Basics of Data Analysis (advanced-level)

This is an advanced-level self-standing learning module on Basics on Data Analysis.

Building upon the foundations of introductory data analysis, this module (10–20 hours) explores more advanced analytical techniques and their application to complex, real-world datasets. The course deepens the understanding of multivariate analysis, predictive modeling, and unsupervised learning, covering methods such as regression, classification, clustering, dimensionality reduction, and model evaluation. It also addresses practical challenges commonly encountered in applied data analysis, including handling missing values, managing class imbalance, ensuring reproducibility, and interpreting results critically.

A strong emphasis is placed on bridging theory and practice: each concept is accompanied by illustrative examples, case studies, and opportunities for hands-on exploration. Learners will be exposed to how advanced data analysis techniques are applied across different domains, from healthcare to finance and social sciences, highlighting the transformative role of data in research and industry.

By the end of the course, students will:

Acquire methodological knowledge for conducting advanced analyses on structured datasets.
Be able to critically interpret and validate results using statistical reasoning and evaluation metrics.
Develop skills in applying data analysis tools to real-world problems.
Gain awareness of the limitations, ethical aspects, and best practices for responsible data analysis.

This module equips participants with the competencies necessary to work independently on data-driven projects and lays the foundation for further specialization in fields such as artificial intelligence, machine learning, and computational modeling.

Target audience

Postgraduate students and professionals with previous coursework on Data Analysis and familiar with coding (basic programming skills, Python).

Intended Learning Outcomes

ILO4	Software Tools	To describe and explain AI software languages and formulation of the algorithms. Be able to use state-of-the-art/most recent deep learning framework and an open-source neural network software to solve demonstrative problems on small datasets.
ILO5	Data Representation and Processing/Symbolic AI	To describe and explain general theories and concepts related to knowledge representation, to prepare, pre-process and analyse different types of data before applying any AI approach to solve problems. Be aware of common data format and quality issues and be able to solve them and adapt this knowledge to the task.
ILO6	Ethical, Privacy and Security Aspects	To understand the complex ethical considerations of AI and the special privacy and security related aspects of its development and application.