1. Hands-on demonstration with Jupyter Notebook and Python
2. Working with key data science libraries including NumPy, Pandas, Matplotlib, and Seaborn.
3. Important topics covered include data types and structures, data munging, transformation, visualisation, time-series modelling, input / output operations, statistical application with python and working with EXCEL.
4. The course will include financial examples.