Course description

The course develops practical skills in data analytics, statistical analysis, modelling and visualisation with Python. Students learn to manipulate data using pandas and NumPy, calculate descriptive and inferential statistics and communicate results through visualisations.

The course also introduces probability, correlation, regression, time-series forecasting and predictive analytics. Students apply the methods to datasets from finance, healthcare, environmental science, social media and cryptocurrency markets.

Learning outcomes

After completing the course, students should be able to:

• use basic Python syntax for data analysis;
• work with arrays, matrices and data frames;
• clean and transform structured datasets;
• calculate and interpret descriptive statistics;
• understand probability distributions and statistical testing;
• analyse relationships using correlation and regression;
• create effective data visualisations;
• apply several analytical methods within one workflow; and
• complete and present an independent data-analysis project.

Course content

Teaching and learning methods

The course uses online lectures, live coding, guided exercises, independent assignments, troubleshooting sessions and project-based learning. Students apply methods to real datasets and receive feedback during the development of their final projects.

Assessment

The final project requires students to apply data management, statistical analysis and visualisation techniques to a real dataset and present their results.

Learning materials

Students receive lecture slides, recorded lessons, coding notebooks and exercises. Recommended resources include works by VanderPlas, Bruce, Bruce and Gedeck, McKinney, Severance, and relevant Python documentation.