Learning outcomes

At the end of this course, the student should be able to:

  • Explain the business context and the role of data analytics within it.
  • Apply the steps of the data analytics process (from data cleaning and processing to visualization and analysis) using Python.
  • Select and implement appropriate analytical models in Python to address business problems.
  • Evaluate when (and when not) to apply specific techniques, and critically assess the strengths and limitations of analytical methods.
  • Integrate all steps of the analytics process in Python into a coherent solution for a business case.

Goals

Data analytics is an interdisciplinary field at the intersection of business, computer science, and statistics. Its goal is to transform raw data into actionable insights that support better decision-making and create business value. This course equips students with practical skills to work through the full data analytics process: data understanding and visualization, preparation, modeling, and evaluation using Python.


The course emphasizes hands-on exercises and business-oriented case studies, allowing students to select and implement appropriate analytical models, apply descriptive and predictive methods, and critically evaluate their suitability and limitations. Applications such as customer churn prediction, and credit scoring illustrate the concepts, and students will develop the ability to integrate all steps of the analytics process into coherent, end-to-end analytics pipeline.

Content

  1. Introduction to data analytics
  2. Python coding
  3. Data understanding and visualization
  4. Data preparation and feature engineering
  5. Model evaluation and sampling
  6. Modeling: regression and classification

Teaching methods

Interactive "ex-cathedra" sessions during which students are exposed to various concepts of data analytics. Python coding examples are also provided to illustrate the practical usage of the theoretical concepts.


Practical exercise session in which the students should solve case studies in Python.


Assessment method

30% of the final mark:

  • Project work in group with written report and final presentation in which the student should solve a data analytics case from start to finish.  
  • The project cannot be redone in the second session and the grade remains the same as in the first session.


70% of the final mark:

  • A closed-book written exam covering theory and applications (including coding) of data analytics. The exam consists of both multiple choice and open-ended questions.


Sources, references and any support material

Baesens, B. (2014). Analytics in a big data world: The essential guide to data science and its applications. John Wiley & Sons.


Shmueli, G., Bruce, P. C., Gedeck, P., & Patel, N. R. (2025). Machine learnig for business analytics: concepts, techniques and applications in Python, 2nd edition. John Wiley & Sons.

Language of instruction

English
Training Study programme Block Credits Mandatory
Bachelor in Business Engineering Standard 0 4
Bachelor in Business Engineering Standard 3 4