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FINANCIAL DATA ANALYSIS
Course Outcomes (Cos):
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Course Outcomes |
Learning and teaching strategies |
Assessment Strategies |
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|---|---|---|---|
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On completion of this course, the students will be able to: CO241 Examine the tools of financial data and classify the data in finance and costing CO242 Examine Processing, organising, cleaning and validation of financial Data CO243 Compare different Graphical and Visualization tools to present financial and non-financial data CO244 Analyse time series visualization and trend analysis techniques CO245 Examine Financial Modelling, forecasting and Investment Analysis techniques |
Interactive Lectures, Group Discussion, Tutorials, Reading assignments, Workshops and question preparation |
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 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
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
Time Series Analysis
• Understanding time series data and its characteristics
• Time series visualization and trend analysis
• Seasonality and cyclical patterns
• Introduction to forecasting techniques
Investment Analysis and Evaluation
• Introduction to portfolio theory
• Measuring Risk and return
• Technical analysis and indicators
• Sentiment analysis techniques for investor sentiment measurement
SUGGESTED TEXT BOOKS
• 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/
• 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
