,1,2,"Ridsdale, Miss. Lucy",female,50,0,0,W./C. 14258,10.5,,S 529 | 528,0,1,"Farthing, Mr. John",male,,0,0,PC 17483,221.7792,C95,S 530 | 529,0,3,"Salonen, Mr. Johan Werner",male,39,0,0,3101296,7.925,,S 531 | 530,0,2,"Hocking, Mr. Richard George",male,23,2,1,29104,11.5,,S 532 | 531,1,2,"Quick, Miss. Phyllis May",female,2,1,1,26360,26,,S 533 | 532,0,3,"Toufik, Mr. Nakli",male,,0,0,2641,7.2292,,C 534 | 533,0,3,"Elias, Mr. Joseph Jr",male,17,1,1,2690,7.2292,,C 535 | 534,1,3,"Peter, Mrs. Catherine (Catherine Rizk)",female,,0,2,2668,22.3583,,C 536 | 535,0,3,"Cacic, Miss. Marija",female,30,0,0,315084,8.6625,,S 537 | 536,1,2,"Hart, Miss. Eva Miriam",female,7,0,2,F.C.C. 13529,26.25,,S 538 | 537,0,1,"Butt, Major. Archibald Willingham",male,45,0,0,113050,26.55,B38,S 539 | 538,1,1,"LeRoy, Miss. Bertha",female,30,0,0,PC 17761,106.425,,C 540 | 539,0,3,"Risien, Mr. Samuel Beard",male,,0,0,364498,14.5,,S 541 | 540,1,1,"Frolicher, Miss. Hedwig Margaritha",female,22,0,2,13568,49.5,B39,C 542 | 541,1,1,"Crosby, Miss. Harriet R",female,36,0,2,WE/P 5735,71,B22,S 543 | 542,0,3,"Andersson, Miss. Ingeborg Constanzia",female,9,4,2,347082,31.275,,S 544 | 543,0,3,"Andersson, Miss. Sigrid Elisabeth",female,11,4,2,347082,31.275,,S 545 | 544,1,2,"Beane, Mr. Edward",male,32,1,0,2908,26,,S 546 | 545,0,1,"Douglas, Mr. Walter Donald",male,50,1,0,PC 17761,106.425,C86,C 547 | 546,0,1,"Nicholson, Mr. Arthur Ernest",male,64,0,0,693,26,,S 548 | 547,1,2,"Beane, Mrs. Edward (Ethel Clarke)",female,19,1,0,2908,26,,S 549 | 548,1,2,"Padro y Manent, Mr. Julian",male,,0,0,SC/PARIS 2146,13.8625,,C 550 | 549,0,3,"Goldsmith, Mr. Frank John",male,33,1,1,363291,20.525,,S 551 | 550,1,2,"Davies, Master. John Morgan Jr",male,8,1,1,C.A. 33112,36.75,,S 552 | 551,1,1,"Thayer, Mr. John Borland Jr",male,17,0,2,17421,110.8833,C70,C 553 | 552,0,2,"Sharp, Mr. Percival James R",male,27,0,0,244358,26,,S 554 | 553,0,3,"O'Brien, Mr. Timothy",male,,0,0,330979,7.8292,,Q 555 | 554,1,3,"Leeni, Mr. Fahim (""Philip Zenni"")",male,22,0,0,2620,7.225,,C 556 | 555,1,3,"Ohman, Miss. Velin",female,22,0,0,347085,7.775,,S 557 | 556,0,1,"Wright, Mr. George",male,62,0,0,113807,26.55,,S 558 | 557,1,1,"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")",female,48,1,0,11755,39.6,A16,C 559 | 558,0,1,"Robbins, Mr. Victor",male,,0,0,PC 17757,227.525,,C 560 | 559,1,1,"Taussig, Mrs. Emil (Tillie Mandelbaum)",female,39,1,1,110413,79.65,E67,S 561 | 560,1,3,"de Messemaeker, Mrs. Guillaume Joseph (Emma)",female,36,1,0,345572,17.4,,S 562 | 561,0,3,"Morrow, Mr. Thomas Rowan",male,,0,0,372622,7.75,,Q 563 | 562,0,3,"Sivic, Mr. Husein",male,40,0,0,349251,7.8958,,S 564 | 563,0,2,"Norman, Mr. Robert Douglas",male,28,0,0,218629,13.5,,S 565 | 564,0,3,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S 566 | 565,0,3,"Meanwell, Miss. (Marion Ogden)",female,,0,0,SOTON/O.Q. 392087,8.05,,S 567 | 566,0,3,"Davies, Mr. Alfred J",male,24,2,0,A/4 48871,24.15,,S 568 | 567,0,3,"Stoytcheff, Mr. Ilia",male,19,0,0,349205,7.8958,,S 569 | 568,0,3,"Palsson, Mrs. Nils (Alma Cornelia Berglund)",female,29,0,4,349909,21.075,,S 570 | 569,0,3,"Doharr, Mr. Tannous",male,,0,0,2686,7.2292,,C 571 | 570,1,3,"Jonsson, Mr. Carl",male,32,0,0,350417,7.8542,,S 572 | 571,1,2,"Harris, Mr. George",male,62,0,0,S.W./PP 752,10.5,,S 573 | 572,1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",female,53,2,0,11769,51.4792,C101,S 574 | 573,1,1,"Flynn, Mr. John Irwin (""Irving"")",male,36,0,0,PC 17474,26.3875,E25,S 575 | 574,1,3,"Kelly, Miss. Mary",female,,0,0,14312,7.75,,Q 576 | 575,0,3,"Rush, Mr. Alfred George John",male,16,0,0,A/4. 20589,8.05,,S 577 | 576,0,3,"Patchett, Mr. George",male,19,0,0,358585,14.5,,S 578 | 577,1,2,"Garside, Miss. Ethel",female,34,0,0,243880,13,,S 579 | 578,1,1,"Silvey, Mrs. William Baird (Alice Munger)",female,39,1,0,13507,55.9,E44,S 580 | 579,0,3,"Caram, Mrs. Joseph (Maria Elias)",female,,1,0,2689,14.4583,,C 581 | 580,1,3,"Jussila, Mr. Eiriik",male,32,0,0,STON/O 2. 3101286,7.925,,S 582 | 581,1,2,"Christy, Miss. Julie Rachel",female,25,1,1,237789,30,,S 583 | 582,1,1,"Thayer, Mrs. John Borland (Marian Longstreth Morris)",female,39,1,1,17421,110.8833,C68,C 584 | 583,0,2,"Downton, Mr. William James",male,54,0,0,28403,26,,S 585 | 584,0,1,"Ross, Mr. John Hugo",male,36,0,0,13049,40.125,A10,C 586 | 585,0,3,"Paulner, Mr. Uscher",male,,0,0,3411,8.7125,,C 587 | 586,1,1,"Taussig, Miss. Ruth",female,18,0,2,110413,79.65,E68,S 588 | 587,0,2,"Jarvis, Mr. John Denzil",male,47,0,0,237565,15,,S 589 | 588,1,1,"Frolicher-Stehli, Mr. Maxmillian",male,60,1,1,13567,79.2,B41,C 590 | 589,0,3,"Gilinski, Mr. Eliezer",male,22,0,0,14973,8.05,,S 591 | 590,0,3,"Murdlin, Mr. Joseph",male,,0,0,A./5. 