Data Science with Python

Objectives

By the end of the course you should:

• Be able to perform basic data analysis using Python
• Have basic understanding of the machine learning process
• Know how to share reproducible results from data analysis
• Have gained familiarity with some important topics in data such as data versioning

Prerequisites

• An appetite for learning
• Commitment to pursue your learning goals

Course outline

Python fundamentals

• Data types
• Operators
• Variables
• Introduction to functions
• Reading and writing files
• Error handling
• Imports
• Iteration
• Flow control

Data visualisation

• Review of graphical EDA techniques
  • Histogram
  • Box plot
  • Scatterplot
  • Line plot
  • Bar plot
• Reading data from flat files with pandas
• Automated EDA with pandas-profiling
• Creating static plots with seaborn and matplotlib

Data analysis

• Aggregating data
• Merging data
• Data cleaning

Introduction to machine learning

• The machine learning landscape
• Supervised learning with tabular data
  • Regression analysis
  • Classification

Miscellaneous

• Communicating results with Quarto
• Tracking machine learning experiments with MLflow
• Version control with Git and GitHub
• Data versioning with DVC

Capstone project