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 196: Examine the tools of financial data and classify the data in finance and costing CO 197: Examine Processing, organising, cleaning and validation of financial Data CO 198: Compare different Graphical and Visualization tools to present financial and non-financial data CO 199: Analyse time series visualization and trend analysis techniques CO 200: Examine Financial Modelling, forecasting and Investment Analysis techniques |
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 |
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
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
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
Understanding time series data and its characteristics
Time series visualization and trend analysis
Seasonality and cyclical patterns
Introduction to forecasting techniques
Introduction to portfolio theory
Measuring Risk and return
Technical analysis and indicators
Sentiment analysis techniques for investor sentiment measurement
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.
SUGGESTED REFERENCE BOOKS
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-RESOURCES:
http://ww1.shodhganga.com/ [2]
https://shodhgangotri.inflibnet.ac.in/ [3]
https://www.scopus.com/home.uri [4]
http://www.e-book.com.au/freebooks.htm [5]
https://www.doaj.org/ [6]
REFERENCE JOURNALS:
Journal of Financial Econometrics, Oxford University Press
Journal of Financial Data Science, Global Association of Risk Professionals
Links:
[1] https://finance.iisuniv.ac.in/courses/subjects/financial-data-analysis-0
[2] http://ww1.shodhganga.com/
[3] https://shodhgangotri.inflibnet.ac.in/
[4] https://www.scopus.com/home.uri
[5] http://www.e-book.com.au/freebooks.htm
[6] https://www.doaj.org/
[7] https://finance.iisuniv.ac.in/academic-year/2023-2024