Financial Analytics Course

Get about, CRISP - ML(Q) the perfect Project Management Methodology used for handling Data Mining projects. Understand the entire process flow including Business Problem definition, Data Collection, Data Cleansing, Feature Engineering, Feature Selection, Model Building, Deployment and Maintenance. Get introduced to the principles of big data and learn about the opportunities being created. Understand about how Data is generation and explosion of data, Innovations in the space of analytics. Learn how to distinguish between data types, Exploratory data analysis, the Various moments of Business decisions and various Graphical techniques. Learn about probability and probability distribution namely Z distribution and Student's t-distribution.

Learn about Hypothesis testing, the many Hypothesis testing Statistics, work with the Null Hypothesis & Alternative hypothesis and Types of hypothesis testing. Interpret the results of Hypothesis test and probabilities of Alpha error, understand Type I and Type II errors. Get introduced to Linear regression, various components of Linear regression viz regression line, Linear regression equation, the concept of Ordinary Least Square. Get introduced to Linear regression analysis, and Linear regression examples

Understand the Linear regression in a multivariate scenario, understand collinearity and how to deal with it. Get introduced to the analysis of Attribute Data, understand the principles of Logistic regression, Binary Logistic regression analysis. Learn about the Multiple Logistic regression, Probability measures, and its interpretation. Get clarity on the confusion matrix and its elements. Get introduced to “Cut off value” estimation using AUC and ROC curve, understand False Positive Rate, False Negative Rate, Sensitivity, Specificity. Gain a birds-eye view to various advanced regression techniques and analysis of count data namely Poisson regression, Negative binomial regression. Learn when to use Poisson regression and negative binomial regression for predicting count data.

Understand about the various sectors and domains with FSI industry. Basic terminologies of Finance Management course to ensure everyone comes up to speed. World overview of challenges faced by companies in FSI. Understand about Data Analytics applications in Stock Market Investments, Banking, Financial Advisory and Management, Electronic Payment Services, etc.

Learn about Financial Data Analytics with respect to Data Science in FSI, Machine Learning in FSI, AI in FSI, etc. Key areas in finance where AI is applicable would be discussed, namely about, Algorithmic Stock Trading, Automated Robo-Advisors, Fraud Detection and Prevention.

Learn about the preliminaries of Data Optimization & Data Simulation which serves as a good reference for people to understand the core Financial Analytics concepts. Learn on solving Linear Programming problems using Simplex techniques, Duality and Sensitivity Analysis.

Understand about investments, how to trade consumption across time, a trade-off between risk and return, decision making under uncertainty, indifference curves, quadratic utility function, etc.

Understand about Capital Allocation Line (CAL) and Optimal Portfolio. Learn about Lending and Borrowing on the CAL, highest utility allocation, feasible set of investment opportunities, optimal allocation, analysis using indifference curves, etc.

Understand about portfolio math, diversification in action by taking an example of two risky assets, mean-standard deviation diagram, mean-variance frontier, efficient frontier, two funds separation, constraints on investment, diversification in well-diversified portfolios, tangency, MVE, etc.

Features of Markowitz analysis, expected returns from historical averages, efficient frontier, resampling based on simulation, estimation error, means and covariances, asset pricing models, market portfolio, pricing model, security market line, equilibrium, disequilibrium, full equilibrium, BU & TU price determination, Beta value etc.

Understand about various European Call Options and application of Monte Carlo method of pricing the option to demonstrate the equivalence with the Black-Scholes formula. Learn about the Monte Carlo method, which will enable us to price options that are not the plain vanilla European calls or puts.

Learn on Predictive modelling for Stock Market Movement, Detecting Financial Statements Fraud, Personal Loan default prediction model, Credit Score prediction model, Credit Card fraud detection model, etc. Understand about the importance of interpretation in FSI industry and emphasis of regulatory bodies on the same.

Learn on how to automatically scan the document and find sections of first name, last name, etc., and then place it in a structured database, thereby reducing the human intervention. Learn on how to rest the bank password automatically using AI-driven chatbot – Rasa NLU.

Learn about various regulatory bodies such as Basel III, SEC, SROs, GLBAct, etc. Also learn about GDPR Act, safeguarding PII and many more regulatory norms laid down for various countries. Understand about difference between data security and data privacy. Understand about Connected Banks, Fly-in Financial Markets, Financial Asset Exchange, Open Banking and Data Sharing.