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Financial Data Analysis-PRACTICAL [1]

Paper Code: 
DFSG 804
Credits: 
2
Contact Hours: 
30.00
Max. Marks: 
100.00
Objective: 

Course Outcomes (Cos):

Learning outcome (at course level)

Learning and teaching strategies

Assessment Strategies

On completion of this course, the students will be able to:

CO 201: Process, organise, clean and validate financial Data

CO 202: Use Graphical and Visualization tools to present financial and non-financial data

CO 203: Conduct Financial Statement Analysis

CO 204: Apply Equity Valuation Models

CO 205: Conduct Investment Analysis for designing investment profile

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

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

Quiz, test, assignments and viva-voce

 

6.00

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: 

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.

Academic Year: 
2023-2024 [2]

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Source URL: https://finance.iisuniv.ac.in/courses/subjects/financial-data-analysis-practical-0

Links:
[1] https://finance.iisuniv.ac.in/courses/subjects/financial-data-analysis-practical-0
[2] https://finance.iisuniv.ac.in/academic-year/2023-2024