Syllabus for
Master of Science (Finance and Analytics)
Academic Year  (2023)

The School of Commerce, Finance, and Accountancy at CHRIST Delhi NCR is a vibrant community of faculty and students who believes in developing global competencies in students through quality education, research, and continuous innovation. The School provides a proficient environment to nurture commerce professionals with a high level of knowledge and competence to contribute to society with commitment and integrity effectively. The School offers various UG and PG programs to develop the students into responsible global citizens and leaders in economic, environmental, social, and cultural sustainability and well-being. The School takes pride in collaborating with globally eminent professional bodies such as ACCA, CISI (UK), IMA (USA), CIMA (UK), learning partners including GlobalFTI, EduEdge Pro and industry associations like Grant Thornton and Equity Levers in rendering professional value addition programs.

Vision: To be a center of excellence in the realm of Commerce and Management, developing and nurturing global competencies in students through quality education, research and continuous innovation

Mission: To nurture commerce professionals who possess a high level of knowledge and competence to effectively contribute to society with commitment and integrity.

Objectives: 

1. To enable the students, apply functional Knowledge, Skills and Attitude (KSA) while solving critical business problems;

2. To train the students, work in diversity and contemporary global business environment with socially responsible behavior and moral ethos;

3. To enhance entrepreneurial capabilities;

4.   

Continuous improvement and innovation is key for success and growth to any business. In fast changing digital world today business leverages, the vast volume of data available to take decisions to improve and innovate, especially in finance. Data has changed the way a business takes decisions. Today almost every business, irrespective of size, uses analytics tools to leverage data for growth. With data analytics business are better equipped with information. However, it takes the knowledge and expertise of a skilled data analytics professional to effectively analyse this data. The current financial industry such as the Big fours, financial institutions, multinational banks, insurance companies, mutual fund companies, finance and leasing companies, stockbroking firms, investment firms, banks etc are heavily relying on big data for informed investment decisions. Thus there is a high demand for professionals with a specialisation in finance and data analytics.  Thus the program aims to train and skill the aspirants with the knowledge and application in finance, investment and data analytics.

Programme Outcome/Programme Learning Goals/Programme Learning Outcome:

PO2: Apply the concepts, techniques, methods and approaches of business analytics using real time data.

PO3: Analyse and Interpret the results of real world financial and data related issues

PO4: Build the models for effective and strong decision making

PO5: Examine the implications of ethical and legal decision processes

Programme Specific Outcome:

Programme Educational Objective:

Students are evaluated for each course on the basis of written examination and continuous internal assessments. Each paper carries maximum of 100 marks and is evaluated as follows:

Course Description This Course provides the knowledge base for understanding the depth and breadth of the principles and handy techniques of contemporary marketing management. The primary objective is to provide a framework for understand the dimensions of new age marketing. Along with, the students will be getting familiar with the advanced concepts of the various branches of marketing management viz., Brand Management, Advertising Management and service marketing. Basic concepts on digital marketing and marketing analytics will enable the students to get acquainted with emerging trends of the marketing world

Course Objectives The course will familiarize the students with various theories, techniques to scan Marketing Environment and Modern Marketing Mix to develop their conceptual and analytical skills enabling them to manage marketing operations of a business firm.

Marketing environment-internal 7 external; environments and their influence on marketing strategies; Marketing in 21st Century – Scope of Marketing – Core Marketing Concepts – New Marketing Realities – Michael Porter’s Value Chain – Marketing Plan (Theory & Activity) – Segmentation (Geography, Demography, Psychography and Behavior Based Segmentation) – VALS Segmentation System - Targeting and Positioning – Positioning Statements and Brand Positioning Statements – Modern Marketing Mix – Psychology of Marketing – Branches of Marketing Management.

Concepts, contribution and reasons for the growth of services sector, difference in goods and service in marketing, characteristics of services, service marketing mix, GAP models of service quality, service encounter. Customer Behaviour in Service Encounters: Customer decision making: The 3-stage model of service consumption, understanding service encounters, defining moments of truth, Customer expectation and perception of services.

Brand – Branding Challenges and Opportunities – Lapferer’s Brand Identity Prism- Strategic Brand Management Process; Brand Equity – Types of Brand Equity (Price Based, Cost Based And Customer Based Brand Equity) - Methods of Calculating Brand Equity – Basic Problems – Sources of Brand Equity – Benetton’s Brand Equity Management; Brand Elements – Criteria for Choosing Brand Elements – Options and Tactics for Brand Elements; Ansoff’s Growth Share Matrix – Brand Extension – Brands Across Geographic Boundaries and Brands Over time

Setting Advertising Objectives – Advertising Objectives Vs Marketing Objectives – DAGMAR Approach – AIDA Model – Shannon Weaver Model - Advertising Agencies; Setting Media Objectives – Media Objectives Vs Advertising Objectives – Principles of Media Planning – Types of Broadcast Media, Telecast Media, Indoor Media, Outdoor Media and Digital Media; Copywriting – Copy Testing and Diagnosis – Practice of Copywriting. 

