├── 03_Programming ├── 15_csv-example.csv ├── 21_install-pkgs.py ├── 1_python-basics.py ├── 4_r_basics.R ├── 15_reading-csv.py └── README.md ├── 06_Data-Visualization ├── 4_histogram-pie.R ├── 1_data-exploration.R └── README.md ├── 08_Data-Ingestion └── README.md ├── 02_Statistics ├── 2_descriptive-statistics.py └── README.md ├── 09_Data-Munging └── README.md ├── 05_Text-Mining-NLP └── README.md ├── 01_Fundamentals ├── 16_regex.py ├── 1_fundamentals.py └── README.md ├── 07_Big-Data └── README.md ├── README.md ├── 10_Toolbox └── README.md ├── 04_Machine-Learning ├── 22_perceptron.py └── README.md └── LICENCE.txt /03_Programming/15_csv-example.csv: -------------------------------------------------------------------------------- 1 | Leaf Green 2 | Lemon Yellow 3 | Cherry Red 4 | Snow White 5 | -------------------------------------------------------------------------------- /03_Programming/21_install-pkgs.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | 3 | # Import pip 4 | import pip 5 | 6 | # Build an installation function 7 | def install(package): 8 | pip.main(['install', package]) 9 | 10 | # Execution 11 | install('fooBAR') 12 | -------------------------------------------------------------------------------- /06_Data-Visualization/4_histogram-pie.R: -------------------------------------------------------------------------------- 1 | # Two plot on the same window 2 | par(mfrow = c(1,2)) 3 | # Histogram 4 | data <- iris 5 | hist(data[,2], main = "histogram about sepal width", xlab = "sepal width", ylab = "Frequency") 6 | # Pie chart 7 | classes <- summary(data[,5]) 8 | pie(classes, main = "Iris species") 9 | -------------------------------------------------------------------------------- /03_Programming/1_python-basics.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | 3 | ''' 1_ Python basics ''' 4 | 5 | # Print something 6 | print('Hello, world') 7 | 8 | # Assign a variable 9 | a = 23 10 | b = 'Hi guys, i\'m a text variable' 11 | print('This is my variable: {}'.format(b)) 12 | 13 | # Mathematics 14 | c = (a + 2) * (245 / 23) 15 | print('This is mathe-magic: {}'.format(c)) 16 | 17 | -------------------------------------------------------------------------------- /08_Data-Ingestion/README.md: -------------------------------------------------------------------------------- 1 | # 8_ Data Ingestion 2 | 3 | ## 1_ Summary of data formats 4 | 5 | ## 2_ Data discovery 6 | 7 | ## 3_ Data sources & Acquisition 8 | 9 | ## 4_ Data integration 10 | 11 | ## 5_ Data fusion 12 | 13 | ## 6_ Transformation & enrichment 14 | 15 | ## 7_ Data survey 16 | 17 | ## 8_ Google OpenRefine 18 | 19 | ## 9_ How much data ? 20 | 21 | ## 10_ Using ETL 22 | -------------------------------------------------------------------------------- /02_Statistics/2_descriptive-statistics.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | 3 | # Import 4 | import numpy as np 5 | 6 | # Create a dataset 7 | dataset = [12, 52, 45, 65, 78, 11, 12, 54, 56] 8 | 9 | # Apply mean/median functions 10 | dataset_mean = np.mean(dataset) 11 | dataset_median = np.median(dataset) 12 | 13 | # Print results 14 | print('Mean: {}, median: {}'.format(dataset_mean, dataset_median)) 15 | -------------------------------------------------------------------------------- /09_Data-Munging/README.md: -------------------------------------------------------------------------------- 1 | # 9_ Data Munging 2 | 3 | ## 1_ Dim. and num. reduction 4 | 5 | ## 2_ Normalization 6 | 7 | ## 3_ Data scrubbing 8 | 9 | ## 4_ Handling missing Values 10 | 11 | ## 5_ Unbiased estimators 12 | 13 | ## 6_ Binning Sparse Values 14 | 15 | ## 7_ Feature extraction 16 | 17 | ## 8_ Denoising 18 | 19 | ## 9_ Sampling 20 | 21 | ## 10_ Stratified sampling 22 | 23 | ## 11_ PCA 24 | -------------------------------------------------------------------------------- /03_Programming/4_r_basics.R: -------------------------------------------------------------------------------- 1 | # Import data already loaded in R into the variable "data" 2 | data <- iris 3 | # The same as 4 | data = iris 5 | # To read a CSV 6 | data <- read.csv('path/to/the/file', sep = ',') 7 | # Select the 2nd column 8 | data[,2] 9 | # And the 3rd line 10 | data[3,] 11 | # Mean of the 2nd column 12 | mean(data[,2]) 13 | # Histogram of the 3rd column 14 | hist(data[,3]) 15 | # To install the package "foobar" 16 | install.packages("foobar") 17 | # And load it 18 | library(foobar) 19 | -------------------------------------------------------------------------------- /05_Text-Mining-NLP/README.md: -------------------------------------------------------------------------------- 1 | # 5_ Text Mining 2 | 3 | ## 1_ Corpus 4 | 5 | ## 2_ Named Entity Recognition 6 | 7 | ## 3_ Text Analysis 8 | 9 | ## 4_ UIMA 10 | 11 | ## 5_ Term Document matrix 12 | 13 | ## 6_ Term frequency and Weight 14 | 15 | ## 7_ Support Vector Machines (SVM) 16 | 17 | ## 8_ Association rules 18 | 19 | ## 9_ Market based analysis 20 | 21 | ## 10_ Feature extraction 22 | 23 | ## 11_ Using mahout 24 | 25 | ## 12_ Using Weka 26 | 27 | ## 13_ Using NLTK 28 | 29 | ## 14_ Classify text 30 | 31 | ## 15_ Vocabulary mapping 32 | -------------------------------------------------------------------------------- /01_Fundamentals/16_regex.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | 3 | # Text coming from Python module __re__ 4 | text = 'This module provides regular expression matching operations similar to those found in Perl.' 5 | 6 | # import re library 7 | import re 8 | 9 | # Substitution of "Perl" by "every languages" 10 | new_text = re.sub('Perl', 'every languages', text) 11 | print(new_text) 12 | 13 | # Searching for capitals letters in the text 14 | new_text = re.findall('[A-Z]', text) 15 | print(new_text) 16 | 17 | # Test if a word is in the text or not 18 | new_text = re.match('.*regular.*', text) 19 | print(new_text) 20 | -------------------------------------------------------------------------------- /03_Programming/15_reading-csv.