3235,8.05,,S 592 | 591,0,3,"Rintamaki, Mr. Matti",male,35,0,0,STON/O 2. 3101273,7.125,,S 593 | 592,1,1,"Stephenson, Mrs. Walter Bertram (Martha Eustis)",female,52,1,0,36947,78.2667,D20,C 594 | 593,0,3,"Elsbury, Mr. William James",male,47,0,0,A/5 3902,7.25,,S 595 | 594,0,3,"Bourke, Miss. Mary",female,,0,2,364848,7.75,,Q 596 | 595,0,2,"Chapman, Mr. John Henry",male,37,1,0,SC/AH 29037,26,,S 597 | 596,0,3,"Van Impe, Mr. Jean Baptiste",male,36,1,1,345773,24.15,,S 598 | 597,1,2,"Leitch, Miss. Jessie Wills",female,,0,0,248727,33,,S 599 | 598,0,3,"Johnson, Mr. Alfred",male,49,0,0,LINE,0,,S 600 | 599,0,3,"Boulos, Mr. Hanna",male,,0,0,2664,7.225,,C 601 | 600,1,1,"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")",male,49,1,0,PC 17485,56.9292,A20,C 602 | 601,1,2,"Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)",female,24,2,1,243847,27,,S 603 | 602,0,3,"Slabenoff, Mr. Petco",male,,0,0,349214,7.8958,,S 604 | 603,0,1,"Harrington, Mr. Charles H",male,,0,0,113796,42.4,,S 605 | 604,0,3,"Torber, Mr. Ernst William",male,44,0,0,364511,8.05,,S 606 | 605,1,1,"Homer, Mr. Harry (""Mr E Haven"")",male,35,0,0,111426,26.55,,C 607 | 606,0,3,"Lindell, Mr. Edvard Bengtsson",male,36,1,0,349910,15.55,,S 608 | 607,0,3,"Karaic, Mr. Milan",male,30,0,0,349246,7.8958,,S 609 | 608,1,1,"Daniel, Mr. Robert Williams",male,27,0,0,113804,30.5,,S 610 | 609,1,2,"Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)",female,22,1,2,SC/Paris 2123,41.5792,,C 611 | 610,1,1,"Shutes, Miss. Elizabeth W",female,40,0,0,PC 17582,153.4625,C125,S 612 | 611,0,3,"Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)",female,39,1,5,347082,31.275,,S 613 | 612,0,3,"Jardin, Mr. Jose Neto",male,,0,0,SOTON/O.Q. 3101305,7.05,,S 614 | 613,1,3,"Murphy, Miss. Margaret Jane",female,,1,0,367230,15.5,,Q 615 | 614,0,3,"Horgan, Mr. John",male,,0,0,370377,7.75,,Q 616 | 615,0,3,"Brocklebank, Mr. William Alfred",male,35,0,0,364512,8.05,,S 617 | 616,1,2,"Herman, Miss. Alice",female,24,1,2,220845,65,,S 618 | 617,0,3,"Danbom, Mr. Ernst Gilbert",male,34,1,1,347080,14.4,,S 619 | 618,0,3,"Lobb, Mrs. William Arthur (Cordelia K Stanlick)",female,26,1,0,A/5. 3336,16.1,,S 620 | 619,1,2,"Becker, Miss. Marion Louise",female,4,2,1,230136,39,F4,S 621 | 620,0,2,"Gavey, Mr. Lawrence",male,26,0,0,31028,10.5,,S 622 | 621,0,3,"Yasbeck, Mr. Antoni",male,27,1,0,2659,14.4542,,C 623 | 622,1,1,"Kimball, Mr. Edwin Nelson Jr",male,42,1,0,11753,52.5542,D19,S 624 | 623,1,3,"Nakid, Mr. Sahid",male,20,1,1,2653,15.7417,,C 625 | 624,0,3,"Hansen, Mr. Henry Damsgaard",male,21,0,0,350029,7.8542,,S 626 | 625,0,3,"Bowen, Mr. David John ""Dai""",male,21,0,0,54636,16.1,,S 627 | 626,0,1,"Sutton, Mr. Frederick",male,61,0,0,36963,32.3208,D50,S 628 | 627,0,2,"Kirkland, Rev. Charles Leonard",male,57,0,0,219533,12.35,,Q 629 | 628,1,1,"Longley, Miss. Gretchen Fiske",female,21,0,0,13502,77.9583,D9,S 630 | 629,0,3,"Bostandyeff, Mr. Guentcho",male,26,0,0,349224,7.8958,,S 631 | 630,0,3,"O'Connell, Mr. Patrick D",male,,0,0,334912,7.7333,,Q 632 | 631,1,1,"Barkworth, Mr. Algernon Henry Wilson",male,80,0,0,27042,30,A23,S 633 | 632,0,3,"Lundahl, Mr. Johan Svensson",male,51,0,0,347743,7.0542,,S 634 | 633,1,1,"Stahelin-Maeglin, Dr. Max",male,32,0,0,13214,30.5,B50,C 635 | 634,0,1,"Parr, Mr. William Henry Marsh",male,,0,0,112052,0,,S 636 | 635,0,3,"Skoog, Miss. Mabel",female,9,3,2,347088,27.9,,S 637 | 636,1,2,"Davis, Miss. Mary",female,28,0,0,237668,13,,S 638 | 637,0,3,"Leinonen, Mr. Antti Gustaf",male,32,0,0,STON/O 2. 3101292,7.925,,S 639 | 638,0,2,"Collyer, Mr. Harvey",male,31,1,1,C.A. 31921,26.25,,S 640 | 639,0,3,"Panula, Mrs. Juha (Maria Emilia Ojala)",female,41,0,5,3101295,39.6875,,S 641 | 640,0,3,"Thorneycroft, Mr. Percival",male,,1,0,376564,16.1,,S 642 | 641,0,3,"Jensen, Mr. Hans Peder",male,20,0,0,350050,7.8542,,S 643 | 642,1,1,"Sagesser, Mlle. Emma",female,24,0,0,PC 17477,69.3,B35,C 644 | 643,0,3,"Skoog, Miss. Margit Elizabeth",female,2,3,2,347088,27.9,,S 645 | 644,1,3,"Foo, Mr. Choong",male,,0,0,1601,56.4958,,S 646 | 645,1,3,"Baclini, Miss. Eugenie",female,0.75,2,1,2666,19.2583,,C 647 | 646,1,1,"Harper, Mr. Henry Sleeper",male,48,1,0,PC 17572,76.7292,D33,C 648 | 647,0,3,"Cor, Mr. Liudevit",male,19,0,0,349231,7.8958,,S 649 | 648,1,1,"Simonius-Blumer, Col. Oberst Alfons",male,56,0,0,13213,35.5,A26,C 650 | 649,0,3,"Willey, Mr. Edward",male,,0,0,S.O./P.P. 751,7.55,,S 651 | 650,1,3,"Stanley, Miss. Amy Zillah Elsie",female,23,0,0,CA. 2314,7.55,,S 652 | 651,0,3,"Mitkoff, Mr. Mito",male,,0,0,349221,7.8958,,S 653 | 652,1,2,"Doling, Miss. Elsie",female,18,0,1,231919,23,,S 654 | 653,0,3,"Kalvik, Mr. Johannes Halvorsen",male,21,0,0,8475,8.4333,,S 655 | 654,1,3,"O'Leary, Miss. Hanora ""Norah""",female,,0,0,330919,7.8292,,Q 656 | 655,0,3,"Hegarty, Miss. Hanora ""Nora""",female,18,0,0,365226,6.75,,Q 657 | 656,0,2,"Hickman, Mr. Leonard Mark",male,24,2,0,S.O.C. 14879,73.5,,S 658 | 657,0,3,"Radeff, Mr. Alexander",male,,0,0,349223,7.8958,,S 659 | 658,0,3,"Bourke, Mrs. John (Catherine)",female,32,1,1,364849,15.5,,Q 660 | 659,0,2,"Eitemiller, Mr. George Floyd",male,23,0,0,29751,13,,S 661 | 660,0,1,"Newell, Mr. Arthur Webster",male,58,0,2,35273,113.275,D48,C 662 | 661,1,1,"Frauenthal, Dr. Henry William",male,50,2,0,PC 17611,133.65,,S 663 | 662,0,3,"Badt, Mr. Mohamed",male,40,0,0,2623,7.225,,C 664 | 663,0,1,"Colley, Mr. Edward Pomeroy",male,47,0,0,5727,25.5875,E58,S 665 | 664,0,3,"Coleff, Mr. Peju",male,36,0,0,349210,7.4958,,S 666 | 665,1,3,"Lindqvist, Mr. Eino William",male,20,1,0,STON/O 2. 3101285,7.925,,S 667 | 666,0,2,"Hickman, Mr. Lewis",male,32,2,0,S.O.C. 14879,73.5,,S 668 | 667,0,2,"Butler, Mr. Reginald Fenton",male,25,0,0,234686,13,,S 669 | 668,0,3,"Rommetvedt, Mr. Knud Paust",male,,0,0,312993,7.775,,S 670 | 669,0,3,"Cook, Mr. Jacob",male,43,0,0,A/5 3536,8.05,,S 671 | 670,1,1,"Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)",female,,1,0,19996,52,C126,S 672 | 671,1,2,"Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)",female,40,1,1,29750,39,,S 673 | 672,0,1,"Davidson, Mr. Thornton",male,31,1,0,F.C. 12750,52,B71,S 674 | 673,0,2,"Mitchell, Mr. Henry Michael",male,70,0,0,C.A. 24580,10.5,,S 675 | 674,1,2,"Wilhelms, Mr. Charles",male,31,0,0,244270,13,,S 676 | 675,0,2,"Watson, Mr. Ennis Hastings",male,,0,0,239856,0,,S 677 | 676,0,3,"Edvardsson, Mr. Gustaf Hjalmar",male,18,0,0,349912,7.775,,S 678 | 677,0,3,"Sawyer, Mr. Frederick Charles",male,24.5,0,0,342826,8.05,,S 679 | 678,1,3,"Turja, Miss. Anna Sofia",female,18,0,0,4138,9.8417,,S 680 | 679,0,3,"Goodwin, Mrs. Frederick (Augusta Tyler)",female,43,1,6,CA 2144,46.9,,S 681 | 680,1,1,"Cardeza, Mr. Thomas Drake Martinez",male,36,0,1,PC 17755,512.3292,B51 B53 B55,C 682 | 681,0,3,"Peters, Miss. Katie",female,,0,0,330935,8.1375,,Q 683 | 682,1,1,"Hassab, Mr. Hammad",male,27,0,0,PC 17572,76.7292,D49,C 684 | 683,0,3,"Olsvigen, Mr. Thor Anderson",male,20,0,0,6563,9.225,,S 685 | 684,0,3,"Goodwin, Mr. Charles Edward",male,14,5,2,CA 2144,46.9,,S 686 | 685,0,2,"Brown, Mr. Thomas William Solomon",male,60,1,1,29750,39,,S 687 | 686,0,2,"Laroche, Mr. Joseph Philippe Lemercier",male,25,1,2,SC/Paris 2123,41.5792,,C 688 | 687,0,3,"Panula, Mr. Jaako Arnold",male,14,4,1,3101295,39.6875,,S 689 | 688,0,3,"Dakic, Mr. Branko",male,19,0,0,349228,10.1708,,S 690 | 689,0,3,"Fischer, Mr. Eberhard Thelander",male,18,0,0,350036,7.7958,,S 691 | 690,1,1,"Madill, Miss. Georgette Alexandra",female,15,0,1,24160,211.3375,B5,S 692 | 691,1,1,"Dick, Mr. Albert Adrian",male,31,1,0,17474,57,B20,S 693 | 692,1,3,"Karun, Miss. Manca",female,4,0,1,349256,13.4167,,C 694 | 693,1,3,"Lam, Mr. Ali",male,,0,0,1601,56.4958,,S 695 | 694,0,3,"Saad, Mr. Khalil",male,25,0,0,2672,7.225,,C 696 | 695,0,1,"Weir, Col. John",male,60,0,0,113800,26.55,,S 697 | 696,0,2,"Chapman, Mr. Charles Henry",male,52,0,0,248731,13.5,,S 698 | 697,0,3,"Kelly, Mr. James",male,44,0,0,363592,8.05,,S 699 | 698,1,3,"Mullens, Miss. Katherine ""Katie""",female,,0,0,35852,7.7333,,Q 700 | 699,0,1,"Thayer, Mr. John Borland",male,49,1,1,17421,110.8833,C68,C 701 | 700,0,3,"Humblen, Mr. Adolf Mathias Nicolai Olsen",male,42,0,0,348121,7.65,F G63,S 702 | 701,1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18,1,0,PC 17757,227.525,C62 C64,C 703 | 702,1,1,"Silverthorne, Mr. Spencer Victor",male,35,0,0,PC 17475,26.2875,E24,S 704 | 703,0,3,"Barbara, Miss. Saiide",female,18,0,1,2691,14.4542,,C 705 | 704,0,3,"Gallagher, Mr. Martin",male,25,0,0,36864,7.7417,,Q 706 | 705,0,3,"Hansen, Mr. Henrik Juul",male,26,1,0,350025,7.8542,,S 707 | 706,0,2,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",male,39,0,0,250655,26,,S 708 | 707,1,2,"Kelly, Mrs. Florence ""Fannie""",female,45,0,0,223596,13.5,,S 709 | 708,1,1,"Calderhead, Mr. Edward Pennington",male,42,0,0,PC 17476,26.2875,E24,S 710 | 709,1,1,"Cleaver, Miss. Alice",female,22,0,0,113781,151.55,,S 711 | 710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C 712 | 711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24,0,0,PC 17482,49.5042,C90,C 713 | 712,0,1,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S 714 | 713,1,1,"Taylor, Mr. Elmer Zebley",male,48,1,0,19996,52,C126,S 715 | 714,0,3,"Larsson, Mr. August Viktor",male,29,0,0,7545,9.4833,,S 716 | 715,0,2,"Greenberg, Mr. Samuel",male,52,0,0,250647,13,,S 717 | 716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19,0,0,348124,7.65,F G73,S 718 | 717,1,1,"Endres, Miss. Caroline Louise",female,38,0,0,PC 17757,227.525,C45,C 719 | 718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",female,27,0,0,34218,10.5,E101,S 