Data Analytics — accfinmsc.com

Module Description

The aim of the course is to understand and apply modern data analysis methods to support business decisions. After successful completion of the course, students will be able to apply data preprocessing and management techniques, create effective visualizations with Power BI and R, perform descriptive and exploratory analysis, create predictive regression and unsupervised learning models, apply basic artificial intelligence, text and network analysis techniques with Python and finally write professional reports and make informed decisions based on data. The Role of Large Language Models (LLMs) in contemporary data analytics is also covered.

Learning Outcomes

Upon successful completion, students will be able to:

Formulate and estimate linear regression models incorporating both qualitative and quantitative variables, and interpret estimated coefficients in applied economic and financial contexts.
Apply contemporary approaches to model selection and estimation, including regularization methods, resampling techniques, and shrinkage estimators, to address issues of overfitting and multicollinearity in regression analysis.
Use appropriate programming languages to perform statistical estimation, data manipulation, and visualization, producing clean and reproducible analytical workflows for real-world datasets.
Engage with published empirical research by replicating key findings, critically assessing methodological choices, and evaluating the validity and generalizability of reported results.
Implement data analytics tasks via Large Language Models (LLMs) for decision making.

Module Outline

12 thematic units across the semester.

The indicative module outline is as follows:

Introduction to Data Analytics: Types of data analytics, Introduction to Data Analytics tools: PowerBI, R, and Python, Case Study: Prioritization of Audit Areas Based on Risk (risk-based auditing).

Data Management and Sampling: Types of Data, Sampling Techniques, Management of Large-Scale Data (Big Data), Case Study: Analysis of Historical Financial Data to Detect Unusual Changes, Case Study: Statistical Sampling of Transactions for Invoice Auditing, practical applications in the R programming language.

Data Preprocessing and Big Data: Data Issues: Outliers, Missing Data, Detection of Problematic Data, Special Topics in Big Data Preprocessing, Case Study: Analysis of Historical Financial Data to Detect Unusual Changes, Case Study: Identification of Missing Information in Documents, practical applications in the R programming language.

Visualization and Descriptive Statistics: Principles of Effective Visualization, Types of Charts and Their Use, Descriptive Measures, Data Distributions, Storytelling and Descriptive Statistics, Case Study: Audit Dashboards for Key Audit Indicators (KAIs), applications in PowerBI.

Exploratory and Correlation Analysis: Exploratory Data Analysis (EDA), Correlation and Causality, Correlation Coefficients (Pearson, Spearman), Correlation Matrices and Heatmaps, Chi-square Test for Categorical Variables, Case Study: Testing the Relationship between Sales and Receivables, practical applications in the R programming language.

Clustering and Unsupervised Learning: Supervised and Unsupervised Learning, K-means Clustering Algorithm, Hierarchical Clustering, DBSCAN for Non-Spherical Clusters, Case Study: Supplier Segmentation Based on Transaction Behaviour, applications in the Python programming language.

Regression Analysis: Simple Linear Regression, Panel Data Regression, Issues in the Use of Regression, Case Study: Estimation of Expected Expenses Based on Historical Data, practical applications in the R programming language.

Artificial Intelligence and Machine Learning I: Introduction to Artificial Intelligence, Supervised and Unsupervised Learning, Applications of Neural Networks, Case Study: Prediction of Fraud Probability in Transactions, practical applications in the Python programming language.

Artificial Intelligence and Machine Learning II: Natural Language Processing (NLP) Techniques, Transformer Technology, Large Language Models (LLMs), Processing of unstructured data, Retrieval Augmented Generation (RAG), Model Context Protocol (MCP) servers, Agentic AI and validation mechanisms, Case Study: Identification of Keywords in Corporate Reports that Indicate Risk, practical applications in the Python programming language.

Network Analysis: Basic Concepts of Graph Theory, Types of Networks, Network Analysis Metrics, Case Study: Analysis of Relationships between Supplier Companies and Executives, practical applications in the R programming language.

Writing Analytical Reports and Decision Making: Structure of a Professional Report, Executive Summary, Presentation of Results to Different Stakeholder Groups, Data Visualization in Reports, Case Study: Presentation of Findings to Management and the Audit Committee, applications in PowerBI.

Assessment

Description of the assessment process

Assessment Language, Assessment Methods, Formative or Summative, Multiple Choice Test, Short Answer Questions, Essay Development Questions, Problem Solving, Written Assignment, Report/Report, Oral Examination, Public Presentation, Laboratory Paper, Clinical Patient Examination, Artistic Interpretation, Other/Other

Explicitly defined assessment criteria and if and where they are accessible by students are mentioned.

The module assessment language is in English and students are expected to exhibit the required level of proficiency.

The assessment of the course consists of:

Midterm Exam (40%, problem solving)

Coursework (10%, report)

Final exam (50%, problem solving)

The evaluation criteria across modes of assessment include the following:

Demonstration of key knowledge related to the content of course

Demonstration of an ability to apply the knowledge in a given problem or case study

Critical ability evident in applying appropriate methods/knowledge in a given case and/or developing theory-based and literature-based arguments.

Structure and presentation

Use of English language

More detailed assessment criteria will be provided to you in the module handbook document or posted on the course webpage, if deemed necessary.

Suggested Bibliography

Camm, J.D., Cochran, J.J. Fry, M.J., Ohlmann, J.W. and Anderson, D.R. (2022), Business Analytics, Cengage Learning.
Clarke, E. (2022), Everything Data Analytics-A Beginner's Guide to Data Literacy: Understanding the Processes That Turn Data Into Insights, Kenneth Michael Fornari.
Kelly, N. (2021), Delivering Data Analytics: A Step-By-Step Guide to Driving Adoption of Business Intelligence from Planning to Launch, Kogan Page.
Levine, D. and Stephan, D. (2022), Even You Can Learn Statistics and Analytics: An Easy to Understand Guide, Addison-Wesley Professional.
Provost, F. and Fawcett, T. (2013), Data Science for Business, O’Reilly Media, Inc.
Other library sources, including journal articles accessible through the Library, as assigned by the instructor.