Data Science In Practice

The Data Science in Practice module provides students with an opportunity to apply their knowledge through project work. They will select a project from a specific domain and appropriately apply exploratory data analysis, statistical methods and select appropriate advanced modelling techniques such as regression analysis, machine learning algorithms or . This module also develops your scientific communication skills through the preparation of project reports and presentations.

Reading List


  • Gao, G., Mishra, B., & Ramazzotti, D. (2018). Causal data science for financial stress testing. J. Comput. Sci., 26, 294-304.
  • Chen, H., Lundberg, S.M., & Lee, S. (2018). Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data. ArXiv, abs/1801.07384.

(Machine Learning):

  • Miller, James D. and Rui Miguel Forte. Mastering Predictive Analytics with R: Machine Learning Techniques For Advanced Models. Second Ed. Birmingham: Packt, 2017.
  • Fuentes, Alvaro. Mastering Predictive Analytics with Python. Birmingham: Packt, 2018.

(Data Analytics in Business):

  • Hands-On Exploratory Data Analysis with R: Become an Expert in Exploratory Data Analysis Using R Packages, Radhika Datar and Harish Garg, 1st Edition. (Packt Publishing, 2019). 266 pages
  • Hands-On Exploratory Data Analysis with Python: Perform EDA Techniques to Understand, Summarize, and Investigate Your Data, Suresh Kumar Mukhiya and Usman Ahmed, 1st Edition. (Packt Publishing, 2020). 352 pages.
    Application requirements

    Candidates who apply for this course must have a recognised undergraduate degree or equivalent. Candidates without a degree but with other relevant qualifications and/or work experience can also be considered.


    English language competency at an IELTS 6.5 (or equivalent) is required of all applicants whose first language is not English. Where students can demonstrate previous substantial studies or work experience in English, this requirement can be waived.


  • Accreditation: Swiss Private Course
  • Total workload: 150 hours
  • Requires extra purchases (outside texts, etc.): Yes, purchases required
  • ID verification: Required
  • Admission requirements: Application required
  • Minimum education requirement for students: Undergraduate