Python Data Analysis Course Outline
Our data analytics course provides hands-on practice on different tools for modern, open-source data analytics. This is a course particularly useful for analysts in financial instituions (banks, insurance companies, hedge funds) that work with tools like R, SAS or Matlab.
The course is suitable for practitioners and newcomers with a basic knowledge of calculus and statistics, and can be tailored to your needs.
- Introduction to Pandas: data structures, array indexing, vectorized operations.
- Split, apply and combine: aggregate data in groups to extract summary statistics.
- Extracting, cleaning and parsing data from different sources.
- Merging data from different sources: join, concat and merge operations.
- Working with different formats, data structures.
- Back and forward filling, timezones and different levels of detail.
- Data Visualization using Matplotlib.
- Exploratory visualization with seaborn.
- Introduction to scikit-learn and machine learning.
- Supervised Learning (Logistic Regression, Random Forests).
- Statistical modelling with statsmodels.
- Unsupervised Learning and outlier detection.