Teach Yourself Data Science

Here is my guide on moving into data science from non-traditional degrees with the best resources.

This guide answers these frequently asked questions.

  • What do data scientists do?
  • How to learn data science? (Lesson Guide)
  • How to get into data science? (Interview Guide)
  • How to get mentorship in data science?

Video

Here is the live stream of the event ‘From Engineering to Data Science’ attended by students from NTU, SG.

Lesson Guide

Step 1: Programming and Data Processing

Learning objectives:

  • Basic SQL
  • Basic Python / R
  • Basic data wrangling and visualization packages in python (pandas, matplotlib) or R (ggplot and tidyverse)
  • Data reporting (Tableau / Google Data Studio / other dashboarding tools)
  • Machine learning (without the math) - optional

Curriculum explainer: How to Learn Data Science - SQL, Python, R, Data Viz

Classes recommended:

  • Pick ONE from ‘Topic 1’, ‘Topic 2’, and ‘Topic 3’ each, OR
  • Pick ONE from ‘Combination of Topic 1 and 2’, and ‘Topic 3’

Step 2: Introduction to Mathematics, Probability, Statistics, Machine Learning

Learning objectives:

  • Pre-college Level Statistics and Probability
  • Pre-college level math (linear algebra / single variable calculus)
  • Machine learning (with a lot of math)
  • Intermediate SQL
  • Make your very own data science project

Curriculum explainer:

Classes recommended:

Shaded in gray: highly recommended by author

Tip #2: Once you have your very own data science project, you are generally ready to start going for data analytics / science interviews. Congratulations for making it here!

Step 3: Intermediate Mathematics, Probability, Statistics, Machine Learning

Learning objectives:

  • Specialize in a particular topic in machine learning or statistics that is relevant to your industry.

Curriculum explainer (Same as above)

Classes recommended:

Shaded in gray: highly recommended by author

Congratulations! You’re on track to get your first interview. Do contact me if you have any questions about the curriculum or doubts about how to prepare for the interview.