SOAS University of London

School of Finance and Management

Data Analytics & Quantitative Business Analysis

Module Code:
FHEQ Level:
Taught in:
Term 1

The business world has traditionally relied on accounting and finance practitioners to provide a summary of business performance in form of financial statements at regular intervals. Also, quantitative business intelligence has traditional being an important field in accounting and finance.

While these two roles continue to be important, there is marked change in their nature, frequency, and potentials given the exponential growth in unstructured data. Consistent with Moore's Law, there's also an exponential increase in computing power. The current business environment, therefore, requires managers to know data analysis and programming to make sense of the huge volume of data.

The module, therefore, complements other modules on the programme, such as financial accounting and financial statement analysis, management accounting, and applied econometrics.

Objectives and learning outcomes of the module

LO1) Understand emerging trends in Big data and data analytics
LO2) Understand the place of unstructured data and narratives in accounting and finance
LO3) Understand the types and uses of analysis
LO4) Understand and use basic Artificial intelligence (AI) and machine learning tools
LO5) Understand and use quantitative business analysis techniques such as decision tree analysis and simulation


  • Lectures: 2hrs per week
  • Tutorials: 1hr per week
  • Independent study: 121hrs (over 10 weeks)

Scope and syllabus

  • Week 1 Introduction to Big Data & Data Analytics
  • Week 2 Types, uses of and leveraging analytics
  • Week 3 Introduction to Python Programming 1
  • Week 4 Introduction to Python Programming 2
  • Week 5 Introduction to Machine Learning 1 - Supervised
  • Week 6 Introduction to Machine Learning 2 - Unsupervisised
  • Week 7 Introduction to Machine Learning 3 - Model Evaluation, Feature Selection & Hyperparameter Tuning
  • Week 8 Business Intelligence and Data Visualisation - (e.g.Excel, Plotly, Apache Superset, Metabase, Redash
  • Week 9 Decision Tree
  • Week 10 Simulation

Method of assessment

  • Exam: 2hrs (60%)
  • Project 1: Programming with Python (15%)
  • Project 2: Further Programming with Python (25%)

Suggested reading

  • Yuxi (Hayden) Liu (2020) - Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn, 3rd Edition Packt
  • Eric Matthes (2019) Python Crash Course. No Starch Press
Additional reading
  • ACCA (2020) Analytics in Finance & Accounting
  • Vic Anand, Khrystyna Bochkay, Roman Chychyla, Andrew Leone (2020) Using Python for Text Analysis in Accounting Research in Foundations and Trends® in Accounting


Important notice regarding changes to programmes and modules