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | 3 | 4 | ''' Example 1 ''' 5 | # Import 6 | import csv 7 | # Open file 8 | with open('15_csv-example.csv') as csv_file: 9 | # Read file with csv library 10 | read_csv_file = csv.reader(csv_file, delimiter='\t') 11 | # Parse every row to print it 12 | for row in read_csv_file: 13 | print('\'s color is '.join(row)) 14 | 15 | 16 | ''' Exemple 2 ''' 17 | # Import 18 | import re 19 | # Open file 20 | csv_file = open('15_csv-example.csv', 'r') 21 | for row in csv_file: 22 | # Get values with regex 23 | row_data = re.findall('(.*)\t(.*)', str(row)) 24 | # And print 25 | print('{}\'s color is {}'.format(row_data[0][0], row_data[0][1])) 26 | -------------------------------------------------------------------------------- /07_Big-Data/README.md: -------------------------------------------------------------------------------- 1 | # 7_ Big Data 2 | 3 | ## 1_ Map Reduc fundamentals 4 | 5 | ## 2_ Hadoop Components 6 | 7 | ## 3_ HDFS 8 | 9 | ## 4_ Data replications Principles 10 | 11 | ## 5_ Setup Hadoop 12 | 13 | ## 6_ Name & data nodes 14 | 15 | ## 7_ Job & task tracker 16 | 17 | ## 8_ M/R programming 18 | 19 | ## 9_ Sqop: Loading data in HDFS 20 | 21 | ## 10_ Flume, Scribe 22 | 23 | ## 11_ SQL with Pig 24 | 25 | ## 12_ DWH with Hive 26 | 27 | ## 13_ Scribe, Chukwa for Weblog 28 | 29 | ## 14_ Using Mahout 30 | 31 | ## 15_ Zookeeper Avro 32 | 33 | ## 16_ Storm: Hadoop Realtime 34 | 35 | ## 17_ Rhadoop, RHIPE 36 | 37 | ## 18_ RMR 38 | 39 | ## 19_ Cassandra 40 | 41 | ## 20_ MongoDB, Neo4j 42 | -------------------------------------------------------------------------------- /01_Fundamentals/1_fundamentals.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | 3 | 4 | import numpy as np 5 | 6 | 7 | # Generate a list of 4 list of 5 random numbers each 8 | list_of_lists = [] 9 | # Loop the action 4 times 10 | for i in range(4): 11 | # Generate a list of 5 numbers between 0 and 100 and add this list to [list_of_lists] 12 | list_of_lists.append(np.random.randint(low = 0, high = 100, size = 5)) 13 | # Convert list_of_lists into numpy matrix 14 | matrix = np.matrix(list_of_lists) 15 | print('Here is your matrix:\n{}\n'.format(matrix)) 16 | 17 | # Addition 18 | new_matrix = np.sum([matrix, 5]) 19 | print('Here is your matrix with addition +5:\n{}\n'.format(new_matrix)) 20 | 21 | # Multiplication 22 | new_matrix = np.multiply(matrix, matrix) 23 | print('Here is your matrix multiplied by itself:\n{}\n'.format(new_matrix)) 24 | 25 | # Transposition 26 | new_matrix = np.transpose(matrix) 27 | print('Here is your matrix transposed:\n{}\n'.format(new_matrix)) 28 | -------------------------------------------------------------------------------- /06_Data-Visualization/1_data-exploration.R: -------------------------------------------------------------------------------- 1 | ##################### 2 | # To execute line by line in Rstudio, select it (hightlight) 3 | # Press Ctrl+Enter 4 | 5 | # Iris is an array of values examples coming with R. 6 | data <- iris 7 | # This is equal to : 8 | data = iris 9 | # To print it: Sepal.Length Sepal.Width Petal.Length Petal.Width Species 10 | show(data) 11 | 12 | # Histogram 13 | column = data[,1] 14 | hist(column) 15 | # Change parameters : 16 | hist(column, main = "Main title", xlab = "SEPAL LENGTH", ylab = "FREQUENCY", col = 'red', breaks = 10) 17 | hist(column, main = "Main title", xlab = "SEPAL LENGTH", ylab = "FREQUENCY", col = 'red', breaks = 15) 18 | 19 | # Box plot 20 | boxplot(column, main = "Main title", ylab = "SEPAL LENGTH", col = 'red') 21 | 22 | # Line chart, not very useful here, indeed 23 | X = data[,1] 24 | Y = data[,3] 25 | plot(x = X, y = Y, main = "Main title", xlab = "SEPAL LENGTH", ylab = "PETAL LENGTH", col = 'red') 26 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # data-scientist-roadmap 2 | 3 | I just found this data science skills roadmap, drew by [Swami Chandrasekaran](http://nirvacana.com/thoughts/becoming-a-data-scientist/) on his cool blog. 4 | 5 | **** 6 | 7 | ![roadmap-picture](http://nirvacana.com/thoughts/wp-content/uploads/2013/07/RoadToDataScientist1.png) 8 | 9 | **** 10 | 11 | Jobs linked to __data science__ are becoming __more and more popular__. A __bunch of tutorials__ could easily complete this roadmap, helping whoever wants to __start learning stuff about data science__. 12 | 13 | For the moment, a lot is __got on wikipedia__ (except for codes, always handmade). Any help's thus welcome! 14 | 15 | ## Rules 16 | 17 | * __Feel free to fork this repository and pull requests__. 18 | * Always comment your code. 19 | * Please respect topology for filenames. 20 | * There's one README for each directory. 21 | * Also, could be great to share useful links or ressources in README files. 22 | -------------------------------------------------------------------------------- /10_Toolbox/README.md: -------------------------------------------------------------------------------- 1 | # 10_ Toolbox 2 | 3 | ## 1_ MS Excel with Analysis toolpack 4 | 5 | ## 2_ Java, Python 6 | 7 | ## 3_ R, Rstudio, Rattle 8 | 9 | ## 4_ Weka, Knime, RapidMiner 10 | 11 | ## 5_ Hadoop dist of choice 12 | 13 | ## 6_ Spark, Storm 14 | 15 | ## 7_ Flume, Scibe, Chukwa 16 | 17 | ## 8_ Nutch, Talend, Scraperwiki 18 | 19 | ## 9_ Webscraper, Flume, Sqoop 20 | 21 | ## 10_ tm, RWeka, NLTK 22 | 23 | ## 11_ RHIPE 24 | 25 | ## 12_ D3.js, ggplot2, Shiny 26 | 27 | ## 13_ IBM Languageware 28 | 29 | ## 14_ Cassandra, MongoDB 30 | 31 | ## 13_ Microsoft Azure, AWS, Google Cloud 32 | 33 | ## 14_ Microsoft Cognitive API 34 | 35 | ## 15_ Tensorflow 36 | 37 | https://www.tensorflow.org/ 38 | 39 | TensorFlow is an open source software library for numerical computation using data flow graphs. 40 | 41 | Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. 42 | 43 | The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. 44 | 45 | TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well. 46 | -------------------------------------------------------------------------------- /04_Machine-Learning/22_perceptron.