720 | 719,0,3,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q 721 | 720,0,3,"Johnson, Mr. Malkolm Joackim",male,33,0,0,347062,7.775,,S 722 | 721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6,0,1,248727,33,,S 723 | 722,0,3,"Jensen, Mr. Svend Lauritz",male,17,1,0,350048,7.0542,,S 724 | 723,0,2,"Gillespie, Mr. William Henry",male,34,0,0,12233,13,,S 725 | 724,0,2,"Hodges, Mr. Henry Price",male,50,0,0,250643,13,,S 726 | 725,1,1,"Chambers, Mr. Norman Campbell",male,27,1,0,113806,53.1,E8,S 727 | 726,0,3,"Oreskovic, Mr. Luka",male,20,0,0,315094,8.6625,,S 728 | 727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S 729 | 728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q 730 | 729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S 731 | 730,0,3,"Ilmakangas, Miss. Pieta Sofia",female,25,1,0,STON/O2. 3101271,7.925,,S 732 | 731,1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S 733 | 732,0,3,"Hassan, Mr. Houssein G N",male,11,0,0,2699,18.7875,,C 734 | 733,0,2,"Knight, Mr. Robert J",male,,0,0,239855,0,,S 735 | 734,0,2,"Berriman, Mr. William John",male,23,0,0,28425,13,,S 736 | 735,0,2,"Troupiansky, Mr. Moses Aaron",male,23,0,0,233639,13,,S 737 | 736,0,3,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S 738 | 737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48,1,3,W./C. 6608,34.375,,S 739 | 738,1,1,"Lesurer, Mr. Gustave J",male,35,0,0,PC 17755,512.3292,B101,C 740 | 739,0,3,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S 741 | 740,0,3,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S 742 | 741,1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30,D45,S 743 | 742,0,1,"Cavendish, Mr. Tyrell William",male,36,1,0,19877,78.85,C46,S 744 | 743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C 745 | 744,0,3,"McNamee, Mr. Neal",male,24,1,0,376566,16.1,,S 746 | 745,1,3,"Stranden, Mr. Juho",male,31,0,0,STON/O 2. 3101288,7.925,,S 747 | 746,0,1,"Crosby, Capt. Edward Gifford",male,70,1,1,WE/P 5735,71,B22,S 748 | 747,0,3,"Abbott, Mr. Rossmore Edward",male,16,1,1,C.A. 2673,20.25,,S 749 | 748,1,2,"Sinkkonen, Miss. Anna",female,30,0,0,250648,13,,S 750 | 749,0,1,"Marvin, Mr. Daniel Warner",male,19,1,0,113773,53.1,D30,S 751 | 750,0,3,"Connaghton, Mr. Michael",male,31,0,0,335097,7.75,,Q 752 | 751,1,2,"Wells, Miss. Joan",female,4,1,1,29103,23,,S 753 | 752,1,3,"Moor, Master. Meier",male,6,0,1,392096,12.475,E121,S 754 | 753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33,0,0,345780,9.5,,S 755 | 754,0,3,"Jonkoff, Mr. Lalio",male,23,0,0,349204,7.8958,,S 756 | 755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S 757 | 756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S 758 | 757,0,3,"Carlsson, Mr. August Sigfrid",male,28,0,0,350042,7.7958,,S 759 | 758,0,2,"Bailey, Mr. Percy Andrew",male,18,0,0,29108,11.5,,S 760 | 759,0,3,"Theobald, Mr. Thomas Leonard",male,34,0,0,363294,8.05,,S 761 | 760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33,0,0,110152,86.5,B77,S 762 | 761,0,3,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S 763 | 762,0,3,"Nirva, Mr. Iisakki Antino Aijo",male,41,0,0,SOTON/O2 3101272,7.125,,S 764 | 763,1,3,"Barah, Mr. Hanna Assi",male,20,0,0,2663,7.2292,,C 765 | 764,1,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36,1,2,113760,120,B96 B98,S 766 | 765,0,3,"Eklund, Mr. Hans Linus",male,16,0,0,347074,7.775,,S 767 | 766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51,1,0,13502,77.9583,D11,S 768 | 767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C 769 | 768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q 770 | 769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q 771 | 770,0,3,"Gronnestad, Mr. Daniel Danielsen",male,32,0,0,8471,8.3625,,S 772 | 771,0,3,"Lievens, Mr. Rene Aime",male,24,0,0,345781,9.5,,S 773 | 772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S 774 | 773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S 775 | 774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C 776 | 775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S 777 | 776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S 778 | 777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q 779 | 778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S 780 | 779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q 781 | 780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S 782 | 781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C 783 | 782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S 784 | 783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S 785 | 784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S 786 | 785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S 