Advanced Data Analysis with Python

Raw data rarely comes ready for analysis. In practice, extracting meaningful insights requires dealing with inconsistent formats, incomplete records, and information scattered across multiple sources. This course equips participants with advanced data acquisition, preparation, and statistical analysis techniques using Python. They learn to retrieve, clean, and integrate complex datasets, apply inferential statistics to draw evidence-based conclusions, and produce reproducible analytical reports using professional best practices.

While Advanced Data Analysis with Python can be taken as a standalone course, it is the second of four Microcredentials leading to the Certificate of Open Studies (COS) in Applied Data Analytics and Visualization.

Upon successful completion of this course, participants will be able to:

• Retrieve data from multiple sources — including tabular files, JSON, HTML pages
• Clean complex datasets by identifying and treating inconsistencies, outliers, and recording errors
• Combine datasets from different sources into a unified, analysis-ready structure
• Interpret descriptive statistics and visualisations to assess data quality and uncover meaningful patterns
• Draw evidence-based conclusions from a collected sample using inferential statistics and hypothesis testing
• Produce reproducible, shareable analytical reports using Quarto

Stackable into Certificate of Open Studies (COS) in Applied Data Analytics and Visualisation

Nongraded

Professionals who work with data and want to strengthen their analytical practice by applying advanced data preparation, statistical analysis, and reporting techniques in Python. The course is designed for data analysts, researchers, engineers, scientists, and other data-driven professionals who wish to adopt analytical workflows and best practices for analyzing complex datasets.