Introduction of the Digital Marketing, importance, Search Engine Optimization (SEO) Social Media Optimization (SMO) using Facebook, Twitter, Corporate Blogs, LinkedIn, Google plus; Search Engine Marketing- Tools used for Search engine Marketing, Marketing Analytics: Point of Sale Data- Assortment Optimization- Shelf Space Optimization- Market Basket Analysis.

2. S.A. Chunnawalia & K.C. Sethia (2011) Foundations of Advertising - Theory & Practice, Himalaya Publishing House.

3. Suja, R. N, (2011). Consumer Behavior In Indian Perspective. Mumbai: Himalaya Publishing House

2. Keller, K. L., Parameswaran, M. G., & Jacob, I. (Latest Edition) Strategic brand management: Building, measuring, and managing brand equity. Pearson Education India.

3. Ruchi, G (Latest Edition) Advertising principles and practice. RamNagar: S. Chand Company Limited.

4. Rackley, J. (2015). Marketing Analytics Roadmap.[Berkeley, Calif.]: Apress. 5. Zeithml,V.A.&Bitner,M.J.(2017).Services Marketing. Tata- McGraw- Hill Edition.

5. Zeithml,V.A.&Bitner,M.J.(2017).Services Marketing. Tata- McGraw- Hill Edition.

Continuous Internal Assessment (CIA – 1): 30%

(CIA-2): 40%

Continuous Internal Assessment (CIA - 3): 30%

Total: 100%

Course Description: This course is designed to highlight the importance of HRM in organizations and familiarize students with the process & mechanism of managing human resources students with the concepts & application of human resource practices followed in organizations.

Course Objectives: 

The course will create an awareness of the role, functions and practices of human resource department of organizations. This will also abreast students with contemporary issues and practices in HRM.

Meaning and definition of HRM – Purpose and Role of HRM Personnel Management and HRM - Organisation and Functions of Personnel Management and HRM - HR Structure and Strategic, Human Resource Planning Process. Job Analysis and its process, HR policies, traditional role of HR.

Job Analysis, Job Description, Human Resource Planning, Recruitment, Sources of Recruitment, selection Process and Methods of Selection – Interview, placement and Induction separation, Methods of induction. Promotions and Transfers- Retirement and other Separating Process. Legal Issues Related to Recruitment and Selection, Online and Social Media Recruitment Tools

Training needs assessment, methods of training, types of training, development, performance appraisal, Performance Appraisal- Purpose- Factors affecting Performance Appraisal, Methods and Systems of Performance Appraisal. Limitations of PA System and overcoming those limitations. Job Evaluation. Methods of Job Evaluation

Introduction to career planning and development, Career goals, Career and road map, Stages in career planning, Internal and external mobility of employees

Theories of motivation-Leadership-theories of Leadership-promotion-transfer Deviant workplace behaviour-Attrition.

Overview, importance and scope of Industrial Relation- Industrial disputes- Negotiation- Discipline-Dispute settlement; Meaning of Industrial Conflicts, Causes and Types of IC- Strikes & Lockouts. Settlement of industrial disputes, Grievance Handling and Industrial Discipline.

Recent developments in HR-Strategic Human resource Management- Global trend & their influence on Practices. Declining productivity, substantial demographic shifts, changing employee attitudes and expectations, innovation technologies. Recent developments in HR- Strategic Human resource Management- Global trend & their influence on Practices. Impact of Government regulations on human resource management into the 21st century.

2. Dr. C.B Gupta Human Resource Management

3. Dr. Ashwataappa: Personnel Management, Himalaya Publications.

4. Reward Management- Remuneration Strategy and Practice, Michael Armstrong & Helen Murlis, Crest Publishing House

5. Essentials of HRM and Industrial Relation- Text and cases. Subba Rao- Himalaya Publications.

2. GrayDessler, Human Resource Management,12th edition2011, Dorling Kinderlsely, New Delhi

3. An Indispensable Guide for Managers and Human Resources Professionals | by Shawn Smith and Rebecca Mazin

(CIA – 1) 20%

 (CIA-2) 20%

(CIA - 3) 25%

 (CIA-4) 30%

Attendance 05%

Total 100%

Course Description This course is an introduction to the concepts, principles, problems, and practices of operations management. The course seeks to develop insights into managerial processes for effective operations in both goods-producing and service-rendering organization.