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # coding: utf8 3 | 4 | 5 | """ IMPORTS """ 6 | import numpy as np 7 | import matplotlib.pyplot as plt 8 | 9 | 10 | 11 | class Perceptron(object): 12 | 13 | 14 | def __init__(self): 15 | 16 | self.l_rate = 0.01 17 | self.n_epoch = 5 18 | self.bias = 0 19 | self.input_data = [[0.2, 0.7], [15.25, 14.37], [0.02, 0.68], [14.55, 16.36], [0.55, 0.36], [0.45, 0.16], [0.45, 0.26], [11.54, 17.226], [12.58, 17.36], [13.95, 15.26]] 20 | self.expected = [0, 1, 0, 1, 0, 0, 0, 1, 1, 1] 21 | 22 | #~ x = [i[0] for i in self.input_data] 23 | #~ y = [i[1] for i in self.input_data] 24 | #~ plt.scatter(x, y) 25 | #~ plt.show() 26 | 27 | 28 | def predict(self, row, weights): 29 | 30 | # Activation threshold function 31 | activation = 0 32 | for i in range(len(row) - 1): 33 | activation += weights[i] * row[i] 34 | activation += self.bias 35 | # Return class 36 | if activation >= 0.0: 37 | return 1.0 38 | else: 39 | return 0.0 40 | 41 | 42 | # Estimate Perceptron weights using stochastic gradient descent 43 | def train_weights(self): 44 | 45 | # Let's initiate weights with small values 46 | weights = list(np.random.uniform(low = 0, high = 0.1, size = 2)) 47 | global_errors = [] 48 | for epoch in range(self.n_epoch): 49 | # Keep track of errors for plotting 50 | epoch_errors = 0.0 51 | for row, expected in zip(self.input_data, self.expected): 52 | prediction = self.predict(row, weights) 53 | error = expected - prediction 54 | # We keep a track of global errors for plotting 55 | global_errors.append(abs(error)) 56 | epoch_errors += abs(error) 57 | # If predicted and expected are different, update weights and bias 58 | if expected != prediction: 59 | self.bias = self.bias + self.l_rate * error 60 | for i in range(len(row)-1): 61 | weights[i] = weights[i] + self.l_rate * error * row[i] 62 | print('epoch: {}, lrate: {}, errors: {}'.format(epoch, self.l_rate, epoch_errors)) 63 | 64 | plt.plot(global_errors) 65 | plt.ylim(-1, 2) 66 | plt.show() 67 | 68 | return weights 69 | 70 | 71 | 72 | 73 | 74 | 75 | if __name__ == '__main__': 76 | # Calculate weights 77 | 78 | neuron = Perceptron() 79 | 80 | weights = neuron.train_weights() 81 | 82 | print(neuron.predict([8.23, 9.45], weights)) 83 | print(neuron.predict([0.23, 1.45], weights)) 84 | 85 | 86 | 87 | 88 | 89 | 90 | -------------------------------------------------------------------------------- /02_Statistics/README.md: -------------------------------------------------------------------------------- 1 | # 2_ Statistics 2 | 3 | 4 | [Statistics-101 for data noobs](https://medium.com/@debuggermalhotra/statistics-101-for-data-noobs-2e2a0e23a5dc) 5 | 6 | ## 1_ Pick a dataset 7 | 8 | ### Datasets repositories 9 | 10 | #### Generalists 11 | 12 | - [KAGGLE](https://www.kaggle.com/datasets) 13 | - [Google](https://toolbox.google.com/datasetsearch) 14 | 15 | #### Medical 16 | 17 | - [PMC](https://www.ncbi.nlm.nih.gov/pmc/) 18 | 19 | #### Other languages 20 | 21 | ##### French 22 | 23 | - [DATAGOUV](https://www.data.gouv.fr/fr/) 24 | 25 | ## 2_ Descriptive statistics 26 | 27 | ### Mean 28 | 29 | In probability and statistics, population mean and expected value are used synonymously to refer to one __measure of the central tendency either of a probability distribution or of the random variable__ characterized by that distribution. 30 | 31 | For a data set, the terms arithmetic mean, mathematical expectation, and sometimes average are used synonymously to refer to a central value of a discrete set of numbers: specifically, the __sum of the values divided by the number of values__. 32 | 33 | ![mean_formula](https://wikimedia.org/api/rest_v1/media/math/render/svg/bd2f5fb530fc192e4db7a315777f5bbb5d462c90) 34 | 35 | ### Median 36 | 37 | The median is the value __separating the higher half of a data sample, a population, or a probability distribution, from the lower half__. In simple terms, it may be thought of as the "middle" value of a data set. 38 | 39 | ### Descriptive statistics in Python 40 | 41 | [Numpy](http://www.numpy.org/) is a python library widely used for statistical analysis. 42 | 43 | #### Installation 44 | 45 | sudo pip3 install numpy 46 | 47 | #### Utilization 48 | 49 | import numpy 50 | 51 | ## 3_ Exploratory data analysis 52 | 53 | ## 4_ Histograms 54 | 55 | ## 5_ Percentiles & outliers 56 | 57 | ## 6_ Probability theory 58 | 59 | ## 7_ Bayes theorem 60 | 61 | ## 8_ Random variables 62 | 63 | ## 9_ Cumul Dist Fn (CDF) 64 | 65 | ## 10_ Continuous distributions 66 | 67 | ## 11_ Skewness 68 | 69 | ## 12_ ANOVA 70 | 71 | ## 13_ Prob Den Fn (PDF) 72 | 73 | ## 14_ Central Limit theorem 74 | 75 | ## 15_ Monte Carlo method 76 | 77 | ## 16_ Hypothesis Testing 78 | 79 | ## 17_ p-Value 80 | 81 | ## 18_ Chi2 test 82 | 83 | ## 19_ Estimation 84 | 85 | ## 20_ Confid Int (CI) 86 | 87 | ## 21_ MLE 88 | 89 | ## 22_ Kernel Density estimate 90 | 91 | ## 23_ Regression 92 | 93 | ## 24_ Covariance 94 | 95 | ## 25_ Correlation 96 | 97 | ## 26_ Pearson coeff 98 | 99 | ## 27_ Causation 100 | 101 | ## 28_ Least2-fit 102 | 103 | ## 29_ Euclidian Distance 104 | -------------------------------------------------------------------------------- /04_Machine-Learning/README.md: -------------------------------------------------------------------------------- 1 | # 4_ Machine learning 2 | 3 | ## 1_ What is ML ? 4 | 5 | ### Definition 6 | 7 | Machine Learning is part of the Artificial Intelligences study. It concerns the conception, devloppement and implementation of sophisticated methods, allowing a machine to achieve really hard tasks, nearly impossible to solve with classic algorithms. 8 | 9 | ### Utilisation examples 10 | 11 | * Computer vision ([Definition](http://www.bmva.org/visionoverview)) 12 | * Search engines 13 | * Financial analysis 14 | * Documents classification 15 | * Music generation 16 | * Robotics ... 17 | 18 | ## 2_ Numerical var 19 | 20 | ## 3_ Categorical var 21 | 22 | ## 4_ Supervised learning 23 | 24 | Supervised learning is the machine learning task of inferring a function from __labeled training data__. 