Skill set Buildup

This is a part of my plan (the executed or currently being executed part) to get acquainted with the practices of Data Science and how I am following it up. It is mostly for personal use, but maybe somebody else can find it useful. I am doing this by taking online courses and reading books.

These are the courses completed to date:

  1. Coursera – University of Illinois – Urbana Champaign
    • Text Retrieval and Search Engines (non Certified)
    • Text Mining and Analytics (non Certified)
    • Data Visualization (Certified)
  2. Coursera – Johns Hopkins University (completed)
    • The Data Scientist’s Toolkit (Certified)
    • R Programming (Certified)
    • Getting and Cleaning Data (Certified)
    • Exploratory Data Analysis (Certified)
    • Reproducible Research (Certified)
    • Statistical Inference (Certified)
    • Regression Models (Certified)
    • Practical Machine Learning (Certified)
    • Developing Data Products (Certified)
    • Data Science Capstone (Certified)
  3. IONISX – EPITA
    • Python pour les Scientifics
  4. Codecademy
    • The Python track
  5. SimpleProgrammer.com
    • Blogging course (for this blog mainly)
  6. Udemy.com
    • R, ggplot and Simple Linear Regression
    • R, GGPlot and Polynomial Regression
    • Training sets, Test sets, R and GGPlot
    • Datta Science: Logistic Regression in Python
  7. Udacity:
    • Introduction to Descriptive Statistics
  8. Coursera: University of Minnesota
    • Introduction to Recommender Systems (Certified)
  9. DataCamp:
    • Having fun with Goglevis (Certified)

In progress or upcoming

  1. Coursera – University of Illinois – Urbana Champaign
    • Pattern Discovery in Data Mining (in doubt)
  2. Coursera – University of Michigan
    • Applied Data Science in Python specialization
  3. Udacity:
    • Introduction to Inferential Statistics
    • Data Analysis with R
    • Intro to Data Science
  4. Edx:
    • The Analytics Edge

Books:

    • Data Science from Scratch (Joel Grus)
    • R Programming (Roger D. Peng) – available on leanpub
    • Introduction to Python for Econometrics, Statistics and Data Analysis (Kevin Sheppard – University of Oxford) – Free download
    • Think Statistics, by Allen B. Downey.  Free download
    • Open Intro to Statistics Edition 3, (Diez, Barr, Çetinkaya-Rundel) Free download
    • Mining of Massive Datasets (free download) by Jure Leskovec, Anand Rajaraman, Jeff Ullman

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