MSc Business Analytics: Operational Research and Risk Analysis

This course offers a beginner-friendly introduction to Python for data analytics. Students will explore key techniques such as data preprocessing, analysis, visualisation, machine learning, and optimisation, all implemented in Python. Designed for those with no prior programming experience, the course emphasises hands-on, practical learning. By the end of the course, students will be equipped to analyse data, generate insights, and tackle real-world business challenges using Python. Whether your goal is to enhance decision-making, gain actionable insights, or optimise operations, this course provides the essential skills and knowledge to excel.

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 equip students with essential Python programming skills tailored for solving complex business problems in Data Science, Machine Learning, and Optimisation. As a core component of the curriculum, this course ensures students acquire competency in coding, forming a strong foundation for advanced technical units in the program.

The course focuses on practical knowledge and real-world applications, going beyond general programming concepts. Students will learn to implement algorithms, utilise Python's extensive library ecosystem for data analysis, machine learning, optimisation, and decision support, and develop expertise in data preparation and visualisation —skills highly sought after in the current job market.

Lab sessions provide ample opportunities for hands-on practice and formative feedback, helping students refine their programming skills and apply them to business challenges. The ultimate goal is to develop versatile data analysts who are well-prepared to meet the demands of the industry and leverage Python effectively for business applications.

Syllabus (indicative curriculum content):

Pre-course Self-Learning:

Chapter 1: Python Basics from https://www.datacamp.com/courses/intro-to-python-for-data-science

Learn Python in 1 hour! from https://www.youtube.com/watch?v=8KCuHHeC_M0

1. Data Structures, Conditionals, and Loops

Learn to use conditional logic and loops to create dynamic Python programs.

2. Functions, Modules, and Exceptions

Master reusable functions, modular programming, and error handling with exceptions.

3. Object-Oriented Programming (OOP)

Understand the basics of classes, objects, inheritance, and encapsulation.

4. Advanced OOP, Nested Structures, and Function Arguments

Explore advanced OOP concepts, nested data structures (shallow/deep copies), and function arguments.

5. Numerical Computing with Python

Use NumPy for mathematical operations, matrix manipulation, and numerical problem-solving.

6. Working with Data in Python

Learn to work with Pandas DataFrames for data manipulation and analysis. Perform operations such as filtering, grouping, merging, and summarising datasets.

7. Data Visualisation

Create clear and informative visualisations using Matplotlib. Learn to plot common chart types and customise visuals for effective data presentation.

8. Data Cleaning, Transformation and Feature Engineering

Focus on cleaning and preparing model-ready data through transformation (encoding/scaling), basic reduction, and feature engineering.

9. Introduction to Machine Learning

Understand the fundamentals of machine learning, including supervised learning and unsupervised learning.

10. Optimisation with Python

Solve optimisation problems using Python libraries.

The course will be delivered in Semester 1 on a weekly basis. Each week will consist of a two-hour lecture and a one-hour lab session, where the knowledge obtained in the lectures is converted into practical experience. The lectures and lab sessions are all face-to-face. Students will receive formative feedback from the teaching staff and peers on their understanding and application of the taught material.  

The module will have a GTA; the GTA will assist in the lab sessions and via the course unit discussion board. Quizzes, supporting material, and short videos of the main concepts learnt are provided for each week on the course home page. Formative feedback is also available for the lecture sessions and made available during the sessions as well as before and after primarily through discussion forums on the course home page.  

It is paramount that students look at the provided material prior to the lectures/labs to avoid getting lost during the delivery as well as make learning as efficient as possible by asking questions on topics requiring clarity.

Learning a programming language and being able to apply it to tackle business analytics problems is like learning and using an actual new language. The only way this can be achieved is by sufficient practice. You have 10 intense weeks – with each lecture and lab session builds on the material taught the previous weeks – to get up to speed with Python, but it will be worth it as it is a well-sought skill on the job market (convince yourself!) that you must mention on your CV (once you obtain it).  

 

Explain core Python programming concepts, including data structures, control flow, functions and object-oriented programming.

Apply fundamental programming principles in Python to structure code for analytical tasks.

Design structured solutions to data-driven problems by selecting appropriate Python-based methods.

Evaluate the suitability and performance of analytical methods and models for business problems.

Apply Python techniques to implement data preprocessing and analytical methods, including basic machine learning and optimisation.

Interpret and communicate analytical results using appropriate visualisations and clear technical explanations.

Apply structured problem-solving and synthesise information from data and problem contexts to support decision-making.

Group Coursework 60% 
Exam 40%

Students will be given a mark for the examination within 15 working days of the exam. (Given that this mark is only known after the course has finished, the mark that they receive will be the only feedback for the examination. Additional feedback or discussion can also be offered in office hour).

Feedback in form of written comments for the coursework will be provided within 15 working days of the submission deadline.  

 

Core texts:

Python manual - https://www.python.org/doc/

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

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

S. Guido, A. Müller. Introduction to Machine Learning with Python: A Guide for Data Scientists. O'Reilly Media, 2016.

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. http://www.scipy.org/, 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.