Take a look at what our course modules include in a little more detail:

Module One: Introduction to Python
Overview of basic Python programming; Basic data input/output; Functions and classes; Key useful resources

Module Two: Common Libraries for Data Science
Numpy: Linear Algebra; Keras: Neural Networks and Models; PyTorch: Open Source Deep Learning; Pandas: Data Frames and Series; TensorFlow: Google Data Science

Module Three: Platforms/IDEs
Writing Python code locally (PyCharm vs. Text Editors); Working with Git and Version Control Publishing/Hosting Python; Frameworks and libraries

Module Four: Jupyter Notebooks
Introduction to Jupyter; Jupyter vs. Databricks; Writing Python in Pieces; Benefits of inline data analysis; Collaboration in ML notebooks

Module Five: Visualising Data
Introduction to Plotly; Introduction to Matplotlib; Histograms and scatterplots; Showing key reference points in graphs

Module Six: Data Analysis (EDA)
Basics of EDA (Exploratory Data Analysis) Data cleaning; Common questions to ask; Reframing data to answer questions

Module Seven: Testing and Troubleshooting
Common Pitfalls; Testing Python modules; Test-driven development; Pair programming/Code reviews; Automated testing procedures