25 | 26 | The training data consist of a __set of training examples__. 27 | 28 | In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). 29 | 30 | A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. 31 | 32 | __An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances__. 33 | 34 | ## 5_ Unsupervised learning 35 | 36 | Unsupervised machine learning is the machine learning task of inferring a function to describe hidden structure __from "unlabeled" data__ (a classification or categorization is not included in the observations). 37 | 38 | Since the examples given to the learner are unlabeled, there is no evaluation of the accuracy of the structure that is output by the relevant algorithm—which is one way of distinguishing unsupervised learning from supervised learning and reinforcement learning. 39 | 40 | ## 6_ Concepts, inputs and attributes 41 | 42 | ## 7_ Training and test data 43 | 44 | ## 8_ Classifiers 45 | 46 | ## 9_ Prediction 47 | 48 | ## 10_ Lift 49 | 50 | ## 11_ Overfitting 51 | 52 | ## 12_ Bias & variance 53 | 54 | ## 13_ Tree and classification 55 | 56 | ## 14_ Classification rate 57 | 58 | ## 15_ Decision tree 59 | 60 | ## 16_ Boosting 61 | 62 | ## 17_ Naïves Bayes classifiers 63 | 64 | ## 18_ K-Nearest neighbor 65 | 66 | ## 19_ Logistic regression 67 | 68 | ## 20_ Ranking 69 | 70 | ## 21_ Linear regression 71 | 72 | ## 22_ Perceptron 73 | 74 | The perceptron has been the first model described in the 50ies. 75 | 76 | This is a __binary classifier__, ie it can't separate more than 2 groups, and thoses groups have to be __linearly separable__. 77 | 78 | The perceptron __works like a biological neuron__. It calculate an activation value, and if this value if positive, it returns 1, 0 otherwise. 79 | 80 | ## 23_ Hierarchical clustering 81 | 82 | ## 24_ K-means clustering 83 | 84 | ## 25_ Neural networks 85 | 86 | ## 26_ Sentiment analysis 87 | 88 | ## 27_ Collaborative filtering 89 | 90 | ## 28_ Tagging 91 | -------------------------------------------------------------------------------- /03_Programming/README.md: -------------------------------------------------------------------------------- 1 | # 3_ Programming 2 | 3 | ## 1_ Python Basics 4 | 5 | ### About 6 | 7 | Python is a high-level programming langage. I can be used in a wide range of works. 8 | 9 | Commonly used in data-science, [Python](https://www.python.org/) has a huge set of libraries, helpful to quickly do something. 10 | 11 | Most of informatics systems already support Python, without installing anything. 12 | 13 | ### Execute a script 14 | 15 | * Download the .py file on your computer 16 | * Make it executable (_chmod +x file.py_ on Linux) 17 | * Open a terminal and go to the directory containing the python file 18 | * _python file.py_ to run with Python2 or _python3 file.py_ with Python3 19 | 20 | ## 2_ Working in excel 21 | 22 | ## 3_ R setup / R studio 23 | 24 | ### About 25 | 26 | R is a programming language specialized in statistics and mathematical visualizations. 27 | 28 | I can be used withs manually created scripts launched in the terminal, or directly in the R console. 29 | 30 | ### Installation 31 | 32 | #### Linux 33 | 34 | sudo apt-get install r-base 35 | 36 | sudo apt-get install r-base-dev 37 | 38 | #### Windows 39 | 40 | Download the .exe setup available on [CRAN](https://cran.rstudio.com/bin/windows/base/) website. 41 | 42 | ### R-studio 43 | 44 | Rstudio is a graphical interface for R. It is available for free on [their website](https://www.rstudio.com/products/rstudio/download/). 45 | 46 | This interface is divided in 4 main areas : 47 | 48 | ![rstudio](https://owi.usgs.gov/R/training-curriculum/intro-curriculum/static/img/rstudio.png) 49 | 50 | * The top left is the script you are working on (highlight code you want to execute and press Ctrl + Enter) 51 | * The bottom left is the console to instant-execute some lines of codes 52 | * The top right is showing your environment (variables, history, ...) 53 | * The bottom right show figures you plotted, packages, help ... The result of code execution 54 | 55 | ## 4_ R basics 56 | 57 | R is an open source programming language and software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing. 58 | 59 | The R language is widely used among statisticians and data miners for developing statistical software and data analysis. 60 | 61 | Polls, surveys of data miners, and studies of scholarly literature databases show that R's popularity has increased substantially in recent years. 62 | 63 | ## 5_ Expressions 64 | 65 | ## 6_ Variables 66 | 67 | ## 7_ IBM SPSS 68 | 69 | ## 8_ Rapid Miner 70 | 71 | ## 9_ Vectors 72 | 73 | ## 10_ Matrices 74 | 75 | ## 11_ Arrays 76 | 77 | ## 12_ Factors 78 | 79 | ## 13_ Lists 80 | 81 | ## 14_ Data frames 82 | 83 | ## 15_ Reading CSV data 84 | 85 | CSV is a format of __tabular data__ comonly used in data science. Most of structured data will come in such a format. 86 | 87 | To __open a CSV file__ in Python, just open the file as usual : 88 | 89 | raw_file = open('file.csv', 'r') 90 | 91 | * 'r': Reading, no modification on the file is possible 92 | * 'w': Writing, every modification will erease the file 93 | * 'a': Adding, every modification will be made at the end of the file 94 | 95 | ### How to read it ? 96 | 97 | Most of the time, you will parse this file line by line and do whatever you want on this line. If you want to store data to use them later, build lists or dictionnaries. 98 | 99 | To read such a file row by row, you can use : 100 | 101 | * Python [library csv](https://docs.python.org/3/library/csv.html) 102 | * Python [function open](https://docs.python.org/2/library/functions.html#open) 103 | 104 | ## 16_ Reading raw data 105 | 106 | ## 17_ Subsetting data 107 | 108 | ## 18_ Manipulate data frames 109 | 110 | ## 19_ Functions 111 | 112 | A function is helpful to execute redondant actions. 