This course aims to introduce fundamental concepts in python, data processing in pandas and data visualisation in matplotlib, seaborn and plotly. The data visualisation part will both cover exploratory data analysis and explanatory data analysis, where we will use data visualisation for data storytelling. Further, in the course we will also work with git, github for source control and poetry for virtual environment. A course in AI, data science and/or data engineering is a natural continuation after this course.
Although the course is a fundamental course, a large amount of concepts are covered in a short time and hence it's good to have some programming experience in other languages.
| Week | Content |
|---|---|
| 6 | installation, git, poetry, fundamentals - if, while, for, list, matplotlib, strings, functions, file handling, dictionary |
| 7 | numpy, exception handling, OOP: class, object, attributes, properties, inheritance, polymorphism, lab 1 |
| 8 | pandas, series, dataframe, missing data, selection, aggregate, plotly express, seaborn, lab 1 |
| 9 | merge, concatenate, join, sort, apply, lab 1, lab 2, (API, .env - moved to next course), KPI |
| 10 | data storytelling, matplotlib depth, lab 2 |
| 11 | high performance pandas (moved to next course), lab 2 |
Note that this schedule is an overview and will be updated during the course.
AIgineer AB