Financial Data Analysis -Practical

Paper Code: 
24DFSG804
Credits: 
02
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 804

Financial Data Analysis -Practical (Practical)

 

CO241: Process, organise, clean and validate financial Data

CO242: Use Graphical and Visualization tools to present financial and non-financial data

CO243: Conduct Financial Statement Analysis

CO244: Apply Equity Valuation Models

CO245: Conduct Investment Analysis for designing investment profile

CO246: 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

  • 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:

 

Reference Journals:

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

 

 

Academic Year: 
2024-2025
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