└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # For Data Science Beginners 2 | 3 | Set of Notes with links to help those who are Data Science Beginners. Feel free to raise a PR if you need to! 4 | 5 | **Blogs to Follow:** 6 | * [Data Science Central](http://www.datasciencecentral.com/) 7 | * [KDNuggets](http://www.kdnuggets.com/) 8 | * [Analytics Vidhya](https://www.analyticsvidhya.com/) 9 | * [Data Science Plus](https://datascienceplus.com/) 10 | * [R-Bloggers](https://www.r-bloggers.com/) 11 | * [What are the best Blogs for Data Scientists to Read - Quora](https://www.quora.com/What-are-the-best-blogs-for-data-scientists-to-read) 12 | * [Cognitive class](https://cognitiveclass.ai/blog/) 13 | * [Simply Statistics](https://simplystatistics.org/) 14 | * [Edwin Chens blog](http://blog.echen.me/) 15 | * [Deepmind Blog](https://deepmind.com/blog/) 16 | * [Dataversity Blog](http://www.dataversity.net/category/blogs/) 17 | * [Yhat Blog](http://blog.yhat.com/) 18 | * [Visualization with R](http://socviz.co/) 19 | * [DataSchool](https://www.dataschool.io/) 20 | 21 | **To Learn:** 22 | * [ADVICE FOR ASPIRING DATA SCIENTISTS AND OTHER FAQS](https://yanirseroussi.com/2017/10/15/advice-for-aspiring-data-scientists-and-other-faqs/) 23 | * [How to get your first job in Data Science? Tomi Mester Medium](https://medium.com/@datalab/how-to-break-into-the-data-science-market-f0e0b79b42f7) 24 | * [The most comprehensive Data Science learning plan for 2017](https://www.analyticsvidhya.com/blog/2017/01/the-most-comprehensive-data-science-learning-plan-for-2017/) 25 | * [LeaRning Path on R - Step by Step Guide to Learn Data Science on R](https://www.analyticsvidhya.com/learning-paths-data-science-business-analytics-business-intelligence-big-data/learning-path-r-data-science/) 26 | * [Python Training \| Python For Data Science \| Learn Python](https://www.analyticsvidhya.com/learning-paths-data-science-business-analytics-business-intelligence-big-data/learning-path-data-science-python/) 27 | * [Infographic - Quick Guide to learn Python for Data Science](https://www.analyticsvidhya.com/blog/2015/05/infographic-quick-guide-learn-python-data-science/) 28 | * [Minimalistic Learning Path to Become a Data Scientist](https://hackernoon.com/minimalistic-learning-path-to-become-a-data-scientist-c0a4f614bd09) 29 | * [SQL for Data Analysis (Udacity)](https://classroom.udacity.com/courses/ud198) 30 | * [Recommended Python learning resources - fast.ai](https://forums.fast.ai/t/recommended-python-learning-resources/26888) 31 | + [Here are 450 Ivy League courses you can take online right now for free](https://www.freecodecamp.org/news/ivy-league-free-online-courses-a0d7ae675869/) 32 | 33 | **Videos:** 34 | * [Practical Deep Learning Course for Coders](https://course.fast.ai/) 35 | * [Introduction to R for Data Science \| edX](https://www.edx.org/course/introduction-r-data-science-microsoft-dat204x-2) 36 | * [Data Science \| Coursera](https://www.coursera.org/specializations/jhu-data-science) 37 | * [Aspiring Data Scientist! Here are 8 free online courses to start](https://medium.com/data36/wannabe-data-scientist-here-are-10-free-online-courses-to-start-693c4e230059) 38 | * [Statistics and Data Science \| Stanford](https://www.youtube.com/playlist?list=PLoROMvodv4rO5jY6RA1eFVcLVY2kJU_EL) 39 | 40 | **Github:** 41 | * [GitHub - Developer-Y/cs-video-courses: List of Computer Science courses with video lectures.](https://github.com/Developer-Y/cs-video-courses) 42 | * [GitHub - JanVanRyswyck/awesome-talks: Awesome online talks and screencasts](https://github.com/JanVanRyswyck/awesome-talks) 43 | * [GitHub - caesar0301/awesome-public-datasets: A topic-centric list of high-quality open datasets in public domains. By everyone, for everyone!](https://github.com/caesar0301/awesome-public-datasets) 44 | * [GitHub - josephmisiti/awesome-machine-learning: A curated list of awesome Machine Learning frameworks, libraries and software.](https://github.com/josephmisiti/awesome-machine-learning) 45 | * [GitHub - ChristosChristofidis/awesome-deep-learning: A curated list of awesome Deep Learning tutorials, projects and communities.](https://github.com/ChristosChristofidis/awesome-deep-learning) 46 | * [GitHub - ossu/data-science: Path to a free self-taught education in Data Science!.](https://github.com/ossu/data-science) 47 | 48 | **Hackathons/Competitions:** 49 | * [Kaggle: Your Home for Data Science](https://www.kaggle.com/) 50 | * [DataHack : Biggest Data hackathon platform for Data Scientists](https://datahack.analyticsvidhya.com/) 51 | * [CrowdANALYTIX:A crowdsourced analytics platform to solve data-driven business problems](https://www.crowdanalytix.com/jq/solver.html) 52 | * [http://www.topcoder.com/active-challenges/data/](http://www.topcoder.com/active-challenges/data/) 53 | 54 | **Datasets:** 55 | * [Data worldbank](http://data.worldbank.org/) 