/ga-data-science

Teaching materials for the part-time Data Science course at General Assembly London

Primary LanguageJupyter NotebookCreative Commons Attribution 4.0 InternationalCC-BY-4.0

Part-time Data Science course

By Gianluca Campanella (g.campanella@estimand.com)

Creative Commons License

This repository contains teaching materials (slides and Jupyter Notebooks) for the part-time Data Science course at General Assembly London.

Aims

By the end of the course, you should be able to:

  • Apply the Data Science workflow to solve problems with data
  • Load, manipulate, and summarise data using pandas
  • Produce basic data visualisations using matplotlib and seaborn
  • Perform hypothesis testing, and interpret the output of generalised linear models
  • Build and validate predictive models using sklearn

Schedule

Session Topic
1 Introduction to Data Science
2 Managing data and analyses
3 Causality and study design
4 EDA and data visualisation
Lightning talks
5 Statistical thinking
6 Generalised linear models
7 Time series
Recap
8 Regression
9 Classification
10 Clustering
11 Dimensionality reduction
Recap
12 Decision trees and random forests
13 Natural language processing
14 Ensemble methods
15 SVMs and neural networks
Final presentations