MSc Data Science (Urban Analytics) / Course details

Year of entry: 2024

Course unit details:
Programming in Python for Business Analytics

Course unit fact file
Unit code BMAN73701
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Available as a free choice unit? No


Data analytics (including data preprocessing, analysis, visualisation, machine learning, and optimisation) with a focus on business applications using the Python programming language and without assuming any previous knowledge in programming.


BMAN73701 Programme Req: BMAN73701 is only available as an elective to students on MSc Business Analytics and MSc Data Science (except CSDI pathway)


The aim of this course is to introduce students to the fundamentals of Python, a general-purpose programming language widely used in the application of Data Science, Big Data Analytics and Optimization to business problems. The course will provide the skills for implementing your own algorithms as well as using the thousands of Python packages available for data analysis, modelling, inference, simulation, prediction, forecasting, visualisation, optimization and decision support. The lab classes will provide ample opportunity for students to practice their programming skills and obtain formative feedback. The course is focused on practical knowledge, examples and business applications for data analytics, rather than learning general programming concepts only. The course is very much hands-on with the ultimate goal of turning you into a versatile data analyst for business applications.

Learning outcomes

At the end of the course unit, students should be able to:

  • Read and write Python code and understand how to use Python packages.
  • Implement algorithms of moderate complexity in Python.
  • Understand the fundamentals of object-oriented programming using Python.
  • Understand how to implement simple data science and optimization algorithms from the literature to tackle business applications.
  • Develop their own algorithms to solve basic data science and optimization problems.
  • Use Python packages to solve complex data science, visualisation and optimisation problems in business and management (e.g., portfolio optimization, customer segmentation, and analysis of financial data).

The learning outcomes of the group work (coursework) will be improved skills in working as a group, and improved communication and presentation skills. These are practical skills that are transferable to team-focused work in general.

Teaching and learning methods

Formal Contact Methods

Minimum Contact hours: 20 

Delivery format: Lecture and Workshops 

Assessment methods

70% Group-based coursework (35% group report, 25% group presentation, 10% peer assessment)
30% (2x15%) Two online-tests


Feedback methods

  • Informal advice and discussion during a lecture, seminar, workshop or lab.

  • Online exercises and quizzes delivered through the Blackboard course space.

  • Responses to student emails and questions from a member of staff including feedback provided to a group via an online discussion forum.

  • Written and/or verbal comments on assessed or non-assessed coursework.

  • Written and/or verbal comments after students have given a group or individual presentation.

  • Generic feedback posted on Blackboard regarding overall examination performance.

Recommended reading

Core texts:

Python manual - 

A.B. Downey. Think Python: How to Think Like a Computer Scientist. O’Reill, Media, Inc., 2012.

W. McKinney. Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. O'Reilly Media, Inc., 2012.

The course draws material from various sources but these three sources provide a nice overview of all the topics covered in the module.

Supplementary Texts:

E. Jones, E. Oliphant, P. Peterson, et al. SciPy: Open Source Scientific Tools for Python., 2001-.

C.H. Papadimitriou and K. Steiglitz. Combinatorial optimization: algorithms and complexity. Courier Corporation, 1982.

C. Reeves and J.E. Rowe. Genetic Algorithms: Principles and Perspectives – A Guide to GA Theory. Kluwer Academic Publishers, 2003. 

Study hours

Scheduled activity hours
Lectures 22
Practical classes & workshops 10
Independent study hours
Independent study 118

Teaching staff

Staff member Role
Xian Yang Unit coordinator
Fanlin Meng Unit coordinator

Additional notes


  • This module is an elective for MSc Business Analytics and MSc Data Science students

Informal Contact Method

  • Office Hours
  • Online Learning Activities (blogs, discussions, self assessment questions)

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