113 | 114 | First, define the function: 115 | 116 | def MyFunction(number): 117 | """This function will multiply a number by 9""" 118 | number = number * 9 119 | return number 120 | 121 | ## 20_ Factor analysis 122 | 123 | ## 21_ Install PKGS 124 | 125 | Python actually has two mainly used distributions. Python2 and python3. 126 | 127 | ### Install pip 128 | 129 | Pip is a library manager for Python. Thus, you can easily install most of the packages with a one-line command. To install pip, just go to a terminal and do: 130 | 131 | # __python2__ 132 | sudo apt-get install python-pip 133 | # __python3__ 134 | sudo apt-get install python3-pip 135 | 136 | You can then install a library with [pip](https://pypi.python.org/pypi/pip?) via a terminal doing: 137 | 138 | # __python2__ 139 | sudo pip install [PCKG_NAME] 140 | # __python3__ 141 | sudo pip3 install [PCKG_NAME] 142 | 143 | You also can install it directly from the core (see 21_install_pkgs.py) 144 | -------------------------------------------------------------------------------- /06_Data-Visualization/README.md: -------------------------------------------------------------------------------- 1 | # 6_ Data Visualization 2 | 3 | Open .R scripts in Rstudio for line-by-line execution. 4 | 5 | See [10_ Toolbox/3_ R, Rstudio, Rattle](https://github.com/MrMimic/data-scientist-roadmap/tree/master/10_Toolbox#3_-r-rstudio-rattle) for installation. 6 | 7 | ## 1_ Data exploration in R 8 | 9 | In mathematics, the graph of a function f is the collection of all ordered pairs (x, f(x)). If the function input x is a scalar, the graph is a two-dimensional graph, and for a continuous function is a curve. If the function input x is an ordered pair (x1, x2) of real numbers, the graph is the collection of all ordered triples (x1, x2, f(x1, x2)), and for a continuous function is a surface. 10 | 11 | ## 2_ Uni, bi and multivariate viz 12 | 13 | ### Univariate 14 | 15 | The term is commonly used in statistics to distinguish a distribution of one variable from a distribution of several variables, although it can be applied in other ways as well. For example, univariate data are composed of a single scalar component. In time series analysis, the term is applied with a whole time series as the object referred to: thus a univariate time series refers to the set of values over time of a single quantity. 16 | 17 | ### Bivariate 18 | 19 | Bivariate analysis is one of the simplest forms of quantitative (statistical) analysis.[1] It involves the analysis of two variables (often denoted as X, Y), for the purpose of determining the empirical relationship between them. 20 | 21 | ### Multivariate 22 | 23 | Multivariate analysis (MVA) is based on the statistical principle of multivariate statistics, which involves observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. 24 | 25 | ## 3_ ggplot2 26 | 27 | ### About 28 | 29 | ggplot2 is a plotting system for R, based on the grammar of graphics, which tries to take the good parts of base and lattice graphics and none of the bad parts. It takes care of many of the fiddly details that make plotting a hassle (like drawing legends) as well as providing a powerful model of graphics that makes it easy to produce complex multi-layered graphics. 30 | 31 | [http://ggplot2.org/](http://ggplot2.org/) 32 | 33 | ### Documentation 34 | 35 | ### Examples 36 | 37 | [http://r4stats.com/examples/graphics-ggplot2/](http://r4stats.com/examples/graphics-ggplot2/) 38 | 39 | ## 4_ Histogram and pie (Uni) 40 | 41 | ### About 42 | 43 | Histograms and pie are 2 types of graphes used to visualize frequencies. 44 | 45 | Histogram is showing the distribution of these frequencies over classes, and pie the relative proportion of this frequencies in a 100% circle. 46 | 47 | ## 5_ Tree & tree map 48 | 49 | ### About 50 | 51 | [Treemaps](https://en.wikipedia.org/wiki/Treemapping) display hierarchical (tree-structured) data as a set of nested rectangles. 52 | Each branch of the tree is given a rectangle, which is then tiled with smaller rectangles representing sub-branches. 53 | A leaf node’s rectangle has an area proportional to a specified dimension of the data. 54 | Often the leaf nodes are colored to show a separate dimension of the data. 55 | 56 | ### When to use it ? 57 | 58 | - Less than 10 branches. 59 | - Positive values. 60 | - Space for visualisation is limited. 61 | 62 | ### Example 63 | 64 | ![treemap-example](https://jingwen-z.github.io/images/20181030-treemap.png) 65 | 66 | This treemap describes volume for each product universe with corresponding surface. Liquid products are more sold than others. 67 | If you want to explore more, we can go into products “liquid” and find which shelves are prefered by clients. 68 | 69 | ### More information 70 | 71 | [Matplotlib Series 5: Treemap](https://jingwen-z.github.io/data-viz-with-matplotlib-series5-treemap/) 72 | 73 | ## 6_ Scatter plot 74 | 75 | ### About 76 | 77 | A [scatter plot](https://en.wikipedia.org/wiki/Scatter_plot) (also called a scatter graph, scatter chart, scattergram, or scatter diagram) is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data. 78 | 79 | ### When to use it ? 80 | 81 | Scatter plots are used when you want to show the relationship between two variables. 82 | Scatter plots are sometimes called correlation plots because they show how two variables are correlated. 83 | 84 | ### Example 85 | 86 | ![scatter-plot-example](https://jingwen-z.github.io/images/20181025-pos-scatter-plot.png) 87 | 88 | This plot describes the positive relation between store’s surface and its turnover(k euros), which is reasonable: for stores, the larger it is, more clients it can accept, more turnover it will generate. 89 | 90 | ### More information 91 | 92 | [Matplotlib Series 4: Scatter plot](https://jingwen-z.github.io/data-viz-with-matplotlib-series4-scatter-plot/) 93 | 94 | ## 7_ Line chart 95 | 96 | ### About 97 | 98 | A [line chart](https://en.wikipedia.org/wiki/Line_chart) or line graph is a type of chart which displays information as a series of data points called ‘markers’ connected by straight line segments. A line chart is often used to visualize a trend in data over intervals of time – a time series – thus the line is often drawn chronologically. 