index.qmd

---
title: "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