Module manager: Vladimir Pazitka
Email: v.pazitka@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2026/27
LUBS1275 Mathematics and Statistics for Economics and Business 1A OR A-level Maths Grade B
| LUBS1285 | Mathematics and Statistics for Economics and Business 1B |
| LUBS1630 | Introductory Statistics for Business |
This module is not approved as a discovery module
Statistics and Data Analytics for Finance introduces you to the essential statistical tools and analytical techniques used throughout modern finance. Whether you are interested in investment analysis, corporate decision making, risk management or financial modelling, the ability to understand and work with quantitative information is a fundamental professional skill. This module gives you the foundation you need to succeed in more advanced finance modules and to stand out in an increasingly data driven job market. You will be introduced to differential calculus and develop a strong grasp of key statistical concepts such as probability distributions, sampling, estimation, and hypothesis testing. These concepts form the basis of quantitative reasoning and support many of the models and techniques you will encounter later in your degree. Building this understanding early ensures you can confidently navigate financial data and recognise the strengths and limitations of different forms of statistical evidence. Alongside this conceptual foundation, the module offers hands on experience with real-world financial datasets. Through practical workshops, you will learn how to construct datasets, prepare them for analysis, and carry out statistical procedures using appropriate software. You will also produce visualisations, tables and other analytical outputs that communicate your findings clearly and professionally. These skills are vital in academic assessments and widely sought after by employers in banking, accounting, consulting and financial services. A key focus of the module is developing your ability to use statistics responsibly. You will learn how to evaluate the quality of quantitative claims, assess uncertainty, and identify common pitfalls in financial analysis. By the end of the module, you will be able to produce well reasoned, evidence based insights that demonstrate both technical competence and critical judgement. Overall, this module offers a practical and intellectually engaging introduction to statistical thinking in finance, equipping you with the analytical skills required for success throughout your degree and future career.
Statistics and Data Analytics for Finance is designed to develop students’ mastery of foundational statistical concepts, introduce them to differential calculus, and develop their ability to conduct rigorous, evidence based analysis of financial data. The module is structured around four interconnected objectives that correspond directly to the subject specific learning outcomes and guide the design of lectures, workshops, and analytical tasks.
The first objective is to establish students’ command of core concepts in statistics, probability, and differential calculus.
These concepts form the basis of quantitative reasoning in finance and support later study in areas such as forecasting, risk measurement, and financial modelling. Lectures introduce key components—probability distributions, sampling frameworks, estimation methods, hypothesis testing, differential calculus—while structured exercises provide opportunities to practise the execution of these methods. This objective underpins Learning Outcome 1, ensuring that students can demonstrate accurate and confident use of statistical principles in financial settings.
The second objective is to develop students’ competence in constructing and preparing financial datasets for analysis.
Workshops and computer based sessions guide students in assembling datasets, ensuring data quality, selecting appropriate variables, and preparing data for valid statistical investigation. These activities support Learning Outcome 2 by enabling students to produce well structured datasets suitable for rigorous statistical work.
The third objective is to build students’ technical proficiency in carrying out statistical analyses and producing valid analytical outputs.
Through supervised practical sessions, students learn to execute statistical techniques using appropriate software and to generate tables, visualisations, and summary statistics that meet professional standards. This objective aligns directly with Learning Outcomes 3 and 4, reinforcing the need to produce clear and correctly specified statistical outputs that communicate patterns in financial data.
A fourth objective is to cultivate students’ ability to evaluate statistical evidence critically and communicate their analytical findings effectively.
Students examine examples from financial reporting and academic practice to assess assumptions, limitations, uncertainty, and the robustness of claims. This objective strengthens Learning Outcome 5 by promoting disciplined evaluation of quantitative evidence, and Learning Outcome 6 by supporting the development of coherent written explanations grounded in statistical reasoning.
Collectively, these objectives ensure that Statistics and Data Analytics for Finance functions as both an introduction to statistical reasoning and a foundation for more advanced analytical work within the programme. The combination of conceptual teaching, software based analysis, and structured opportunities for critical evaluation supports students in attaining the level of statistical mastery required for subsequent modules and for data driven financial practice.
Upon successful completion of this module, students will be able to:
1. Demonstrate accurate command of foundational concepts in statistics, probability, and differential calculus—including probability distributions, sampling, estimation and hypothesis testing—when working with financial data.
2. Construct and prepare quantitative and financial datasets suitable for statistical analysis, ensuring data quality, appropriateness of variables, and reproducibility of analytical steps.
3. Execute statistical techniques using appropriate software to analyse financial datasets and produce valid, well structured statistical outputs.
4. Develop clear, correctly specified data visualisations that effectively communicate properties, patterns, and relationships in financial data.
5. Evaluate the validity and limitations of statistical evidence in financial contexts by assessing assumptions, reliability, uncertainty, and the robustness of analytical conclusions.
6. Synthesize statistical findings into coherent written explanations that demonstrate mastery of core statistical reasoning and its relevance for financial decision making.
Upon completion of this module students will be able to:
1. Use digital tools confidently and accurately to organise, process and analyse financial datasets, demonstrating emerging proficiency in statistical software and spreadsheet based workflows.
(Leeds Skills Matrix: Digital Proficiency; Information & Data Literacies)
2. Communicate statistical findings clearly and professionally through well structured written, numerical and visual formats suitable for academic, managerial and investor-facing audiences.
(Leeds Skills Matrix: Academic Communication; Professional Communication)
3. Apply structured problem solving approaches to diagnose data issues, select appropriate statistical techniques, and generate evidence based conclusions that withstand critical scrutiny.
(Leeds Skills Matrix: Problem Solving; Critical Thinking)
4. Work independently and reflectively to plan analytical tasks, monitor progress, and evaluate the quality and reliability of outputs, demonstrating responsible and ethical use of quantitative evidence.
(Leeds Skills Matrix: Self Management; Academic & Ethical Integrity)
Indicative:
- Probability concepts and distributions
- Statistical inference and hypotheses testing
- Differential Calculus
- Data management
- Analysing financial statistics and time series
- Interpreting and communicating statistical evidence
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Workshop | 5 | 2 | 10 |
| Lecture | 10 | 1 | 10 |
| Independent online learning hours | 10 | ||
| Private study hours | 70 | ||
| Total Contact hours | 20 | ||
| Total hours (100hr per 10 credits) | 100 | ||
Throughout this module, students will have regular and structured opportunities to receive formative feedback that supports their learning and prepares them for the assessed coursework. Bi-weekly workshops provide the primary environment for hands on practice with statistical techniques, data preparation, and software based analysis. During these sessions, students receive immediate verbal feedback from staff as they work through analytical tasks, enabling them to identify errors, refine their approach, and build confidence in applying statistical methods.
In addition to workshop based feedback, students will complete short, low stakes online activities such as knowledge checks, mini quizzes, and guided exercises designed to reinforce key concepts from lectures. Automated and tutor provided feedback will help students monitor their progress and address any gaps in understanding before moving on to more advanced material.
A central aspect of the module’s formative support is guiding students through the step by step process of conducting statistical analysis for their assessed coursework. This includes feedback on tasks such as assembling a dataset, selecting appropriate statistical tools, generating visualisations, interpreting outputs, preparing and drafting a report. By engaging with each stage of the analytical workflow in a supported environment, students will be well prepared to produce rigorous, well reasoned work in their final assessment.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Assignment | 2,000 words | 100 |
| Total percentage (Assessment Coursework) | 100 | |
The coursework requires students to build their own dataset, perform descriptive analysis, present findings, visualise data, and write a report including financial statistics. The resit for this module will be 100% by 2,000 word assignment.
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
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