99 | 100 | ### When to use it ? 101 | 102 | - Track changes over time. 103 | - X-axis displays continuous variables. 104 | - Y-axis displays measurement. 105 | 106 | ### Example 107 | 108 | ![line-chart-example](https://jingwen-z.github.io/images/20180916-line-chart.png) 109 | 110 | Suppose that the plot above describes the turnover(k euros) of ice-cream’s sales during one year. 111 | According to the plot, we can clearly find that the sales reach a peak in summer, then fall from autumn to winter, which is logical. 112 | 113 | ### More information 114 | 115 | [Matplotlib Series 2: Line chart](https://jingwen-z.github.io/data-viz-with-matplotlib-series2-line-chart/) 116 | 117 | ## 8_ Spatial charts 118 | 119 | ## 9_ Survey plot 120 | 121 | ## 10_ Timeline 122 | 123 | ## 11_ Decision tree 124 | 125 | ## 12_ D3.js 126 | 127 | ### About 128 | 129 | This is a JavaScript library, allowing you to create a huge number of different figure easily. 130 | 131 | https://d3js.org/ 132 | 133 | D3.js is a JavaScript library for manipulating documents based on data. 134 | D3 helps you bring data to life using HTML, SVG, and CSS. 135 | D3’s emphasis on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework, combining powerful visualization components and a data-driven approach to DOM manipulation. 136 | 137 | ### Examples 138 | 139 | There is many examples of chars using D3.js on [D3's Github](https://github.com/d3/d3/wiki/Gallery). 140 | 141 | ## 13_ InfoVis 142 | 143 | ## 14_ IBM ManyEyes 144 | 145 | ## 15_ Tableau 146 | 147 | ## 16_ Venn diagram 148 | 149 | ### About 150 | 151 | A [venn diagram](https://en.wikipedia.org/wiki/Venn_diagram) (also called primary diagram, set diagram or logic diagram) is a diagram that shows all possible logical relations between a finite collection of different sets. 152 | 153 | ### When to use it ? 154 | 155 | Show logical relations between different groups (intersection, difference, union). 156 | 157 | ### Example 158 | 159 | ![venn-diagram-example](https://jingwen-z.github.io/images/20181106-venn2.png) 160 | 161 | This kind of venn diagram can usually be used in retail trading. 162 | Assuming that we need to study the popularity of cheese and red wine, and 2500 clients answered our questionnaire. 163 | According to the diagram above, we find that among 2500 clients, 900 clients(36%) prefer cheese, 1200 clients(48%) prefer red wine, and 400 clients(16%) favor both product. 164 | 165 | ### More information 166 | 167 | [Matplotlib Series 6: Venn diagram](https://jingwen-z.github.io/data-viz-with-matplotlib-series6-venn-diagram/) 168 | 169 | ## 17_ Area chart 170 | 171 | ### About 172 | 173 | An [area chart](https://en.wikipedia.org/wiki/Area_chart) or area graph displays graphically quantitative data. 174 | It is based on the line chart. The area between axis and line are commonly emphasized with colors, textures and hatchings. 175 | 176 | ### When to use it ? 177 | 178 | Show or compare a quantitative progression over time. 179 | 180 | ### Example 181 | 182 | ![area-chart-example](https://jingwen-z.github.io/images/20181114-stacked-area-chart.png) 183 | 184 | This stacked area chart displays the amounts’ changes in each account, their contribution to total amount (in term of value) as well. 185 | 186 | ### More information 187 | 188 | [Matplotlib Series 7: Area chart](https://jingwen-z.github.io/data-viz-with-matplotlib-series7-area-chart/) 189 | 190 | ## 18_ Radar chart 191 | 192 | ### About 193 | 194 | The [radar chart](https://en.wikipedia.org/wiki/Radar_chart) is a chart and/or plot that consists of a sequence of equi-angular spokes, called radii, with each spoke representing one of the variables. The data length of a spoke is proportional to the magnitude of the variable for the data point relative to the maximum magnitude of the variable across all data points. A line is drawn connecting the data values for each spoke. This gives the plot a star-like appearance and the origin of one of the popular names for this plot. 195 | 196 | ### When to use it ? 197 | 198 | - Comparing two or more items or groups on various features or characteristics. 199 | - Examining the relative values for a single data point. 200 | - Displaying less than ten factors on one radar chart. 201 | 202 | ### Example 203 | 204 | ![radar-chart-example](https://jingwen-z.github.io/images/20181121-multi-radar-chart.png) 205 | 206 | This radar chart displays the preference of 2 clients among 4. 207 | Client c1 favors chicken and bread, and doesn’t like cheese that much. 208 | Nevertheless, client c2 prefers cheese to other 4 products and doesn’t like beer. 209 | We can have an interview with these 2 clients, in order to find the weakness of products which are out of preference. 210 | 211 | ### More information 212 | 213 | [Matplotlib Series 8: Radar chart](https://jingwen-z.github.io/data-viz-with-matplotlib-series8-radar-chart/) 214 | 215 | ## 19_ Word cloud 216 | 217 | ### About 218 | 219 | A [word cloud](https://en.wikipedia.org/wiki/Tag_cloud) (tag cloud, or weighted list in visual design) is a novelty visual representation of text data. Tags are usually single words, and the importance of each tag is shown with font size or color. This format is useful for quickly perceiving the most prominent terms and for locating a term alphabetically to determine its relative prominence. 220 | 221 | ### When to use it ? 222 | 223 | - Depicting keyword metadata (tags) on websites. 224 | - Delighting and provide emotional connection. 225 | 226 | ### Example 227 | 228 | ![word-cloud-example](https://jingwen-z.github.io/images/20181127-basic-word-cloud.png) 229 | 230 | According to this word cloud, we can globally know that data science employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science. It can be used for business analysis, and called “The Sexiest Job of the 21st Century”. 