787 | 786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S 788 | 787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S 789 | 788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q 790 | 789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S 791 | 790,0,1,"Guggenheim, Mr. Benjamin",male,46,0,0,PC 17593,79.2,B82 B84,C 792 | 791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q 793 | 792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S 794 | 793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S 795 | 794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C 796 | 795,0,3,"Dantcheff, Mr. Ristiu",male,25,0,0,349203,7.8958,,S 797 | 796,0,2,"Otter, Mr. Richard",male,39,0,0,28213,13,,S 798 | 797,1,1,"Leader, Dr. Alice (Farnham)",female,49,0,0,17465,25.9292,D17,S 799 | 798,1,3,"Osman, Mrs. Mara",female,31,0,0,349244,8.6833,,S 800 | 799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30,0,0,2685,7.2292,,C 801 | 800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S 802 | 801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S 803 | 802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31,1,1,C.A. 31921,26.25,,S 804 | 803,1,1,"Carter, Master. William Thornton II",male,11,1,2,113760,120,B96 B98,S 805 | 804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C 806 | 805,1,3,"Hedman, Mr. Oskar Arvid",male,27,0,0,347089,6.975,,S 807 | 806,0,3,"Johansson, Mr. Karl Johan",male,31,0,0,347063,7.775,,S 808 | 807,0,1,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0,A36,S 809 | 808,0,3,"Pettersson, Miss. Ellen Natalia",female,18,0,0,347087,7.775,,S 810 | 809,0,2,"Meyer, Mr. August",male,39,0,0,248723,13,,S 811 | 810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33,1,0,113806,53.1,E8,S 812 | 811,0,3,"Alexander, Mr. William",male,26,0,0,3474,7.8875,,S 813 | 812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S 814 | 813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S 815 | 814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S 816 | 815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S 817 | 816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S 818 | 817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S 819 | 818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C 820 | 819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S 821 | 820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S 822 | 821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S 823 | 822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S 824 | 823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S 825 | 824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S 826 | 825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S 827 | 826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q 828 | 827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S 829 | 828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C 830 | 829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q 831 | 830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28, 832 | 831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C 833 | 832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S 834 | 833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C 835 | 834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S 836 | 835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S 837 | 836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C 838 | 837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S 839 | 838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S 840 | 839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S 841 | 840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C 842 | 841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S 843 | 842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S 844 | 843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C 845 | 844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C 846 | 845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S 847 | 846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S 848 | 847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S 849 | 848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C 850 | 849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S 851 | 850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C 852 | 851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S 853 | 852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S 854 | 853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C 855 | 854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S 856 | 855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S 857 | 856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S 