Course Objectives This course introduces major concepts and tools used in the design and use of operations systems in organizations. It introduces the discipline and the role the function plays in a value-creating organization. Emphasis is given both to familiarization of various production processes and service systems, and to quantitative analysis of problems/ issues arising in the management of operations. Successful completion of the course will empower the students to be able to improve just about processes in varied areas of business.

Plant Location Criteria, Plant Layout Types, Product, Process, Cell Layout, Fixed Station, Merits & Demerits, Modern Practices of Production Management.

Inspection V/S Quality, Seven Stages of Quality, ISO 9000 & ISO 14000, Seven Tools of Quality Circles, Pareto Chart, Cause and Effect Diagram, Histogram, Stratification, Scatter Diagram, Control Charts, Check Sheets, Concept of Total Quality Management, Excellence in all Subsystem Leading to Organizational Excellence, Introduction to SIX SIGMA, QFD and FMEA & POKAYOKE, Vender Development and Vender Quality Rating

Different Types of Maintenance, Concept of OEE (Overall Equipment Effectiveness), Concept of “5S” House Keeping 

Material Requirement Planning (MRP), Enterprise Resource Planning (ERP), Production Planning and Control, Master Production Scheduling.

Inventory Management, RMC Inventory, ABC Analysis, JIT, Lead-time Management, Pareto Principles, WIP: Lean Manufacturing, Line Balancing, SPC, FGS, Push V/S Pull System, Advantages of Pull System, Spares, EOQ & Breakeven Analysis to Reduce Total Inventory Cost. 

Jacobs, F.R. & R.B. Chase. (2010). Operations and Supply Chain Management (13th edition). Boston: McGraw-Hill Irwin. 

G. Cachon and C. Terwiesch. Matching Supply with Demand: An Introduction to Operations Management (3rd Ed). McGraw-Hill. 2013 

The course aims to upskill the students by providing Capstone Simulation Programmes (CSP) offered by Equity Lever. A student has to mandtiarly complete minimum 5 assigned simulations.

As assigned during the class.

Course Description This course provides students with an understanding of financial statements and interpretation of the results to assess performance and use the information for decision-making, planning and control. The appreciation of the behaviour of costs and its applications for management decisions and financial control through preparation of budgets will also be discussed. As a perquisite-the student should be having basic knowledge of accounting concepts & conventions and preparation of financial statements.

Course Objectives To equip students with the basic accounting concepts, its application and enabling them to analyse transactions and financial statements in various business concerns. To equip the students with the concepts, methods and techniques of management accounting and enable them to use various techniques of cost ascertainment, budget preparation and variance analysis, while focusing on its need for managerial decision-making.

Introduction to Business, Trade, Industry and Commerce. Various types of business organizations; Introduction to Accounting, Branches of Accounting, Objectives-UsersProcess of Accounting, Fundamental Concepts of Accounting (GAAP), Accounting Standards, IFRS; Accounting Mechanics, Double Entry System, Accounting Equations, books of Accounts Journal book, Subsidiary Books, Ledger, Preparation of Trail balance.

Preparation of Financial Statements of sole proprietorship, partnership firms and Joint Stock Companies, Income Statement and Balance Sheet, Grouping and Marshalling. Legal requirements of Companies Act during preparation and presentation of Financial Statements, Annual reports of Companies, Limitations of Financial Statements- Critical Evaluation of Profitability of Companies, Ethical Conduct in Accounting Profession. 

 Conceptual framework of Inventory, 1- Perpetual Inventory System, 2- Periodic Inventory System, Methods of Inventory Valuation, 1- First in first out (FIFO) method, 2- Last in First Out (LIFO) Method, 3- Weighted Average Cost Method, 4 Moving Average method, ICAI Guidelines on Inventory Valuation, Inventory valuation and Window Dressing in Financial Statements.

Causes for Depreciation, Need for Depreciation, various Methods of Depreciation, ICAI Guidelines on Depreciation, Depreciation Accounting and Window Dressing in Financial Statements.

Introduction, Meaning of Management accounting, The Role of Management Accounting, Management Accounting Framework, Functions of Management Accounting, Tools of Management Accounting, The Balanced Scorecard, Cost Management System, Value Added Concept, Merits of Management Accounting, Demerits of Management Accounting, Distinction between Management Accounting and Financial Accounting. Analyzing financial performance of companies using financial ratios, du-pont analysis, reading reports, comparing balance sheets for decision making. 

