airalcorn2 / Michael-s-Guide-to-Becoming-a-Data-Scientist

I was once asked about transitioning to a career in data science by three different UChicago grad students over a short period of time, so I decided to put together this outline in case anyone else was curious.

Michael's Guide to Becoming a Data Scientist

Michael's Guide to Becoming a Data Scientist by Michael A. Alcorn is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

I was once asked about transitioning to a career in data science by three different UChicago grad students over a short period of time, so I decided to put together this outline in case anyone else was curious.

Table of Contents

• My CV
• General Information
• Get Experience!
• Curriculum
• Programming
• Databases
• Big Data Tools

Guide

• My CV
• General Information
  • 8 Skills You Need to be a Data Scientist
  • What's the difference between a data architect, data analyst, data engineer, and data scientist?
    • "Data analyst" will probably be less exciting than "data scientist" for those with a scientific background.
  • Advice from a Data Scientist at Quora
  • /r/MachineLearning
• Get Experience!
  • Intern - this is the best possible thing you can do.
  • Try out Kaggle competitions.
  • Create a LinkedIn account and keep it updated.
• Curriculum
  • Free Courses - use them
    • Coursera, edX, Udacity, Saylor, Khan Academy
    • Can use my course history as a guide.
  • Math
    • Calculus (at least up to partial derivatives, which is typically Calculus III)
    • Linear Algebra
    • Analysis (advanced)
  • Statistics - know Bayesian and frequentist theory
  • Algorithms
  • Machine Learning - know the big algorithms; natural language processing is probably the most useful subfield to learn
  • Other Topics - graphs, game theory, information theory, etc.
• Programming
  • Must know Python. Almost all data scientist positions require cleansing and transforming data on a large scale and Python is typically the language of choice for this task.
  • Important Python packages/libraries → scikit-learn, NumPy, Keras, TensorFlow, Theano, SciPy, Pandas, Statsmodels
  • Must know R.
  • Should know your way around a *nix terminal.
  • Version control - should know basics of Git.
  • Put personal projects on GitHub.
  • Contribute to open source projects.
• Databases - definitely know SQL, should probably look into NoSQL databases as well (e.g., MongoDB)
  • The best way to learn databases is by working with them. Find a database and practice writing queries for it.
• Big Data Tools
  • Be familiar with the following: Apache Hadoop, MapReduce, Apache Spark, Apache Pig, Apache Hive, Apache Mahout, Apache Solr, Apache Lucene