858 | 857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S 859 | 858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S 860 | 859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C 861 | 860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C 862 | 861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S 863 | 862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S 864 | 863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S 865 | 864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S 866 | 865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S 867 | 866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S 868 | 867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C 869 | 868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S 870 | 869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S 871 | 870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S 872 | 871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S 873 | 872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S 874 | 873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S 875 | 874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S 876 | 875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C 877 | 876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C 878 | 877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S 879 | 878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S 880 | 879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S 881 | 880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C 882 | 881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S 883 | 882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S 884 | 883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S 885 | 884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S 886 | 885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S 887 | 886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q 888 | 887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S 889 | 888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S 890 | 889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S 891 | 890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C 892 | 891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q 893 | -------------------------------------------------------------------------------- /R/Introduction to R/R for data cleaning.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datasciencesg/workshops/a867fbf3016a4722c8ff994a1d1f6f2e0adcf659/R/Introduction to R/R for data cleaning.pdf -------------------------------------------------------------------------------- /R/Introduction to R/README.md: -------------------------------------------------------------------------------- 1 | # Introduction to R 2 | - Designed by @linchun1993 3 | - Refer to PDF to get started 4 | -------------------------------------------------------------------------------- /R/Introduction to R/iris.csv: -------------------------------------------------------------------------------- 1 | Sepal.Length,Sepal.Width,Petal.Length,Petal.Width,Species 2 | 5.1,3.5,1.4,0.2,Iris-setosa 3 | 4.9,3,1.4,0.2,Iris-setosa 4 | 4.7,3.2,1.3,0.2,Iris-setosa 5 | 4.6,3.1,1.5,0.2,Iris-setosa 6 | 5,3.6,1.4,0.2,Iris-setosa 7 | 5.4,3.9,1.7,0.4,Iris-setosa 8 | 4.6,3.4,1.4,0.3,Iris-setosa 9 | 5,3.4,1.5,0.2,Iris-setosa 10 | 4.4,2.9,1.4,0.2,Iris-setosa 11 | 4.9,3.1,1.5,0.1,Iris-setosa 12 | 5.4,3.7,1.5,0.2,Iris-setosa 13 | 4.8,3.4,1.6,0.2,Iris-setosa 14 | 4.8,3,1.4,0.1,Iris-setosa 15 | 4.3,3,1.1,0.1,Iris-setosa 16 | 5.8,4,1.2,0.2,Iris-setosa 17 | 5.7,4.4,1.5,0.4,Iris-setosa 18 | 5.4,3.9,1.3,0.4,Iris-setosa 19 | 5.1,3.5,1.4,0.3,Iris-setosa 20 | 5.7,3.8,1.7,0.3,Iris-setosa 21 | 5.1,3.8,1.5,0.3,Iris-setosa 22 | 5.4,3.4,1.7,0.2,Iris-setosa 23 | 5.1,3.7,1.5,0.4,Iris-setosa 24 | 4.6,3.6,1,0.2,Iris-setosa 25 | 5.1,3.3,1.7,0.5,Iris-setosa 26 | 4.8,3.4,1.9,0.2,Iris-setosa 27 | 5,3,1.6,0.2,Iris-setosa 28 | 5,3.4,1.6,0.4,Iris-setosa 29 | 5.2,3.5,1.5,0.2,Iris-setosa 30 | 5.2,3.4,1.4,0.2,Iris-setosa 31 | 4.7,3.2,1.6,0.2,Iris-setosa 32 | 4.8,3.1,1.6,0.2,Iris-setosa 33 | 5.4,3.4,1.5,0.4,Iris-setosa 34 | 5.2,4.1,1.5,0.1,Iris-setosa 35 | 5.5,4.2,1.4,0.2,Iris-setosa 36 | 4.9,3.1,1.5,0.1,Iris-setosa 37 | 5,3.2,1.2,0.2,Iris-setosa 38 | 5.5,3.5,1.3,0.2,Iris-setosa 39 | 4.9,3.1,1.5,0.1,Iris-setosa 40 | 4.4,3,1.3,0.2,Iris-setosa 41 | 5.1,3.4,1.5,0.2,Iris-setosa 42 | 5,3.5,1.3,0.3,Iris-setosa 43 | 4.5,2.3,1.3,0.3,Iris-setosa 44 | 4.4,3.2,1.3,0.2,Iris-setosa 45 | 5,3.5,1.6,0.6,Iris-setosa 46 | 5.1,3.8,1.9,0.4,Iris-setosa 47 | 4.8,3,1.4,0.3,Iris-setosa 48 | 5.1,3.8,1.6,0.2,Iris-setosa 49 | 4.6,3.2,1.4,0.2,Iris-setosa 50 | 5.3,3.7,1.5,0.2,Iris-setosa 51 | 