Introduction, Meaning of Funds Flow Statement, Ascertainment of flow of funds, Technique of preparing funds flow statement, Schedule of Changes in Working Capital, Adjusted Profit and Loss account, Funds Flow Statement. Cash Flow Analysis: Introduction, Meaning of Cash Flow Statement, Purpose of Cash Flow Statement, Preparation of Cash Flow Statement, Format of Cash Flow Statement, Cash Flow from Operating Activities, Cash Flow Statement under Direct Method, Different between Cash Flow Analysis and Fund Flow Analysis, Uses of Cash Flow Statement

 Introduction, Concept of Marginal Costing, Characteristics of Marginal Costing, Difference between Absorption Costing and Marginal Costing, Cost Volume Profit (CVP) Analysis, Application of Marginal cost in decision making, Limitations of Marginal costing, Relevant Costs and Decision Making: Pricing, Product Profitability.

Budgetary Control & Standard Costing: Introduction, Meaning of a Budget, Budgetary control, Steps in budgetary Control, Types of Budgets, Limitation of Budgetary Control. Standard Costing: Meaning and Determination of Standard Costs. Material, Labor, and Overhead and Sales Variances (two way analysis). Responsibility Centers, Divisional Performance, Transfer Pricing. 

Target costing, Activity based Costing, Human Resource Accounting, forensic Accounting, Inflation Accounting, Economic Value Added, life cycle costing, strategic costing, and cost cutting Vs cost savings, Green Accounting.

2. Shukala & Grewal – Advanced Accounting, S. Chand Publications, Delhi. 

2. Jain & Narang – Advanced Accounting, Kalyani Publishing.

 3. Dr.S.N. Maheswari, K. Maheswari , Principles of Management Accounting, Sultan Chand & Sons ,2010

4. Khan- M. Y. & Jain (2013); P. K Management Accounting & Financial Analysis; Tata McGraw Hill Publishing Co. Ltd; New Delhi

5. Arora- M.N; Cost Accounting, 10th ED; Vikas Publishing House (Pvt) Ltd

 6. Horngren, C.T., Foster, G, and Datar, S.M. (2008), Cost Accounting: A Managerial Emphasis, Prentice Hall of India Pvt. Ltd., New Delhi, 2009

7. Henke, E.O., and Spoede, C.W. (2005), Cost Accounting: Managerial Use of Accounting Data, PWS-KENT Publishing Company, Boston.

Finance being life blood of any business organization plays a very crucial role in profit planning. That is why a finance manager always aims to achieve a right balance between risk and return by using various tools and techniques of financial management. This course deals with deeper insight in to the concept of financial management and equip the students with use of various tools that helps them in development of sharp analytical skills for making optimal decisions under given set of business situations.

The course will equip students to understand the fundamental concepts of corporate finance enabling them to apply the financial tools and techniques to maximize value of the firm. 

Nature and objective of Financial Management-Role of Finance Manager- Interface of Financial Management with Other Functional Areas. Corporate goals and objectives; profit maximization Vs wealth maximization, Issues in financial management, agency relationship, overview on Indian financial system.

Introduction to the concept of time value- Nominal Vs Effective rate; process of compounding – Process of discounting- Future Value of a Single Cash Flow – Future Value Multiple Flows - Future Value Annuity – Present Value of a Single Cash Flow – Present Value of uneven Multiple Flows - Present Value Annuity, perpetuity, loan amortization.

Nature of Investment decisions-The Process of Capital Budgeting, types of investment projects-expansion projects, replacement projects, diversification projects, modernization projects. Independent and mutually exclusive projects. Techniques of Capital Budgeting: Financial Appraisal of a Project- Appraisal criteria-Discounted and Non-Discounted Methods (Pay Back Period- Average Rate of Return-Net Present Value- Benefit Cost Ratio- Internal Rate of Return), modern tools for capital budgeting, Profitability Index, SA, DTA, CCV.

(Owned and Borrowed Sources) Need for Long–term Finance- Types of Capital: Equity Capital (IPO, Bonus issue, right issue, private placement) and Preference Capital – Debenture Capital – Term Loans-venture capital & private equity and Deferred Credit –Government Subsidies – Sales Tax – Sales Tax Deferments and Exemptions – Leasing and Hire Purchase. Cost of Capital: The meaning of cost of Capital-cost of Different Sources of Finance- Concept and Importance- Cost of Debenture – Term Loans – Equity Capital and Retained Earnings – Calculation of Weighted Average Cost of Capital – Weighted Marginal Cost of Capital Schedule.

The concepts of risk and return –Measuring the rate of return- risk –Risk and expected Return Concepts – Sources of Risk-Portfolios and risk-the Capital Asset Pricing Model (CAPM), measurement of market risk, determinants of Beta.

