Financial Data Analysis

Paper Code: 
24DFSG803
Credits: 
02
Contact Hours: 
30.00
Max. Marks: 
100.00
Objective: 

This course will enable students to utilize data science techniques for financial decision-making, including data processing, visualization, analysis, and modeling, enhancing their ability to evaluate investments effectively and ethically.

Course Outcomes: 

 

Course

Learning outcome

(at course level)

Learning and teaching strategies

Assessment Strategies

Course Code

Course

Title

 

24DFSG 803

Financial Data Analysis

(Theory)

CO235: Examine the tools of financial data and classify the data in finance and costing

CO236: Examine Processing, organising, cleaning and validation of financial Data

CO237: Compare different Graphical and Visualization tools to present financial and non-financial data

CO238: Analyse time series visualization and trend analysis techniques

CO239: Examine Financial Modelling, forecasting and Investment Analysis techniques

CO240: 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,  Seminar presentation.

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

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6.00
Unit I: 
Data Science for Financial Decision-Making
  • Meaning, Nature, Properties and Scope of Data
  • Types of Data in Finance and Costing
  • Digitalization of data and Information
  • Transformation of data to decision relevant information
  • Communication of Information for quality decision Making
  • Professional Scepticism regarding data
  • Ethical use of data and information
  • Data Processing, organisation, cleaning and validation :
  • Development of data processing
  • Functions of data processing
  • Data organisation and Distribution
  • Data cleaning and validation

 

6.00
Unit II: 
Data presentation: Visualization and Graphical Presentation
  • Data Visualization of Financial and Non-Financial Data
  • Objective and Function of data presentation
  • Data Presentation Architecture
  • Dashboard, graphs, Diagrams, Tables Report Design
  • Tools and Techniques of Visualization and Graphical Presentation
6.00
Unit III: 
Data Analysis and Modelling
  • Process Benefits and Types of Data Analysis
  • Data mining and Implementation of Data Mining
  • Analytics and Modelling
  • Standards of data tagging and Reporting
  • Cloud Computing, Business Intelligence, Artificial Intelligence, Robotic Process Automation and Machine Learning
  • Model vs Data-Driven Decision Making

 

6.00
Unit IV: 
Time Series Analysis
  • Understanding time series data and its characteristics
  • Time series visualization and trend analysis
  • Seasonality and cyclical patterns
  • Introduction to forecasting techniques
6.00
Unit V: 
Investment Analysis and Evaluation
  • Introduction to portfolio theory
  • Measuring Risk and return
  • Technical analysis and indicators
  • Sentiment analysis techniques for investor sentiment measurement
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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