Learn Data science with Github

Learn Data Science with GitHub

Articles & Tutorials

Learn data Science with Github. Yes, It is an idea that started at the popular site Github a short time ago, where some programmers – calling themselves “OSS Community University” designed an integrated curriculum from completely free sources to study Data Science as if you were studying in a completely regular university.

 

Method & Process Learn Data Science :

1) Linear Algebra:

1.1-Linear Algebra – Foundations to Frontiers
https://www.edx.org/course/linear-algebra-foundations-to-frontiers

1.2-Applications of Linear Algebra :

Part 1:
https://www.edx.org/course/applications-linear-algebra-part-1-davidsonx-d003x-1

Part 2:
https://www.edx.org/course/applications-linear-algebra-part-2-davidsonx-d003x-2

2) Single Variable Calculus:

2.1- Calculus 1A: Differentiation:
www.edx.org/course/calculus-1a-differentiation

2.2- Calculus 1B: Integration:

https://www.edx.org/course/calculus-1b-integration

2.3- Calculus 1C: Coordinate Systems & Infinite Series:

www.edx.org/course/calculus-1c-coordinate-systems-infinite-series

 

3) Multivariable Calculus:

3.1- MIT OCW Multivariable Calculus:

https://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/index.htm

 

4) Python:

4.1- Introduction to Computer Science and Programming Using Python:
https://www.edx.org/course/introduction-to-computer-science-and-programming-7

4.2- Introduction to Computational Thinking and Data Science:
https://www.edx.org/course/introduction-to-computational-thinking-and-data-4

4.3- Introduction to Python for Data Science:
https://prod-edx-mktg-edit.edx.org/course/introduction-to-python-for-data-science-4

4.4- Programming with Python for Data Science
https://www.edx.org/course/programming-with-python-for-data-science

 

5) Probability and Statistics:

5.1 Introduction to Probability
https://www.edx.org/course/probability-the-science-of-uncertainty-and-data

5.2 Statistical Reasoning:

https://lagunita.stanford.edu/courses/OLI/StatReasoning/Open/about

5.3 Introduction to Statistics: Descriptive Statistics:
https://www.edx.org/course/introduction-to-statistics-descriptive-statistic-2

5.4 Introduction to Statistics: Probability:
https://www.edx.org/course/introduction-to-statistics-probability-2

5.5 Introduction to Statistics: Inference:

https://www.edx.org/course/introduction-to-statistics-inference-5

 

6) Introduction to Data Science:

6.1-Introduction to Data Science
https://www.coursera.org/specializations/data-science

6.2-Data Science – CS109 from Harvard
http://cs109.github.io/2015/

6.3-The Analytics Edge
https://www.edx.org/course/analytics-edge-mitx-15-071x-3

 

7) Machine Learning:

7.1 Learning From Data (Introductory Machine Learning)
https://www.edx.org/course/learning-from-data-introductory-machine-learning

7.2 Statistical Learning
https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about

7.3 Stanford’s Machine Learning Course
https://www.coursera.org/learn/machine-learning

 

Project Data Science:

Complete Kaggle’s Getting Started and Playground Competitions
https://www.kaggle.com/

 

1) Convex Optimization:

https://lagunita.stanford.edu/courses/Engineering/CVX101/Winter2014/about

2) Data Wrangling:

https://www.udacity.com/course/data-wrangling-with-mongodb–ud032

3) Big Data:

3.1 Intro to Hadoop and MapReduce:

https://www.udacity.com/course/intro-to-hadoop-and-mapreduce–ud617

3.2 Deploying a Hadoop Cluster:

https://www.udacity.com/course/deploying-a-hadoop-cluster–ud1000

 

4) Database:

4.1 Stanford’s Database course:

https://lagunita.stanford.edu/courses/DB/2014/SelfPaced/about

5) Natural Language Processing:

5.1-Deep Learning for Natural Language Processing
http://cs224d.stanford.edu/

 

6) Deep Learning:

6.1 Deep Learning:
https://eg.udacity.com/course/deep-learning–ud730

 

Source :

https://github.com/ossu/data-science#project

 

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