Meaning of capital structure- Factors affecting the capital structure-Theories of capital structure-Net Income Approach–Net Operating Income Approach–Traditional Approach – Modigliani–Miller Model (MM), Miller Model– Criticisms of MM –Agency Cost. Operating and Financial Leverage: The concept of leverage-Measuring of leverage-Operating Leverage, Financial Leverage and Total Leverage, application of leverage in business operations. Designing of Capital Structure: steps in financial planning, over and under capitalization, models for designing optimum capital structure; earning coverage criteria- interest coverage criteria- debt service burden coverage ratio,

Components of Current Assets and Current Liabilities- Objectives of Working Capital (Conservative vs. Aggressive policies), Static vs. Dynamic View of Working Capital–Factors Affecting the Composition of Working Capital –Independence among Components of Working Capital–Operating Cycle Approach to Working Capital –Criteria for Evaluation of Working Capital Management–Important Working Capital Ratios.

Inventory Management: Introduction – Role in Working Capital–Purpose of Inventories–Types and Costs of Inventory–Inventory Management Techniques–Inventory Planning and other Inventory Management Techniques –Pricing of Inventories-Inventory and Finance Manager.

Receivables Management: Purpose of Receivables– Cost Of Maintaining Receivables – Credit Policy Variables (Credit Standard, Credit Period, Cash Discount, And Collection Program), Credit Evaluation – The Process of Credit Evaluation –Decision-Tree Approach- Monitoring Receivables.

Cash Management: Difference between Profits and Cash–Need for and Objectives of Cash Management –Factors for Efficient Cash Management –Internal Treasury Controls.

Financing Current Assets: Behavior of Current Assets and Pattern of Financing–Accruals – Trade Credit–Provisions–Short–Term Bank Finance–Public Deposits, Commercial Paper –Factoring–Regulation of Bank Credit.

Types of dividends, Factor affecting a dividend policy, stable and fluctuating dividend policy. Dividend Models: Traditional Position–Walter Model–Gordon Model – Miller–Modigliani Position and Rational Expectations Model.

2. M Y Khan. – Indian Financial System, the McGraw-Hill, SYLLABUS- MSC (FINANCE AND ANALYTICS)- 2021 9

3. Brealey and Myers, Principles of Corporate Finance (Mc Graw Hill)

4. James C Van Horne, Financial Management and Policy , 10e (PHI)

5. Ross, Westerfield and Jordan, Essentials of Corporate Finance (Mc Graw Hill)

6. Dr.Prasanna Chandra, Financial Management: Theory and Practice, 4e (TMH)

7. A Besant C Raj - Corporate Financial Management: An introduction (TMH)

8. Ravi M. Kishore, Financial Management, 6th Edition (Taxmann’s) 

In order to learn data science, a student must reinforce their knowledge of mathematics and statistics. Thus the course provides participants understanding and application of statistical and mathematical tools for handling data for financial decision making.

Descriptive statistics data, measures of central tendency, measures of variations, measures of skewness and kurtosis, Moments and their applications in financial statistics, Statistical Software’s, Qualitative and quantitative data, nominal and ordinal data, cross sectional and time series data, discrete and continuous data, frequency and non-frequency data, collection and scrutiny of data, Qualitative and quantitative data, nominal and ordinal data, cross-sectional and time series data, discrete and continuous data, frequency and non- frequency data, collection and scrutiny of data, scrutiny of data for internal consistency and detection of errors of recording, classification and tabulation of data. Diagrammatic and graphical presentation of grouped data, graphing the data constructing histograms, frequency polygon, frequency curve and ogives.

Probability Theory, Probability Distributions, Discrete and Continuous. Binomial, Poisson, Hypergeometric, Geometric, Negative Binomial, Uniform, Exponential and Normal probability distributions, Rules of Probability, Conditional Probability and Independence, Distribution of a Random Variable, Moment Generating functions, Central Limit Theorem, Expectation and Variance

Estimation: Sampling and Sampling Distribution, Standard Error, Law of Large Numbers and Central Limit Theorem, Point Estimation – Properties of a good point estimator, and Interval Estimation.

Null and Alternate Hypotheses; One-tailed and two-tailed Tests; Type I and Type II errors; Power of a Test; p-values, Parametric and Non Parametric Tests for one sample, two samples and more than two samples and for measures and their differences of central tendency, variation and association, Run Test for testing randomness. Analysis of Variance One-way Analysis of Variance and two-way Analysis of Variance.

