Financial Data Analysis -Practical

Paper Code: 
24DFSG814
Credits: 
2
Contact Hours: 
30.00
Max. Marks: 
100.00
Objective: 

This course will enable students to apply data analysis techniques, financial statement analysis, equity valuation models, and investment analysis methods using tools like Excel/Python/R for informed decision-making in finance.

 

Course Outcomes: 

Course

Learning outcome

(at course level)

Learning and teaching strategies

Assessment Strategies

Course Code

Course

Title

 

24DFSG 814

Financial Data Analysis -Practical (Practical)

 

CO301: Process, organise, clean and validate financial Data

CO302: Use Graphical and Visualization tools to present financial and non-financial data

CO303: Conduct Financial Statement Analysis

CO304: Apply Equity Valuation Models

CO305: Conduct Investment Analysis for designing investment profile

CO306: Contribute effectively in course-specific interaction

 

Approach in teaching: Interactive Lectures, Discussion, Tutorials, Practical cases, Power point presentation.    

 Learning activities for the students:                              Self learning assignments, Effective questions, Seminar presentation, Live practical problems analysis

CA test, Semester end examinations, Solving problems in tutorials,  Presentations.

 

6.00
Unit I: 
Data analysis techniques and tools

• Introduction to data analysis techniques and tools (Excel/Python/R)
• Data cleaning and preprocessing techniques
• Handling missing data and outliers

6.00
Unit II: 
Data presentation: Visualization and graphical Presentation

• Dashboard, graphs, Diagrams, Tables Report Design
• Application of Tools and Techniques of Visualization and Graphical Presentation

6.00
Unit III: 
Financial Statement Analysis

• Introduction to financial statements (balance sheet, income statement, cash flow statement)
• Ratio analysis for financial statement interpretation
• Assessing profitability, liquidity, and solvency
• Comparative analysis and benchmarking

6.00
Unit IV: 
Equity Valuation Models

• Introduction to equity valuation
• Discounted cash flow (DCF) analysis
• Relative valuation methods (P/E ratio, P/B ratio)
• Analyzing analyst recommendations and target prices

6.00
Unit V: 
Investment Analysis

• Risk and Return Analysis/Sentiment Analysis/ Technical Analysis

Essential Readings: 

• Albright, S. C., Winston, W. L., & Zappe, C. (2019). Data Analysis and Decision Making (6th ed.). Cengage Learning.
• Chen, C., & Zhang, L. (2020). Financial Data Analysis with Python: Analyzing, Visualizing and Modeling Financial Data with Python. Apress.
• Montgomery, D. C., Peck, E. A., & Vining, G. G. (2020). Introduction to Linear Regression Analysis (6th ed.). John Wiley & Sons.
• Ruey S. T., & Tsay, R. S. (2012). Analysis of Financial Time Series (3rd ed.). John Wiley & Sons.
• Sharma, J. (2020). Financial Analytics with R: Building a Laptop Laboratory for Data Science. Springer.

References: 

Suggested Readings:
• De Jong, E., & Wilmott, P. (2017). Financial Modeling and Valuation: A Practical Guide to Investment Banking and Private Equity. John Wiley & Sons.
• Greene, W. H. (2017). Econometric Analysis (8th ed.). Pearson.
• Johnson, R. A., & Wichern, D. W. (2020). Applied Multivariate Statistical Analysis (7th ed.). Pearson.
• Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer.
• McDonald, R. L. (2014). Derivatives Markets (3rd ed.). Pearson.

E-Contents:
• http://ww1.shodhganga.com/
• https://shodhgangotri.inflibnet.ac.in/
• https://www.scopus.com/home.uri
• http://www.e-book.com.au/freebooks.htm
• https://www.doaj.org/

Reference Journals:
• Journal of Financial Econometrics, Oxford University Press
• Journal of Financial Data Science, Global Association of Risk Professionals

Academic Year: 
2024-2025
  • Printer-friendly version
  • PDF version