231 | 232 | ### More information 233 | 234 | [Matplotlib Series 9: Word cloud](https://jingwen-z.github.io/data-viz-with-matplotlib-series9-word-cloud/) 235 | -------------------------------------------------------------------------------- /01_Fundamentals/README.md: -------------------------------------------------------------------------------- 1 | # 1_ Fundamentals 2 | 3 | 4 | ## 1_ Matrices & Algebra fundamentals 5 | 6 | ### About 7 | 8 | In mathematics, a matrix is a __rectangular array of numbers, symbols, or expressions, arranged in rows and columns__. A matrix could be reduced as a submatrix of a matrix by deleting any collection of rows and/or columns. 9 | 10 | ![matrix-image](https://upload.wikimedia.org/wikipedia/commons/b/bb/Matrix.svg) 11 | 12 | ### Operations 13 | 14 | There are a number of basic operations that can be applied to modify matrices: 15 | 16 | * [Addition](https://en.wikipedia.org/wiki/Matrix_addition) 17 | * [Scalar Multiplication](https://en.wikipedia.org/wiki/Scalar_multiplication) 18 | * [Transposition](https://en.wikipedia.org/wiki/Transpose) 19 | * [Multiplication](https://en.wikipedia.org/wiki/Matrix_multiplication) 20 | 21 | 22 | ## 2_ Hash function, binary tree, O(n) 23 | 24 | ### Hash function 25 | 26 | #### Definition 27 | 28 | A hash function is __any function that can be used to map data of arbitrary size to data of fixed size__. One use is a data structure called a hash table, widely used in computer software for rapid data lookup. Hash functions accelerate table or database lookup by detecting duplicated records in a large file. 29 | 30 | ![hash-image](https://upload.wikimedia.org/wikipedia/commons/5/58/Hash_table_4_1_1_0_0_1_0_LL.svg) 31 | 32 | ### Binary tree 33 | 34 | #### Definition 35 | 36 | In computer science, a binary tree is __a tree data structure in which each node has at most two children__, which are referred to as the left child and the right child. 37 | 38 | ![binary-tree-image](https://upload.wikimedia.org/wikipedia/commons/f/f7/Binary_tree.svg) 39 | 40 | ### O(n) 41 | 42 | #### Definition 43 | 44 | In computer science, big O notation is used to __classify algorithms according to how their running time or space requirements grow as the input size grows__. In analytic number theory, big O notation is often used to __express a bound on the difference between an arithmetical function and a better understood approximation__. 45 | 46 | ## 3_ Relational algebra, DB basics 47 | 48 | ### Definition 49 | 50 | Relational algebra is a family of algebras with a __well-founded semantics used for modelling the data stored in relational databases__, and defining queries on it. 51 | 52 | The main application of relational algebra is providing a theoretical foundation for __relational databases__, particularly query languages for such databases, chief among which is SQL. 53 | 54 | ### Natural join 55 | 56 | #### About 57 | 58 | In SQL language, a natural junction between two tables will be done if : 59 | 60 | * At least one column has the same name in both tables 61 | * Theses two columns have the same data type 62 | * CHAR (character) 63 | * INT (integer) 64 | * FLOAT (floating point numeric data) 65 | * VARCHAR (long character chain) 66 | 67 | #### mySQL request 68 | 69 | SELECT 70 | FROM 71 | NATURAL JOIN 72 | 73 | SELECT 74 | FROM , 75 | WHERE TABLE_1.ID = TABLE_2.ID 76 | 77 | ## 4_ Inner, Outer, Cross, theta-join 78 | 79 | ### Inner join 80 | 81 | The INNER JOIN keyword selects records that have matching values in both tables. 82 | 83 | #### Request 84 | 85 | SELECT column_name(s) 86 | FROM table1 87 | INNER JOIN table2 ON table1.column_name = table2.column_name; 88 | 89 | ![inner-join-image](https://www.w3schools.com/sql/img_innerjoin.gif) 90 | 91 | ### Outer join 92 | 93 | The FULL OUTER JOIN keyword return all records when there is a match in either left (table1) or right (table2) table records. 94 | 95 | #### Request 96 | 97 | SELECT column_name(s) 98 | FROM table1 99 | FULL OUTER JOIN table2 ON table1.column_name = table2.column_name; 100 | 101 | ![outer-join-image](https://www.w3schools.com/sql/img_fulljoin.gif) 102 | 103 | ### Left join 104 | 105 | The LEFT JOIN keyword returns all records from the left table (table1), and the matched records from the right table (table2). The result is NULL from the right side, if there is no match. 106 | 107 | #### Request 108 | 109 | SELECT column_name(s) 110 | FROM table1 111 | LEFT JOIN table2 ON table1.column_name = table2.column_name; 112 | 113 | ![left-join-image](https://www.w3schools.com/sql/img_leftjoin.gif) 114 | 115 | ### Right join 116 | 117 | The RIGHT JOIN keyword returns all records from the right table (table2), and the matched records from the left table (table1). The result is NULL from the left side, when there is no match. 118 | #### Request 119 | 120 | SELECT column_name(s) 121 | FROM table1 122 | RIGHT JOIN table2 ON table1.column_name = table2.column_name; 123 | 124 | ![left-join-image](https://www.w3schools.com/sql/img_rightjoin.gif) 125 | 126 | ## 5_ CAP theorem 127 | 128 | It is impossible for a distributed data store to simultaneously provide more than two out of the following three guarantees: 129 | 130 | * Every read receives the most recent write or an error. 131 | * Every request receives a (non-error) response – without guarantee that it contains the most recent write. 132 | * The system continues to operate despite an arbitrary number of messages being dropped (or delayed) by the network between nodes. 133 | 134 | In other words, the CAP Theorem states that in the presence of a network partition, one has to choose between consistency and availability. Note that consistency as defined in the CAP Theorem is quite different from the consistency guaranteed in ACID database transactions. 135 | 136 | ## 6_ Tabular data 137 | 138 | Tabular data are __opposed to relational__ data, like SQL database. 139 | 140 | In tabular data, __everything is arranged in columns and rows__. Every row have the same number of column (except for missing value, which could be substituted by "N/A". 