Data Science for Beginners Reading Lists

Reading lists for these sessions:

If it's "Required", you'll get most out of the course if you read this before the class.
If it's "Optional", it'll help you understand in more depth, but you'll be fine in the class without it. If it's "Deeper", you don't need it for the class, but it will help you get deeper into the class subject if you get really interested in it.

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Session 1: Designing a data science project

Required (read before the session)  

Deeper: the data science process:

Deeper: data science project design:

Deeper: examples of data science for social good:

Deeper: other data science examples:

Deeper into data ethics:

Session: Python basics

Optional: tool installs:

Deeper into the basics of Python:

Deeper into coding for data science:

Session: Acquiring data

Deeper into acquiring data:

Session: Communicating results

Required (read before the session)

Deeper into visualisation:

Session: Cleaning and Exploring Data

Deeper into data cleaning:

Deeper into Pandas:

Deeper into R:

Deeper into probability and statistics:

Session: Machine Learning

Deeper into Machine learning:

Session: Handling text data

Deeper into handling text data:

Session: Handling geospatial data


Deeper into GIS data:

Session: Learning relationships from data

Deeper into relationship data:

Session: Handling big data

Big data and data engineering:

Data science teams:

Session: Enterprise data tools

Deeper into proprietory systems:

Session: Statistics

Deeper into statistics:

General / Unsorted