Measures of central tendency and location, mean, median, mode, geometric mean, harmonic mean; partition values-quartiles, deciles, percentiles and their graphical location, Decision Theory, Correlation and Regression Analysis: Index Numbers, Time Series Analysis and Forecasting, Sheppardís correction for moments (without derivation), Charlierís checks.

Matrix Algebra- matrices and determinants, Operation on matrices, Properties of Matrix Multiplication, inverse of Matrix, Determinants , properties of determinants, Solving linear equation with matrices, Applications of Matrices and Determinants,

Limits, Differentiation, Methods of differentiation, Second order derivative, Maxima and Minima, Application to commerce and Economics, Revenue Function Cost function , profit function, Elasticity of demand, Breakeven point, constrained optimization

2. Dr. Sancheti; Kapoor: Statistics Theory, Methods and Applications

3. Zamirudding Khanna: Business Mathematics

4. S.P.Gupta: Statistical Methods- Sultan Chand, New Delhi.

5. ELHANCE: Statistical Methods/Fundamentals of Statistics,5th edition, Kitab Mahal, Wholesale Division, New Delhi

Course Description: This course will extensively cover python tool can be used to analyse real-life business problems such as prediction, classification, discrete choice problems and application of analytics for all.

Course Objectives: The focus of this course will be on case-based practical problem-solving and solution oriented decision making using python.

Analytics In Decision Making Game Changers & Innovators, Introduction To Time Series Analysis, Decomposing Time Series. Additive & Multiplicative Models, Review Of Statistical Distributions, And Properties Of Financial Time Series, Testing And Correcting Stationarity In Time Series. Time Series Modelling With Exponential Smoothing Methods, Holt Winter’s, Forecasting Accuracy, Auto Regressive And Moving Average Models, Stock Price Forecasting Using ARIMA, Fundamentals Of Probability, Discrete And Continuous Variables.

GUI Of Python: IDLE And Statistical, Python Notebooks, Anaconda Python Distribution Expressions, Constant Values, Numeric & Strings, Arithmetic: Operations And Bodmas, Common Mathematical Functions, Conditions: Equality, Greater Than, Less Than, Etc. Function Calls, Introduction To Python Functions, Symbols & Assignment, Declaring Python Variables, Reserved Keywords, Naming A Variable, Generally Accepted Conventions, Basic Data Types Bool (Boolean), Int (Integer/Long), Float,, Complex, Type Conversions: Into To Float, Float To Int, Etc., Python Interpreter And Its Environment, Python 3.X : Background, Relevance Numbers, Strings, Declaration Of Variables, Basic Operations In Python, String Definition And Manipulation Commands.

Containers In Python, Lists, Tuples, Dictionary, Sets, Operations On Set, Frozen Sets, Performing Math Operations In Python, Leraning To Import The Libraries, How To Import Different Kind Of Libaries In Python, Conditional Statements And Control Structures, Break And Continue Statements, And Else Clauses On Loops, Continue Statement, Pass Statements, Function In Python, Creating A Function, Calling A Function, Best Practices For Function, Arguemnts In A Function, Default Argument And Keyword Arguments, Global Variable, Localvariable, Non Local Variables, Args And *Kwargs In A Function, Lambda Functions, Nested Loops In A List Comprehension, Isalpha, Map, Apply, Reduce, Filter

Errors, Try .. except, ValueError, KeyboardInterrupt, ZeroDivisionError, NameError, KeyError, Handling exceptions , Raising exceptions , Try .. finally , Python Iterator, Python Generator, Python Closure, Python Decorators, Python RegEx

Python Program to Create a Countdown Timer, Python Program Read a File Line by Line Into a List, Python Program to Shuffle Deck of Cards, Python Program to Make a Simple Calculator, Python Program to Capitalize the First Character of a String.

Creating And Opening A File, Reading From A File, Writing To A File ( Variations ), Closing A File, Handling Csv Files, Handling Data From Files Intro To Numpy Arrays, Creating Ndarrays, Indexing, Data Processing Using Arrays, File Input And Output, Getting Started With Pandas, Data Acquisition(Import & Export), Intro To Series & Dataframes, Indexing Set And Reset An Index In A Dataframe, Retrieve Dataframe Rows By Index Position Or Index Label, Set_Index And Reset_Index Methods To Define A New Dataframe Index, Retrieve Rows By Index Label With Loc Accessor, Retrieve Rows By Index Position With Iloc Accessor, Passing Second Arguments To The Loc And Iloc Accessors, Set New Value For A Specific Cell Or Cells In A Row, Set New Values For One Or More Cells In The Dataframe, Rename Or Delete Rows Or Columns, Data Frame Manipulation, Delete Rows or Columns from a DataFrame(drop(),pop()), Create Random Sample with the sample Method, smallest / nlargest methods to get rows with smallest / largest values, Filter A DataFrame with the where method( .where() method), Filter A DataFrame with the query method(.query() method), Apply Method on a pandas Series Object, Apply a Function to every DataFrame Row with the apply Method, Create a Copy of a DataFrame with the copy Method