56 | * [Open Government Data (OGD) Platform India](http://data.gov.in/) 57 | * [http://archive.ics.uci.edu/ml/](http://archive.ics.uci.edu/ml/) 58 | * [Datasets \| Kaggle](https://www.kaggle.com/datasets) 59 | * [r/datasets \| Reddit](https://www.reddit.com/r/datasets/) 60 | * [Data World](https://data.world/) 61 | * [Data Sources - Jo Hardin - Pomona College](http://research.pomona.edu/johardin/datasources/) 62 | * [Google Dataset Search](https://toolbox.google.com/datasetsearch) 63 | * [Gapminder World](https://www.gapminder.org/data/) 64 | 65 | **Podcasts:** 66 | * [Artificial Intelligence: AI Podcast - by Lex Fridman](https://lexfridman.com/ai/) 67 | * [Partially Derivative](http://www.partiallyderivative.com/) 68 | * [Data Skeptic](http://dataskeptic.com/) 69 | * [Linear Digressions](http://lineardigressions.com/) 70 | * [R-Podcast](https://r-podcast.org/) 71 | * [Not so standard deviations](http://nssdeviations.com/) 72 | * [Dataframed](https://www.datacamp.com/community/podcast) 73 | 74 | **Libraries:** 75 | * [Apache Singa](http://singa.apache.org/docs/overview.html) 76 | * [Amazon Machine Learning](https://aws.amazon.com/machine-learning/) 77 | * [TensorFlow](https://www.tensorflow.org/) 78 | * [Theano](http://deeplearning.net/software/theano/) 79 | * [Torch](http://torch.ch/) 80 | 81 | **Communities:** 82 | * [Reddit /r/DataScience](https://www.reddit.com/r/datascience/) 83 | * [Reddit r/rstats](https://www.reddit.com/r/rstats/) 84 | * [Reddit r/learnmachinelearning](https://www.reddit.com/r/learnmachinelearning/) 85 | 86 | **Misc:** 87 | * [Git for Humans - Alice Bartlett](https://speakerdeck.com/alicebartlett/git-for-humans) [[Video]](https://www.youtube.com/watch?v=eWxxfttcMts) 88 | * [From Data to Viz leads you to the most appropriate graph for your data.](https://www.data-to-viz.com/) 89 | 90 | **Videos:** 91 | * [Tetiana Ivanova - How to become a Data Scientist in 6 months a hacker’s approach to career planning](https://www.youtube.com/watch?v=rIofV14c0tc) 92 | * Beginner guide for [Neural Networks and Machine Learning](https://www.youtube.com/user/shiffman/playlists?flow=grid&view=50&shelf_id=16) 93 | 94 | **Newsletter:** 95 | * [Data Science](https://www.datascienceweekly.org/) 96 | * [Data Science and R: how do I start?](https://medium.com/@kierisi/data-science-and-r-how-do-i-start-7a87426e103e) 97 | 98 | **Math Courses:** 99 | * [Linear Algebra - Foundations to Frontiers](https://www.edx.org/course/linear-algebra-foundations-to-frontiers#!) 100 | * [Applications of Linear Algebra Part 1](https://www.edx.org/course/applications-linear-algebra-part-1-davidsonx-d003x-1) 101 | * [Applications of Linear Algebra Part 2](https://www.edx.org/course/applications-linear-algebra-part-2-davidsonx-d003x-2) 102 | 103 | * [Calculus 1A: Differentiation](https://www.edx.org/course/calculus-1a-differentiation-mitx-18-01-1x-0) 104 | * [Calculus 1B: Integration](https://www.edx.org/course/calculus-1b-integration-mitx-18-01-2x-0) 105 | * [Calculus 1C: Coordinate Systems & Infinite Series](https://www.edx.org/course/calculus-1c-coordinate-systems-infinite-mitx-18-01-3x-0) 106 | 107 | **Statistics Courses:** 108 | * [Introduction to Probability](https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2#.U3yb762SzIo) 109 | * [Introduction to Statistics: Probability](https://www.edx.org/course/subject/data-analysis-statistics) 110 | * [Statistical Reasoning](https://lagunita.stanford.edu/courses/OLI/StatReasoning/Open/about) 111 | * [Introduction to Statistics: Inference](https://www.edx.org/course/introduction-statistics-inference-uc-berkeleyx-stat2-3x) 112 | * [Introduction to Statistics: Descriptive Statistics](https://www.edx.org/course/introduction-statistics-descriptive-uc-berkeleyx-stat2-1x) 113 | * [MIT OCW Multivariable Calculus](https://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/index.htm) 114 | 115 | **Importance of Portfolio** 116 | 117 | 118 | * [My freewheeling conversation on Data Science](https://medium.com/@kesari/my-freewheeling-conversation-on-data-science-a34de1211e6d) 119 | * [How to Build a Data Science Portfolio](https://towardsdatascience.com/how-to-build-a-data-science-portfolio-5f566517c79c?gi=51f41f814cd2) 120 | * [How to flaunt your Passion for analytics in Data science job interviews?](https://towardsdatascience.com/how-to-flaunt-your-passion-for-analytics-in-data-science-job-interviews-2cb432cc3d3d) 121 | 122 | * [The cold start problem: how to build your machine learning portfolio](https://towardsdatascience.com/the-cold-start-problem-how-to-build-your-machine-learning-portfolio-6718b4ae83e9) 123 | 124 | * [Show off your Data Science skills with Kaggle Kernels](https://towardsdatascience.com/show-off-your-data-science-skills-with-kaggle-kernels-762403618c5) 125 | --------------------------------------------------------------------------------