141 | 142 | The __first line__ of tabular data is most of the time a __header__, describing the content of each column. 143 | 144 | The most used format of tabular data in data science is __CSV___. Every column is surrounded by a character (a tabulation, a coma ..), delimiting this column from its two neighbours. 145 | 146 | ## 7_ Entropy 147 | 148 | Entropy is a __measure of uncertainty__. High entropy means the data has high variance and thus contains a lot of information and/or noise. 149 | 150 | For instance, __a constant function where f(x) = 4 for all x has no entropy and is easily predictable__, has little information, has no noise and can be succinctly represented . Similarly, f(x) = ~4 has some entropy while f(x) = random number is very high entropy due to noise. 151 | 152 | ## 8_ Data frames & series 153 | 154 | A data frame is used for storing data tables. It is a list of vectors of equal length. 155 | 156 | A series is a series of data points ordered. 157 | 158 | ## 9_ Sharding 159 | 160 | *Sharding* is **horizontal(row wise) database partitioning** as opposed to **vertical(column wise) partitioning** which is *Normalization* 161 | 162 | Why use Sharding? 163 | 164 | 1. Database systems with large data sets or high throughput applications can challenge the capacity of a single server. 165 | 2. Two methods to address the growth : Vertical Scaling and Horizontal Scaling 166 | 3. Vertical Scaling 167 | 168 | * Involves increasing the capacity of a single server 169 | * But due to technological and economical restrictions, a single machine may not be sufficient for the given workload. 170 | 171 | 4. Horizontal Scaling 172 | * Involves dividing the dataset and load over multiple servers, adding additional servers to increase capacity as required 173 | * While the overall speed or capacity of a single machine may not be high, each machine handles a subset of the overall workload, potentially providing better efficiency than a single high-speed high-capacity server. 174 | * Idea is to use concepts of Distributed systems to achieve scale 175 | * But it comes with same tradeoffs of increased complexity that comes hand in hand with distributed systems. 176 | * Many Database systems provide Horizontal scaling via Sharding the datasets. 177 | 178 | ## 10_ OLAP 179 | 180 | Online analytical processing, or OLAP, is an approach to answering multi-dimensional analytical (MDA) queries swiftly in computing. 181 | 182 | OLAP is part of the __broader category of business intelligence__, which also encompasses relational database, report writing and data mining. Typical applications of OLAP include ___business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications coming up, such as agriculture__. 183 | 184 | The term OLAP was created as a slight modification of the traditional database term online transaction processing (OLTP). 185 | 186 | ## 11_ Multidimensional Data model 187 | 188 | ## 12_ ETL 189 | 190 | * Extract 191 | * extracting the data from the multiple heterogenous source system(s) 192 | * data validation to confirm whether the data pulled has the correct/expected values in a given domain 193 | 194 | * Transform 195 | * extracted data is fed into a pipeline which applies multiple functions on top of data 196 | * these functions intend to convert the data into the format which is accepted by the end system 197 | * involves cleaning the data to remove noise, anamolies and redudant data 198 | * Load 199 | * loads the transformed data into the end target 200 | 201 | ## 13_ Reporting vs BI vs Analytics 202 | 203 | ## 14_ JSON and XML 204 | 205 | ### JSON 206 | 207 | JSON is a language-independent data format. Example describing a person: 208 | 209 | { 210 | "firstName": "John", 211 | "lastName": "Smith", 212 | "isAlive": true, 213 | "age": 25, 214 | "address": { 215 | "streetAddress": "21 2nd Street", 216 | "city": "New York", 217 | "state": "NY", 218 | "postalCode": "10021-3100" 219 | }, 220 | "phoneNumbers": [ 221 | { 222 | "type": "home", 223 | "number": "212 555-1234" 224 | }, 225 | { 226 | "type": "office", 227 | "number": "646 555-4567" 228 | }, 229 | { 230 | "type": "mobile", 231 | "number": "123 456-7890" 232 | } 233 | ], 234 | "children": [], 235 | "spouse": null 236 | } 237 | 238 | ## XML 239 | 240 | Extensible Markup Language (XML) is a markup language that defines a set of rules for encoding documents in a format that is both human-readable and machine-readable. 241 | 242 | 243 | 244 | Bloodroot 245 | Sanguinaria canadensis 246 | 4 247 | Mostly Shady 248 | $2.44 249 | 031599 250 | 251 | 252 | Columbine 253 | Aquilegia canadensis 254 | 3 255 | Mostly Shady 256 | $9.37 257 | 030699 258 | 259 | 260 | Marsh Marigold 261 | Caltha palustris 262 | 4 263 | Mostly Sunny 264 | $6.81 265 | 051799 266 | 267 | 268 | 269 | ## 15_ NoSQL 270 | 271 | noSQL is oppsed to relationnal databases (stand for __N__ot __O__nly __SQL__). Data are not structured and there's no notion of keys between tables. 272 | 273 | Any kind of data can be stored in a noSQL database (JSON, CSV, ...) whithout thinking about a complex relationnal scheme. 274 | 275 | __Commonly used noSQL stacks__: Cassandra, MongoDB, Redis, Oracle noSQL ... 276 | 277 | ## 16_ Regex 278 | 279 | ### About 280 | 281 | __Reg__ ular __ex__ pressions (__regex__) are commonly used in informatics. 282 | 283 | It can be used in a wide range of possibilities : 284 | * Text replacing 285 | * Extract information in a text (email, phone number, etc) 286 | * List files with the .txt extension .. 287 | 288 | http://regexr.com/ is a good website for experimenting on Regex. 289 | 290 | ### Utilisation 291 | 292 | To use them in [Python](https://docs.python.org/3/library/re.html), just import: 293 | 294 | import re 295 | 296 | ## 17_ Vendor landscape 297 | 298 | ## 18_ Env Setup 299 | 300 | 301 | -------------------------------------------------------------------------------- /LICENCE.txt: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------