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