Date And Time Data Frame Manipulation, Intro To The Working With Dates And Times Module, Python's Datetime Module, Pandas Timestamp Object, The Pandas Datetimeindex Object, The Pd.To_Datetime() Method, Pd.Date_Range() Method To Generate A Datetimeindex Of Timestamp Objects, Pd.Date_Range() Method Operates With Arguments For The Start And Periods Parameters, Pd.Date_Range() Method Operates With Arguments For The End And Periods Parameters.

Working with Text Data Section, Changing Pandas Options With Attributes And Dot Syntax, Get_Option(), Set_Option() Reset_Option(), Describe_Option(), Max_Rows, Max_Columns, The Precision Option, Export CSV File With The To_Csv Method, Install Xlrd And Openpyxl Libraries To Read And Write Excel Files, Import Excel File Into Pandas With The Read_Excel Method, Export Excel File With The To Excel Method, Visualization In Python, Line Plots, Bar Charts, Pie Charts, Histograms, Scatter Plots , Parallel Coordinates, Plotly, Bokeh

 M. Heydt, Python Web Scraping Cookbook: Over 90 proven recipes to get you scraping with Python, microservices, Docker, and AWS. Birmingham, England: Packt Publishing, 2021  Y.J. Hilpisch, Python for finance:Mastering data-driven finance. Beijing;Boston;Farnham;Sebastpol; Tokyo:O’Reilly, 2021

 F. Nelli, Python Data Analytics: With Pandas, NumPy, and Matplotlib, 2nd ed. Berlin, Germany: APress, 2021

Course Description: The course will help students understand why financial markets exist, how financial institutions serve them and the services the institutions offer. Focusing on management performance and regulation of financial institutions, the course explores Indian Money Markets, Capital Markets and Financial Service Institutions. 

Course Objectives: The course will provide an understanding of the functions and operations of the financial markets and institutions with special knowledge on financial marketing practices and skills for financial service sector.

Meaning, role, functions and constituents of financial markets – Financial instruments – Indian Money and Capital Markets – Money Market: Meaning, characteristics, objectives, importance, general functions and segments of money market – Characteristics of a developed money market – Money market Vs Capital market – Global money markets

Nature of dealings – participants – mode of operation – call money rates – Commercial Paper Market: meaning and features – Satellite Dealers – Commercial Bill Market: Meaning and importance – Developed Bill Market – shortcomings of Indian Bill Market – growth of Indian Bill Market – Bill Market Schemes – Failure of Bill Market Scheme Blues of bill discounting – RBI directives 

Certificate of Deposit Market: Meaning, features – time deposit Vs certificate of deposit – Role of DFHI and banks – Treasury Bills Market: Meaning and features – features of Indian treasury bills – Gilt-edged securities market: meaning and features – REPOS – Repo Accounting – Government bonds – important of gilt-edged market – criticisms, recent developments in the market including collapse of IL&FS, DHFL.

Capital market: meaning – Indian money market – Indian capital market – evaluation and growth – new financial instruments recent initiatives in the Indian capital market – major issues of Indian capital market – Capital market instruments – New Issues Market – meaning and features – NIM Vs secondary market – intermediaries in NIM, the role of SROs, Exchanges, AMCs and issues relating to Financial Inclusion.

Clearing Corporation of India – Credit Rating and Information Services of India Limited (CRISIL) – Discount and Finance House of India Limited (DFHIL) – Investment Information and Credit Rating Agency of India Limited (ICRA) – Moody’s Investor Service – S & P – Fitch ratings – OTCEI – NSDL – STCI – Financial Institutions: NHB – EXIM Bank – NABARD – Stock Exchange – functions and working

 Bhole L M, Financial Institutions and Markets, Tata McGraw Hill Company

Kohmn Meir, Financial Institutions and Markets, Tata McGraw Hill Company

Apte P G, International Financial Management, Tata McGraw Hill Company

Avadhani V A, Capital Market Management, Himalaya Publishing Company

Khan M Y, Indian Financial System, Tata McGraw Hill Company

Continuous Internal Assessment (CIA – 1) 20%

Mid Semester Examination (CIA-2) 25%

Continuous Internal Assessment (CIA - 3) 20%

End Semester Examination (ESE) 30%

Attendance 05%

Total 100% 

The course aims to upskill the students by providing Capstone Simulation Programmes (CSP) offered by Equity Lever. A student has to mandtiarly complete minimum 5 assigned simulations.

