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For-Data-Science-Beginners

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Set of 📝 with 🔗 to help those who are Data Science beginners 🤖

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# For Data Science Beginners

Set of Notes with links to help those who are Data Science Beginners. Feel free to raise a PR if you need to!

**Blogs to Follow:**
* [Data Science Central](http://www.datasciencecentral.com/)
* [KDNuggets](http://www.kdnuggets.com/)
* [Analytics Vidhya](https://www.analyticsvidhya.com/)
* [Data Science Plus](https://datascienceplus.com/)
* [R-Bloggers](https://www.r-bloggers.com/)
* [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)
* [Cognitive class](https://cognitiveclass.ai/blog/)
* [Simply Statistics](https://simplystatistics.org/)
* [Edwin Chens blog](http://blog.echen.me/)
* [Deepmind Blog](https://deepmind.com/blog/)
* [Dataversity Blog](http://www.dataversity.net/category/blogs/)
* [Yhat Blog](http://blog.yhat.com/)
* [Visualization with R](http://socviz.co/)
* [DataSchool](https://www.dataschool.io/)

**To Learn:**
* [ADVICE FOR ASPIRING DATA SCIENTISTS AND OTHER FAQS](https://yanirseroussi.com/2017/10/15/advice-for-aspiring-data-scientists-and-other-faqs/)
* [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)
* [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/)
* [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/)
* [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/)
* [Infographic - Quick Guide to learn Python for Data Science](https://www.analyticsvidhya.com/blog/2015/05/infographic-quick-guide-learn-python-data-science/)
* [Minimalistic Learning Path to Become a Data Scientist](https://hackernoon.com/minimalistic-learning-path-to-become-a-data-scientist-c0a4f614bd09)
* [SQL for Data Analysis (Udacity)](https://classroom.udacity.com/courses/ud198)
* [Recommended Python learning resources - fast.ai](https://forums.fast.ai/t/recommended-python-learning-resources/26888)
+ [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/)

**Videos:**
* [Practical Deep Learning Course for Coders](https://course.fast.ai/)
* [Introduction to R for Data Science \| edX](https://www.edx.org/course/introduction-r-data-science-microsoft-dat204x-2)
* [Data Science \| Coursera](https://www.coursera.org/specializations/jhu-data-science)
* [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)
* [Statistics and Data Science \| Stanford](https://www.youtube.com/playlist?list=PLoROMvodv4rO5jY6RA1eFVcLVY2kJU_EL)

**Github:**
* [GitHub - Developer-Y/cs-video-courses: List of Computer Science courses with video lectures.](https://github.com/Developer-Y/cs-video-courses)
* [GitHub - JanVanRyswyck/awesome-talks: Awesome online talks and screencasts](https://github.com/JanVanRyswyck/awesome-talks)
* [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)
* [GitHub - josephmisiti/awesome-machine-learning: A curated list of awesome Machine Learning frameworks, libraries and software.](https://github.com/josephmisiti/awesome-machine-learning)
* [GitHub - ChristosChristofidis/awesome-deep-learning: A curated list of awesome Deep Learning tutorials, projects and communities.](https://github.com/ChristosChristofidis/awesome-deep-learning)
* [GitHub - ossu/data-science: Path to a free self-taught education in Data Science!.](https://github.com/ossu/data-science)

**Hackathons/Competitions:**
* [Kaggle: Your Home for Data Science](https://www.kaggle.com/)
* [DataHack : Biggest Data hackathon platform for Data Scientists](https://datahack.analyticsvidhya.com/)
* [CrowdANALYTIX:A crowdsourced analytics platform to solve data-driven business problems](https://www.crowdanalytix.com/jq/solver.html)
* [http://www.topcoder.com/active-challenges/data/](http://www.topcoder.com/active-challenges/data/)

**Datasets:**
* [Data worldbank](http://data.worldbank.org/)
* [Open Government Data (OGD) Platform India](http://data.gov.in/)
* [http://archive.ics.uci.edu/ml/](http://archive.ics.uci.edu/ml/)
* [Datasets \| Kaggle](https://www.kaggle.com/datasets)
* [r/datasets \| Reddit](https://www.reddit.com/r/datasets/)
* [Data World](https://data.world/)
* [Data Sources - Jo Hardin - Pomona College](http://research.pomona.edu/johardin/datasources/)
* [Google Dataset Search](https://toolbox.google.com/datasetsearch)
* [Gapminder World](https://www.gapminder.org/data/)

