⭐⭐⭐ 🎯 Personal roadmap to get a deeper knowledge in Data Science and related areas
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README-detailed-version.md Update README-detailed-version.md Jul 8, 2018
README.md Update README.md Jul 8, 2018

README.md

DATA SCIENCE ROADMAP

This repo has been inspired by these:

Why this?

I want to track the progress of my studies in this broad area. I do not intend to list a huge number of resources or courses, just the ones that I have completed so far and the following ones that are on my mind for a next step (in a short/medium or even long term). Anyway, valuable resources that may be part of my plan in the future.

How are things classified here?

It is not that easy to classify subjects in Data Science. Some courses may correspond clearly to only one category, some others may belong to more than one, etc. I have tried to simplify the categories of interest in what you can see in the table of contents. There may still be some incongruences, but I think I am happy with the result :)


TABLE OF CONTENTS

  1. Introductory Courses in Data Science.
  2. General Courses in Data Science.
  3. Data Analysis.
  4. Machine Learning.
  5. Text Mining and NLP.
  6. Data Visualization and Reporting.
  7. Probability and Statistics.
  8. Big Data.
  9. Books.
  10. Other courses in Computer Science.

1. INTRODUCTORY COURSES IN DATA SCIENCE (back to top ↑)

Python (back to top ↑)

R (back to top ↑)

SQL (back to top ↑)

2. GENERAL COURSES IN DATA SCIENCE (back to top ↑)

Python (back to top ↑)

R (back to top ↑)

3. DATA ANALYSIS (back to top ↑)

Python (back to top ↑)

R (back to top ↑)

Weka (back to top ↑)

  • Data Mining and Big Data Analysis (by Itziar Irigoien, Javier Muguerza, Ibai Gurrutxaga, José Ignacio Martín, Olatz Arbelaitz, Txus Perez. Master in Computational Engineering and Intelligent Systems, University of the Basque Country). 3 ECTS ~ 75 hours.

4. MACHINE LEARNING (back to top ↑)

Octave/Matlab (back to top ↑)

Python (back to top ↑)

R (back to top ↑)

5. TEXT MINING AND NLP (back to top ↑)

Python (back to top ↑)

R (back to top ↑)

6. DATA VISUALIZATION AND REPORTING (back to top ↑)

Python (back to top ↑)

R (back to top ↑)

JavaScript (back to top ↑)

7. PROBABILITY AND STATISTICS (back to top ↑)

Python (back to top ↑)

R (back to top ↑)

8. BIG DATA (back to top ↑)

General (back to top ↑)

Spark (back to top ↑)

9. BOOKS (back to top ↑)

This is a selection of books for Data Science and related disciplines from which I have good references. The books are listed in descending order of publication date.

Title Author Publisher Release Date Code
◻️ Deep Learning with Python Francois Chollet Manning Jan 2018 (*) GitHub
✔️ Python Tricks: The Book Dan Bader Ron Holland Designs Oct 2017
◻️ Python for Data Analysis (2nd ed.) Wes McKinney O'Reilly Oct 2017 GitHub
◻️ Python Machine Learning (2nd ed.) Sebastian Raschka, Vahid Mirjalili Packt Sep 2017 GitHub
◻️ An Introduction to Statistical Learning (2nd ed.) Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Springer Sep 2017 R code
◻️ Deep Learning Josh Patterson, Adam Gibson O'Reilly Aug 2017
◻️ Fundamentals of Deep Learning Nikhil Buduma O'Reilly Jun 2017 GitHub
◻️ The Elements of Statistical Learning (2nd ed.) Trevor Hastie, Robert Tibshirani, Jerome Friedman Springer May 2017 Datasets
◻️ Practical Statistics for Data Scientists Peter Bruce, Andrew Bruce O'Reilly May 2017 GitHub
◻️ Hands-On Machine Learning with Scikit-Learn and TensorFlow Aurélien Géron O'Reilly Apr 2017 GitHub
◻️ Think Like a Data Scientist Brian Godsey Manning Abr 2017
◻️ Deep Learning Ian Goodfellow, Yoshua Bengio, Aaron Courville MIT Press Jan 2017
◻️ Efficient R Programming Robin Lovelace, Colin Gillespie O'Reilly Dec 2016 GitHub
◻️ Python Data Science Handbook Jake VanderPlas O'Reilly Nov 2016 GitHub
◻️ Introduction to Machine Learning with Python Sarah Guido, Andreas C. Müller O'Reilly Oct 2016 GitHub
◻️ Real-World Machine Learning Henrik Brink, Joseph W. Richards, Mark Fetherolf Manning Sep 2016 GitHub
◻️ Algorithms of the Intelligent Web (2nd ed.) Douglas G. McIlwraith, Haralambos Marmanis, Dmitry Babenko Manning Aug 2016 GitHub
◻️ R for Data Science Garrett Grolemund, Hadley Wickham O'Reilly Jul 2016 GitHub
◻️ Introducing Data Science Davy Cielen, Arno D. B. Meysman, Mohamed Ali Manning May 2016 Code 1, 2
◻️ R Deep Learning Essentials Joshua F. Wiley Packt Mar 2016 GitHub
◻️ R in Action (2nd ed.) Robert I. Kabacoff Manning May 2015 GitHub
◻️ Data Science from Scratch Joel Grus O'Reilly Apr 2015 GitHub
◻️ Data Science at the Command Line Jeroen Janssens O'Reilly Oct 2014 GitHub
✔️ Learning scikit-learn: Machine Learning in Python Raúl Garreta, Guillermo Moncecchi Packt Nov 2013 GitHub

(*) Expected publication date

10. OTHER COURSES IN COMPUTER SCIENCE (back to top ↑)

Software Design (back to top ↑)

JavaScript (back to top ↑)

Python (back to top ↑)

R (back to top ↑)

  • Computation in Science and Engineering: numerical simulation (by Ander Murua. Master in Computational Engineering and Intelligent Systems, University of the Basque Country). 6 ECTS ~ 150 hours.
  • Image and signal processing (by Mamen Hernández and Josune Gallego. Master in Computational Engineering and Intelligent Systems, University of the Basque Country). 4.5 ECTS ~ 112.5 hours.
  • Cryptography (by Itziar Baragaña and Alicia Roca. Master in Computational Engineering and Intelligent Systems, University of the Basque Country). 4.5 ECTS ~ 112.5 hours.

Miscellaneous (back to top ↑)