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
- An appetite for learning
- Commitment to pursue your learning goals
- Data types
- Operators
- Variables
- Introduction to functions
- Reading and writing files
- Error handling
- Imports
- Iteration
- Flow control
- 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
- Aggregating data
- Merging data
- Data cleaning
- The machine learning landscape
- Supervised learning with tabular data
- Regression analysis
- Classification
- Communicating results with Quarto
- Tracking machine learning experiments with MLflow
- Version control with Git and GitHub
- Data versioning with DVC