5,3.3,1.4,0.2,Iris-setosa 52 | 7,3.2,4.7,1.4,Iris-versicolor 53 | 6.4,3.2,4.5,1.5,Iris-versicolor 54 | 6.9,3.1,4.9,1.5,Iris-versicolor 55 | 5.5,2.3,4,1.3,Iris-versicolor 56 | 6.5,2.8,4.6,1.5,Iris-versicolor 57 | 5.7,2.8,4.5,1.3,Iris-versicolor 58 | 6.3,3.3,4.7,1.6,Iris-versicolor 59 | 4.9,2.4,3.3,1,Iris-versicolor 60 | 6.6,2.9,4.6,1.3,Iris-versicolor 61 | 5.2,2.7,3.9,1.4,Iris-versicolor 62 | 5,2,3.5,1,Iris-versicolor 63 | 5.9,3,4.2,1.5,Iris-versicolor 64 | 6,2.2,4,1,Iris-versicolor 65 | 6.1,2.9,4.7,1.4,Iris-versicolor 66 | 5.6,2.9,3.6,1.3,Iris-versicolor 67 | 6.7,3.1,4.4,1.4,Iris-versicolor 68 | 5.6,3,4.5,1.5,Iris-versicolor 69 | 5.8,2.7,4.1,1,Iris-versicolor 70 | 6.2,2.2,4.5,1.5,Iris-versicolor 71 | 5.6,2.5,3.9,1.1,Iris-versicolor 72 | 5.9,3.2,4.8,1.8,Iris-versicolor 73 | 6.1,2.8,4,1.3,Iris-versicolor 74 | 6.3,2.5,4.9,1.5,Iris-versicolor 75 | 6.1,2.8,4.7,1.2,Iris-versicolor 76 | 6.4,2.9,4.3,1.3,Iris-versicolor 77 | 6.6,3,4.4,1.4,Iris-versicolor 78 | 6.8,2.8,4.8,1.4,Iris-versicolor 79 | 6.7,3,5,1.7,Iris-versicolor 80 | 6,2.9,4.5,1.5,Iris-versicolor 81 | 5.7,2.6,3.5,1,Iris-versicolor 82 | 5.5,2.4,3.8,1.1,Iris-versicolor 83 | 5.5,2.4,3.7,1,Iris-versicolor 84 | 5.8,2.7,3.9,1.2,Iris-versicolor 85 | 6,2.7,5.1,1.6,Iris-versicolor 86 | 5.4,3,4.5,1.5,Iris-versicolor 87 | 6,3.4,4.5,1.6,Iris-versicolor 88 | 6.7,3.1,4.7,1.5,Iris-versicolor 89 | 6.3,2.3,4.4,1.3,Iris-versicolor 90 | 5.6,3,4.1,1.3,Iris-versicolor 91 | 5.5,2.5,4,1.3,Iris-versicolor 92 | 5.5,2.6,4.4,1.2,Iris-versicolor 93 | 6.1,3,4.6,1.4,Iris-versicolor 94 | 5.8,2.6,4,1.2,Iris-versicolor 95 | 5,2.3,3.3,1,Iris-versicolor 96 | 5.6,2.7,4.2,1.3,Iris-versicolor 97 | 5.7,3,4.2,1.2,Iris-versicolor 98 | 5.7,2.9,4.2,1.3,Iris-versicolor 99 | 6.2,2.9,4.3,1.3,Iris-versicolor 100 | 5.1,2.5,3,1.1,Iris-versicolor 101 | 5.7,2.8,4.1,1.3,Iris-versicolor 102 | 6.3,3.3,6,2.5,Iris-virginica 103 | 5.8,2.7,5.1,1.9,Iris-virginica 104 | 7.1,3,5.9,2.1,Iris-virginica 105 | 6.3,2.9,5.6,1.8,Iris-virginica 106 | 6.5,3,5.8,2.2,Iris-virginica 107 | 7.6,3,6.6,2.1,Iris-virginica 108 | 4.9,2.5,4.5,1.7,Iris-virginica 109 | 7.3,2.9,6.3,1.8,Iris-virginica 110 | 6.7,2.5,5.8,1.8,Iris-virginica 111 | 7.2,3.6,6.1,2.5,Iris-virginica 112 | 6.5,3.2,5.1,2,Iris-virginica 113 | 6.4,2.7,5.3,1.9,Iris-virginica 114 | 6.8,3,5.5,2.1,Iris-virginica 115 | 5.7,2.5,5,2,Iris-virginica 116 | 5.8,2.8,5.1,2.4,Iris-virginica 117 | 6.4,3.2,5.3,2.3,Iris-virginica 118 | 6.5,3,5.5,1.8,Iris-virginica 119 | 7.7,3.8,6.7,2.2,Iris-virginica 120 | 7.7,2.6,6.9,2.3,Iris-virginica 121 | 6,2.2,5,1.5,Iris-virginica 122 | 6.9,3.2,5.7,2.3,Iris-virginica 123 | 5.6,2.8,4.9,2,Iris-virginica 124 | 7.7,2.8,6.7,2,Iris-virginica 125 | 6.3,2.7,4.9,1.8,Iris-virginica 126 | 6.7,3.3,5.7,2.1,Iris-virginica 127 | 7.2,3.2,6,1.8,Iris-virginica 128 | 6.2,2.8,4.8,1.8,Iris-virginica 129 | 6.1,3,4.9,1.8,Iris-virginica 130 | 6.4,2.8,5.6,2.1,Iris-virginica 131 | 7.2,3,5.8,1.6,Iris-virginica 132 | 7.4,2.8,6.1,1.9,Iris-virginica 133 | 7.9,3.8,6.4,2,Iris-virginica 134 | 6.4,2.8,5.6,2.2,Iris-virginica 135 | 6.3,2.8,5.1,1.5,Iris-virginica 136 | 6.1,2.6,5.6,1.4,Iris-virginica 137 | 7.7,3,6.1,2.3,Iris-virginica 138 | 6.3,3.4,5.6,2.4,Iris-virginica 139 | 6.4,3.1,5.5,1.8,Iris-virginica 140 | 6,3,4.8,1.8,Iris-virginica 141 | 6.9,3.1,5.4,2.1,Iris-virginica 142 | 6.7,3.1,5.6,2.4,Iris-virginica 143 | 6.9,3.1,5.1,2.3,Iris-virginica 144 | 5.8,2.7,5.1,1.9,Iris-virginica 145 | 6.8,3.2,5.9,2.3,Iris-virginica 146 | 6.7,3.3,5.7,2.5,Iris-virginica 147 | 6.7,3,5.2,2.3,Iris-virginica 148 | 6.3,2.5,5,1.9,Iris-virginica 149 | 6.5,3,5.2,2,Iris-virginica 150 | 6.2,3.4,5.4,2.3,Iris-virginica 151 | 5.9,3,5.1,1.8,Iris-virginica -------------------------------------------------------------------------------- /R/Introduction to R/iris_2.csv: -------------------------------------------------------------------------------- 1 | Sepal.Length,Sepal.Width,Petal.Length,Petal.Width,Species 2 | 5.1,3.5,1.4,0.2,Iris-setosa$ 3 | 4.9,3,1.4,0.2,Iris-setosa$ 4 | 4.7,3.2,1.3,0.2,Iris-setosa$ 5 | 4.6,3.1,1.5,0.2,Iris-setosa$ 6 | ,3.6,1.4,0.2,Iris-setosa$ 7 | 5.4,3.9,1.7,0.4,Iris-setosa$ 8 | 4.6,3.4,1.4,0.3,Iris-setosa$ 9 | ,3.4,1.5,0.2,Iris-setosa$ 10 | 4.4,2.9,1.4,0.2,Iris-setosa$ 11 | 4.9,3.1,1.5,0.1,Iris-setosa$ 12 | 5.4,3.7,1.5,0.2,Iris-setosa$ 13 | 4.8,3.4,1.6,0.2,Iris-setosa$ 14 | 4.8,3,1.4,0.1,Iris-setosa$ 15 | 4.3,3,1.1,0.1,Iris-setosa$ 16 | 5.8,4,1.2,0.2,Iris-setosa$ 17 | 5.7,4.4,1.5,0.4,Iris-setosa$ 18 | 5.4,3.9,1.3,0.4,Iris-setosa$ 19 | 5.1,3.5,1.4,0.3,Iris-setosa$ 20 | 5.7,3.8,1.7,0.3,Iris-setosa$ 21 | 5.1,3.8,1.5,0.3,Iris-setosa$ 22 | 5.4,3.4,1.7,0.2,Iris-setosa$ 23 | ,3.7,1.5,0.4,Iris-setosa$ 24 | 4.6,3.6,1,0.2,Iris-setosa$ 25 | 5.1,3.3,1.7,0.5,Iris-setosa$ 26 | 4.8,3.4,1.9,0.2,Iris-setosa$ 