Data Analytics for Accounting and Finance - MAN00041I

Department: The York Management School
Credit value: 20 credits
Credit level: I
Academic year of delivery: 2026-27

Professional requirements

N/A

Related modules

Pre-requisite modules

Module will run

Occurrence	Teaching period
A	Semester 1 2026-27

Module aims

The module aims to introduce students to the use of Microsoft Excel and Python with accounting and finance applications. The module encourages a hands-one approach, and it is based on examples relevant to accounting and finance. Data will come from a range of sources including the databases available at the School for Business and Society (e.g., LSEG Data & Analytics, Compustat, CRSP, CSMAR etc.).

Module learning outcomes

After successful completion, the student should be able to:
- Develop proficiency in using Excel spreadsheets (sorting and filtering data, use and apply XLOOKUP and VLOOKUP formula, and Pivot tables)
- Create effective financial models and reports using Excel spreadsheets
- Acquire basic programming skills in Python for data manipulation, analysis and automation.
- Develop proficiency in integrating Python with Excel
- Create compelling visualizations of business data using tools available in both Excel spreadsheets and Python.
- Apply statistical techniques to analyse financial and business data trends and patterns using tools available in both Excel and Python.
- Apply data analytics skills to solve real-world problems and make informed business decisions.
- Understand and adhere to ethical standards in data analysis.
- Ability to clearly communicate results and conclusions from data analysis.

Academic and graduate skills:
- Problem-solving: ability to analyse business data and derive meaningful insights.
- Critical Thinking: proficiency in handling and interpreting quantitative data.
- Technical Proficiency: proficiency in Excel and Python for financial modelling and analysis, along with basic Python programming skills for financial data manipulation and automation.
- Communication Skills: ability to create effective financial models and reports.
- Decision-Making: application of financial analytics for informed business decisions and implement learned concepts in practical, industry-relevant scenarios.
- Adaptability: ability to adapt to evolving technologies and methodologies in the field of financial analytics.

Module content

Introduction to Excel environment and spreadsheet modelling
Describe and visualise data in Excel
Financial Formulas, XLOOKUP and VLOOKUP formula
Sorting and filtering data and Pivot Tables
Introduction to coding in Python
Cleaning and managing data in Python
Describe and visualise data in Python
Integrate Python and Excel
Application of regression analysis in Python and Excel
Introduction to time series analysis and forecasting in Python and Excel
Applications in Data Analytics with Python and Excel

Indicative assessment

Task	% of module mark
Essay/coursework	70.0
Oral presentation/seminar/exam	30.0

Special assessment rules

None

Indicative reassessment

Task	% of module mark
Essay/coursework	70.0
Oral presentation/seminar/exam	30.0

Module feedback

Feedback will be given in accordance with the University Policy on feedback in the Guide to Assessment as well as in line with the School policy.

Indicative reading

Benninga, S., Mofkadi, T. (2022). Financial Modeling, Fifth Edition. United States: MIT Press.
Camm, J. D., Cochran, J. J., Fry, M. J., Ohlmann, J. W. (2020). Business Analytics. United Kingdom: Cengage Learning.
Hilpisch, Y. (2018). Python for Finance: Mastering Data-Driven Finance. United States: O'Reilly Media.
McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. United States: O'Reilly Media.
Zumstein, F. (2021). Python for Excel. United States: O'Reilly Media.