Learning Path To Become Data Scientist – Step by Step Guide

There’s an interesting Q&A in Quora on ‘How can I become a data scientist?’. We’d like to summarize the learning path recommended by Alex Kamil, one of the most viewed writers in Quora on Data Science. We provided some useful links for you to follow up on each stage/step:

Data Scientist's Skillset
Data Scientist’s Skillset

[Here’s a report on how much Data Scientists get paid in US in 2016: Salary Insights 2016]

  1. Learn about linear algebra/matrix factorizations
    Standford’s Applied Linear Algebra Refresher: http://stanford.edu/~arbenson/refresher/applied-la-2013.pdf
    Linear Algebra and Its Applications by David C. Lay
    University of California, Davis’ Textbook on Linear Algebra
  2. Learn about distributed database systems
    Learn the usage of different design strategies for distributed databases, and study query processing techniques and algorithms, transaction management and concurrency control concepts used in such systems.
    Purdue University’s Course: https://www.cs.purdue.edu/homes/clifton/cs542/
  3. Learn about statistical analysis
    MIT’s Statistical Thinking and Data Analysis Course: http://ocw.mit.edu/courses/sloan-school-of-management/15-075j-statistical-thinking-and-data-analysis-fall-2011/
    Statistics and Data Analysis: From Elementary to Intermediate
    The Elements of Statistical Learning
    An Introduction to Statistical Learning with Applications in R
    Probability and Statistics for Programmers
    Statistics for Hackers
  4. Learn about mathematical optimization or more generally, computational mathematics
    Standford’s Convex Optimization Course: http://web.stanford.edu/~boyd/cvxbook/, http://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf
  5. Learn about machine learning
    Standford’s Machine Learning Course: http://cs229.stanford.edu/materials.html
    Python Machine Learning
    Machine learning projects: http://cs229.stanford.edu/projects2015.html
    Machine Learning Cheatsheet
  6. Learn about Information retrieval
    Information retrieval (IR) is finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need from within large collections (usually stored on computers).
    Standford’s Information Retrieval Course: http://nlp.stanford.edu/IR-book/
  7. Learn about signal detection and estimation
    University of Illinois at Chicago’s Detection and Estimation Course: http://www.ece.uic.edu/~devroye/courses/ECE531/
    Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory
    Fundamentals of Statistical Signal Processing, Volume II: Detection Theory
  8. Master algorithms and data structures
    Princeton University’s Algorithms and Data Structures Course: https://www.cs.princeton.edu/~rs/AlgsDS07/
  9. Practice Problems
    Titanic dataset from Kaggle
    Human activity recognition using smartphone dataset
    Hubway Visualization challenge
    MovieLens Dataset
  10. Take a look at the list of Machine Learning & Data Science skills/areas you need to focus on to get hired by Fortune 500 Companies
Copy Protected by Chetan's WP-Copyprotect.