27 | 5,3,1.6,0.2,Iris-setosa$ 28 | 5,3.4,1.6,0.4,Iris-setosa$ 29 | 5.2,3.5,1.5,0.2,Iris-setosa$ 30 | 5.2,3.4,1.4,0.2,Iris-setosa$ 31 | 4.7,3.2,1.6,0.2,Iris-setosa$ 32 | 4.8,3.1,1.6,0.2,Iris-setosa$ 33 | 5.4,3.4,1.5,0.4,Iris-setosa$ 34 | 5.2,4.1,1.5,0.1,Iris-setosa$ 35 | 5.5,4.2,1.4,0.2,Iris-setosa$ 36 | 4.9,3.1,1.5,0.1,Iris-setosa$ 37 | 5,3.2,1.2,0.2,Iris-setosa$ 38 | 5.5,,1.3,0.2,Iris-setosa$ 39 | 4.9,3.1,1.5,0.1,Iris-setosa$ 40 | 4.4,3,1.3,0.2,Iris-setosa$ 41 | 5.1,3.4,1.5,0.2,Iris-setosa$ 42 | 5,3.5,1.3,0.3,Iris-setosa$ 43 | 4.5,2.3,1.3,0.3,Iris-setosa$ 44 | 4.4,3.2,1.3,0.2,Iris-setosa$ 45 | ,3.5,1.6,0.6,Iris-setosa$ 46 | 5.1,3.8,,0.4,Iris-setosa$ 47 | 4.8,3,1.4,0.3,Iris-setosa$ 48 | 5.1,3.8,1.6,0.2,Iris-setosa$ 49 | 4.6,3.2,1.4,0.2,Iris-setosa$ 50 | 5.3,,1.5,0.2,Iris-setosa$ 51 | 5,3.3,1.4,0.2,Iris-setosa$ 52 | 7,3.2,4.7,1.4,Iris-versicolor$ 53 | 6.4,3.2,4.5,1.5,Iris-versicolor$ 54 | 6.9,3.1,4.9,1.5,Iris-versicolor$ 55 | 5.5,2.3,4,1.3,Iris-versicolor$ 56 | 6.5,2.8,4.6,1.5,Iris-versicolor$ 57 | 5.7,2.8,4.5,1.3,Iris-versicolor$ 58 | 6.3,3.3,4.7,1.6,Iris-versicolor$ 59 | 4.9,2.4,3.3,1,Iris-versicolor$ 60 | 6.6,2.9,4.6,1.3,Iris-versicolor$ 61 | 5.2,2.7,3.9,1.4,Iris-versicolor$ 62 | 5,2,3.5,1,Iris-versicolor$ 63 | ,3,4.2,1.5,Iris-versicolor$ 64 | 6,2.2,4,1,Iris-versicolor$ 65 | 6.1,2.9,4.7,1.4,Iris-versicolor$ 66 | 5.6,2.9,3.6,1.3,Iris-versicolor$ 67 | 6.7,3.1,4.4,1.4,Iris-versicolor$ 68 | 5.6,3,4.5,1.5,Iris-versicolor$ 69 | 5.8,2.7,4.1,1,Iris-versicolor$ 70 | 6.2,2.2,4.5,1.5,Iris-versicolor$ 71 | 5.6,2.5,3.9,1.1,Iris-versicolor$ 72 | 5.9,,4.8,1.8,Iris-versicolor$ 73 | 6.1,2.8,4,1.3,Iris-versicolor$ 74 | 6.3,2.5,4.9,1.5,Iris-versicolor$ 75 | 6.1,2.8,4.7,1.2,Iris-versicolor$ 76 | 6.4,2.9,4.3,1.3,Iris-versicolor$ 77 | 6.6,3,4.4,1.4,Iris-versicolor$ 78 | 6.8,2.8,4.8,1.4,Iris-versicolor$ 79 | ,3,5,1.7,Iris-versicolor$ 80 | 6,2.9,4.5,1.5,Iris-versicolor$ 81 | 5.7,2.6,3.5,1,Iris-versicolor$ 82 | 5.5,2.4,3.8,1.1,Iris-versicolor$ 83 | 5.5,2.4,3.7,1,Iris-versicolor$ 84 | 5.8,2.7,3.9,1.2,Iris-versicolor$ 85 | 6,2.7,5.1,1.6,Iris-versicolor$ 86 | 5.4,3,4.5,1.5,Iris-versicolor$ 87 | 6,3.4,4.5,1.6,Iris-versicolor$ 88 | 6.7,3.1,4.7,1.5,Iris-versicolor$ 89 | 6.3,2.3,4.4,1.3,Iris-versicolor$ 90 | 5.6,3,4.1,1.3,Iris-versicolor$ 91 | 5.5,2.5,,1.3,Iris-versicolor$ 92 | 5.5,2.6,4.4,1.2,Iris-versicolor$ 93 | ,3,4.6,1.4,Iris-versicolor$ 94 | 5.8,2.6,4,1.2,Iris-versicolor$ 95 | 5,2.3,3.3,1,Iris-versicolor$ 96 | 5.6,2.7,4.2,1.3,Iris-versicolor$ 97 | 5.7,3,4.2,1.2,Iris-versicolor$ 98 | 5.7,,4.2,1.3,Iris-versicolor$ 99 | 6.2,2.9,4.3,1.3,Iris-versicolor$ 100 | 5.1,2.5,3,1.1,Iris-versicolor$ 101 | 5.7,2.8,4.1,1.3,Iris-versicolor$ 102 | 6.3,3.3,6,2.5,Iris-virginica$ 103 | 5.8,2.7,5.1,1.9,Iris-virginica$ 104 | 7.1,3,5.9,2.1,Iris-virginica$ 105 | 6.3,2.9,,1.8,Iris-virginica$ 106 | ,3,5.8,2.2,Iris-virginica$ 107 | 7.6,3,6.6,2.1,Iris-virginica$ 108 | 4.9,2.5,4.5,1.7,Iris-virginica$ 109 | 7.3,2.9,6.3,1.8,Iris-virginica$ 110 | 6.7,,5.8,1.8,Iris-virginica$ 111 | 7.2,3.6,6.1,2.5,Iris-virginica$ 112 | 6.5,3.2,5.1,2,Iris-virginica$ 113 | 6.4,2.7,5.3,1.9,Iris-virginica$ 114 | 6.8,3,5.5,2.1,Iris-virginica$ 115 | 5.7,2.5,5,2,Iris-virginica$ 116 | 5.8,2.8,5.1,2.4,Iris-virginica$ 117 | 6.4,3.2,5.3,2.3,Iris-virginica$ 118 | 6.5,3,5.5,1.8,Iris-virginica$ 119 | 7.7,3.8,6.7,2.2,Iris-virginica$ 120 | 7.7,2.6,6.9,2.3,Iris-virginica$ 121 | 6,2.2,5,1.5,Iris-virginica$ 122 | 6.9,3.2,5.7,2.3,Iris-virginica$ 123 | 5.6,2.8,4.9,2,Iris-virginica$ 124 | 7.7,2.8,6.7,2,Iris-virginica$ 125 | 6.3,2.7,4.9,1.8,Iris-virginica$ 126 | 6.7,3.3,5.7,2.1,Iris-virginica$ 127 | 7.2,3.2,6,1.8,Iris-virginica$ 128 | 6.2,2.8,4.8,1.8,Iris-virginica$ 129 | 6.1,3,4.9,1.8,Iris-virginica$ 130 | 6.4,2.8,5.6,2.1,Iris-virginica$ 131 | 7.2,3,5.8,1.6,Iris-virginica$ 132 | 7.4,2.8,6.1,1.9,Iris-virginica$ 133 | 7.9,3.8,6.4,2,Iris-virginica$ 134 | 6.4,2.8,5.6,2.2,Iris-virginica$ 135 | 6.3,2.8,5.1,1.5,Iris-virginica$ 136 | 6.1,2.6,5.6,1.4,Iris-virginica$ 137 | 7.7,3,6.1,2.3,Iris-virginica$ 138 | 6.3,3.4,5.6,2.4,Iris-virginica$ 139 | 6.4,3.1,5.5,1.8,Iris-virginica$ 140 | 6,3,4.8,1.8,Iris-virginica$ 141 | 6.9,3.1,5.4,2.1,Iris-virginica$ 142 | 6.7,3.1,5.6,2.4,Iris-virginica$ 143 | 6.9,3.1,5.1,2.3,Iris-virginica$ 144 | 5.8,2.7,5.1,1.9,Iris-virginica$ 145 | 6.8,3.2,5.9,2.3,Iris-virginica$ 146 | 6.7,3.3,5.7,2.5,Iris-virginica$ 147 | 6.7,3,5.2,2.3,Iris-virginica$ 148 | 6.3,2.5,5,1.9,Iris-virginica$ 149 | 6.5,3,5.2,2,Iris-virginica$ 150 | 6.2,3.4,5.4,2.3,Iris-virginica$ 151 | 5.9,3,5.1,1.8,Iris-virginica$ -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # workshops 2 | Collection of all DSSG workshops 3 | --------------------------------------------------------------------------------