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Must-watch videos on data-science
https://github.com/kmonsoor/data-must-watch

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Must-watch videos on data-science

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# Must-watch videos on Data Science



This list tries to cover from basic introductory staffs to deep analytical staffs on industry-renowned tools.



Inspired by [py-must-watch](https://github.com/s16h/py-must-watch). 



### Contribution

Please create pull requests to suggest more awesome video links. However, please don't add too many in a single pull-request. :)

Please add your name in the file **contributors.md** file, if possible, in the same pull-request you make to add new video in the main list.



Thanks !



------------------------------------------------------



## Intro into Data-science

* DJ Patil: **Data Science, how to be data driven and build great products** (BBVA Innovation Center, 2014)

    * Video: [youtube](https://www.youtube.com/watch?v=54t7bSXniAs) [1:22:37]

    * Comments: A great intro. Potrays Spock(Star Trek) as ship's data scientist.



* Micheael Manoochehri: **Data Just Right: A Practical Introduction to Data Science Skills** (DataEDGE 2013)

    * Video: [youtube](https://www.youtube.com/watch?v=rpwZ_i-9U0o) [1:24:50]

    * Slides: [Google Docs](http://goo.gl/sCmF0)

    * Comments: awesome intro



* Ryan Orban: **How To Become A Data Scientist** (SF Data Science)

    * Video: [youtube](https://www.youtube.com/watch?v=c52IOlnPw08) [59:07]

    * Slides: [slideshare](http://www.slideshare.net/ryanorban/how-to-become-a-data-scientist)

    * Comments: Audio is a bit hazy with background noise



* Buck Woody: **Becoming a Data Professional: Taking It To The Next Level** (The DRIVE/conference 2013)

    * Video: [youtube](https://www.youtube.com/watch?v=Zdh3p4EKLeQ) [57:05]

    * Notes: [MSDN Blog](http://blogs.msdn.com/b/buckwoody/archive/2013/02/21/link-list-becoming-a-data-professional.aspx)

    * Comments: 



## Machine Learning

* Dr. Andrew Ng: **CS 229: Machine Learning** (Stanford Lecture series 2008)

    * Video: [youtube](https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599) (20 videos, 1+Hr each)

    * Lecture materials: [Handouts](http://cs229.stanford.edu/materials.html), [course-home](http://cs229.stanford.edu/)

* [Dr. Yaser S. Abu-Mostafa](https://work.caltech.edu/index.html): **Learning from Data** (from Caltech) 

    * Lecture materials: [Handouts/Videos/Homeworks](https://work.caltech.edu/telecourse.html)

    * Comments: A self-paced advanced course on Machine Learning

* Jake VanderPlas: Machine Learning with Scikit-Learn (I) (PyCon 2015)

    * Video: [youtube](https://www.youtube.com/watch?v=L7R4HUQ-eQ0)[3:02:11]

    * Lecture materials: [codes](https://github.com/jakevdp/sklearn_pycon2015), [IPython Notebook](http://nbviewer.ipython.org/github/jakevdp/sklearn_pycon2015/blob/master/notebooks/Index.ipynb)



## Statistical Methods

* Chris Fonnesbeck: **Statistical Thinking for Data Science** (SciPy 2015)

    * Video: [youtube](https://www.youtube.com/watch?v=TGGGDpb04Yc) [23:49]



## Algorithms

* Presenter-name: **Title of the video** (Event, Year)

    * Video: [video link] [length-of-video]

    * Slides: [slide link]

    * [Misc resources](link)

    * 



## Mathematics

* Presenter-name: **Title of the video** (Event, Year)

    * Video: [video link] [length-of-video]

    * Slides: [slide link]

    * [Misc resources](link)

    * 



## Intro on Hadoop Ecosystem

*  Bill Graham: **Intro to Hadoop** (Part of BerkeleyiSchool course Info290:"Analyzing Big Data With Twitter", 2012)

    * Video: [youtube](https://www.youtube.com/watch?v=t3fEGhE-HYA) [1:22:26]

    * Slides: [presentation](http://blogs.ischool.berkeley.edu/i290-abdt-s12/files/2012/08/BillGraham_IntroToHadoop_Aug30.pdf)

    * 



## HDFS

* Andrew Collette: **HDF5 is Eating the World** (SciPy 2015)

    * Video: (Youtube)[https://www.youtube.com/watch?v=nddj5OA8LJo] [18:30]

    * Slides: 



* Quincey Koziol: **Title of the video** (2014)

    * Video: (Youtube)[https://www.youtube.com/watch?v=IN1bqxj4pxE] [1:17:09]

    * Slides: (mcs.anl.gov)[http://press3.mcs.anl.gov/computingschool/files/2014/01/QKHDF5-Intro-v2.pdf]



## Spark

* Presenter-name: **Title of the video** (Event, Year)

    * Video: [video link] [length-of-video]

    * Slides: [slide link]

    * [Misc resources](link)

    * 



## Python-based analysis

* Wes McKinney: **My Data Journey with Python** (SciPy 2015)

    * Video: [youtube](https://www.youtube.com/watch?v=kHdkFyGCxiY) [47:45]

    * Slides: [SlideShare](http://www.slideshare.net/wesm/my-data-journey-with-python)

* Sarah Guido: **Hands-on Data Analysis with Python** (PyCon 2015)

    * Video: [Youtube](https://www.youtube.com/watch?v=L4Hbv4ugUWk) [2:54:57]

* Jonathan Rocher: **Analyzing and Manipulating Data with Pandas** (SciPy 2015 Tutorial )

    * Video: [Youtube](https://www.youtube.com/watch?v=0CFFTJUZ2dc)

    * [Exercise and tutorial instructions](https://github.com/jonathanrocher/pandas_tutorial)

* Andreas Mueller & Kyle Kastner: **Machine Learning with Scikit Learn** (SciPy 2015 Tutorial)

    * Video: [Part I](https://www.youtube.com/watch?v=80fZrVMurPM), [Part II](https://www.youtube.com/watch?v=Ud-FsEWegmA)

    * [Tutorial Materials](https://github.com/amueller/scipy_2015_sklearn_tutorial)



## R-based analysis

* David Langer: **Introduction to Data Science with R - Data Analysis** (2014)

    * Video: [https://www.youtube.com/watch?v=32o0DnuRjfg&index=1&list=PLTJTBoU5HOCRrTs3cJK-PbHM39cwCU0PF] [1:21:49]

    * Source code: [https://github.com/EasyD/IntroToDataScience]

    * This is the first video of a two-part tutorial. 



## Uncategorized

* Presenter-name: **Title of the video** (Event, Year)

    * Video: [video link] [length-of-video]

    * Slides: [slide link]

    * comments

    *