**Podcasts:**
* [Artificial Intelligence: AI Podcast - by Lex Fridman](https://lexfridman.com/ai/)
* [Partially Derivative](http://www.partiallyderivative.com/)
* [Data Skeptic](http://dataskeptic.com/)
* [Linear Digressions](http://lineardigressions.com/)
* [R-Podcast](https://r-podcast.org/)
* [Not so standard deviations](http://nssdeviations.com/)
* [Dataframed](https://www.datacamp.com/community/podcast)

**Libraries:**
* [Apache Singa](http://singa.apache.org/docs/overview.html)
* [Amazon Machine Learning](https://aws.amazon.com/machine-learning/)
* [TensorFlow](https://www.tensorflow.org/)
* [Theano](http://deeplearning.net/software/theano/)
* [Torch](http://torch.ch/)

**Communities:**
* [Reddit /r/DataScience](https://www.reddit.com/r/datascience/)
* [Reddit r/rstats](https://www.reddit.com/r/rstats/)
* [Reddit r/learnmachinelearning](https://www.reddit.com/r/learnmachinelearning/)

**Misc:**
* [Git for Humans - Alice Bartlett](https://speakerdeck.com/alicebartlett/git-for-humans) [[Video]](https://www.youtube.com/watch?v=eWxxfttcMts)
* [From Data to Viz leads you to the most appropriate graph for your data.](https://www.data-to-viz.com/)

**Videos:**
* [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)
* Beginner guide for [Neural Networks and Machine Learning](https://www.youtube.com/user/shiffman/playlists?flow=grid&view=50&shelf_id=16)

**Newsletter:**
* [Data Science](https://www.datascienceweekly.org/)
* [Data Science and R: how do I start?](https://medium.com/@kierisi/data-science-and-r-how-do-i-start-7a87426e103e)

**Math Courses:**
* [Linear Algebra - Foundations to Frontiers](https://www.edx.org/course/linear-algebra-foundations-to-frontiers#!)
* [Applications of Linear Algebra Part 1](https://www.edx.org/course/applications-linear-algebra-part-1-davidsonx-d003x-1)
* [Applications of Linear Algebra Part 2](https://www.edx.org/course/applications-linear-algebra-part-2-davidsonx-d003x-2)

* [Calculus 1A: Differentiation](https://www.edx.org/course/calculus-1a-differentiation-mitx-18-01-1x-0)
* [Calculus 1B: Integration](https://www.edx.org/course/calculus-1b-integration-mitx-18-01-2x-0)
* [Calculus 1C: Coordinate Systems & Infinite Series](https://www.edx.org/course/calculus-1c-coordinate-systems-infinite-mitx-18-01-3x-0)

**Statistics Courses:**
* [Introduction to Probability](https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2#.U3yb762SzIo)
* [Introduction to Statistics: Probability](https://www.edx.org/course/subject/data-analysis-statistics)
* [Statistical Reasoning](https://lagunita.stanford.edu/courses/OLI/StatReasoning/Open/about)
* [Introduction to Statistics: Inference](https://www.edx.org/course/introduction-statistics-inference-uc-berkeleyx-stat2-3x)
* [Introduction to Statistics: Descriptive Statistics](https://www.edx.org/course/introduction-statistics-descriptive-uc-berkeleyx-stat2-1x)
* [MIT OCW Multivariable Calculus](https://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/index.htm)

**Importance of Portfolio**

* [My freewheeling conversation on Data Science](https://medium.com/@kesari/my-freewheeling-conversation-on-data-science-a34de1211e6d)
* [How to Build a Data Science Portfolio](https://towardsdatascience.com/how-to-build-a-data-science-portfolio-5f566517c79c?gi=51f41f814cd2)
* [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)

* [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)

* [Show off your Data Science skills with Kaggle Kernels](https://towardsdatascience.com/show-off-your-data-science-skills-with-kaggle-kernels-762403618c5)

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