As assigned during the class.

To successfully qualify for a particular simulation a student has to score at least 70% marks. Scoring /evaluation rubrics as decided by the department and Equitly lever.
A student will get 3 attempts to successfully qualify a particular simulation(s).

Course Description:This course will help learners to gain the skills and understanding to convert data to appropriate chart elements, using glyphs, parallel coordinates, and streamgraphs, as well as implementing principles of design and colour to make  visualizations more engaging and effective.

Course Objectives: This course will give a hands-on experience on visualization, exploratory data analytics and descriptive analytics including designing visualization system for large datasets and dashboards using tools such as Tableau, Power BI, Excel, R, and Python to generate visualizations.

Conceptual knowledge, Importance, Different types of Data, Data Cleaning, Types of charts in visualization, Tools used for Data Visualization, Choosing the most appropriate Data Visualization Tools, Designing and creating a dashboard, Discover user needs, Identify key metrics, and Tailoring dashboard to a particular audience, Defining an effective problem statement, Structure a data presentation, Scope analyses, Identify biases and limitations within the dataset, and pull together an end-to-end analysis, Understanding continuous vs discrete- YOY, Packed bubble chart and word cloud. Dual Axis -Map, Line and Bar Chart, Quick Filters, Page Shelf. Calculated Field and Parameters, Dynamic measures, IF and Case statements, Histogram and Bins. Scatter Plot, Box-plot whisker Chart, Bullet Chart. High light table, Text Table, Heat map Tables. Calculations, Sub/Grand Total, Aggregate vs Non-AggregateTool Tip.

Data Collection scraping the internet, and using APIs, Data Wrangling using various techniques to identify duplicate rows, find missing values, and normalize data, Exploratory Analysis finding the distribution of data, the presence of outliers and the correlation between different columns, Data Visualization using developer survey data to create visualizations highlighting the distribution of data, relationships between data, and the composition and comparison of data, Dashboard Construction that is intuitive, appealing, and easy to understand, Presentations demonstrating the ability to clarify the analysis and relay findings for effective decision making.

Introduction, Installation, Creating Effective and Interactive Dashboards using tools such as Tableau, Power BI, Excel, R, and Python, Power Pivot and Analysis, Prototyping and wireframing stage of dashboard design, Use data hierarchies, filters, groups, sets, and calculated fields, Creating map-based data visualizations, Effective visualization using Tableau Interface, Distribution and Publishing, Connect Tableau to Any Big Data Source (MongoDB and Salesforce), Connectivity using Custom SQL (Resource1, Resource2), Creating Views in Tableau, Visual Analytics and Mapping, Creating calculated fields in Tableau, Advanced visualization in Tableau, Introduction to Tableau Server and Tableau Online, Control charts, Using INDEX to sort multiple columns , SQL Overview, Joins, UNION vs UNION ALL, WITH Statements.

Advanced Data Visualization and Storytelling Techniques. Using Tableau Storypoint to add interactivity and other visual elements to a story, Adding animation and narration with Tableau Pages and Flourish, Visualize time series data using TABLEAU function - Perform calculations with time series data in Tableau, Publishing to office and PDF; Publishing dashboard to server, Publishing data source to server, Connecting to Data in server, Publishing extract to server, Scheduling extract, Permissions, Groups, Access levels.

Project Work: Building Covid Dashboard by connecting Live data, Customer profiling for loan sanctioning, Building a Stock Market Dashboard.

Trevor Hastie, Robert Tibshirani, Jerome Friedman The Elements of Statistical Learning - Data Mining, Inference, and Prediction, Second Edition , Springer Verlag, 2009

Course Description: This course will help learners to gain the skills and understanding to oversee or implement a project that covers the full data science pipeline, including predictive modelling and evaluation using machine learning.

Course Objectives: This course will give a hands-on experience in predictive, prescriptive and machine learning models.

Excel Formula And Functions, Data Connections In Microsoft Excel, Data Summarisation Using Pivot Table, Data Modelling Using Power Pivot, Data Pre-Processing Using Power Query, Advanced Excel Functions, Visualization Using Power View, MIS Reports Using Excel, Data Analysis Tool Pack, What-If Analysis And Solver, Advanced Excel Add-Ins For Analysis And Modelling, building predictive models using clustering, quantitative forecasting, decision trees, random forests, moving average time series, neural network algorithms, exponential smoothing, support vector machines and implementing all in python.

Foundational Concepts Of Data Analysis: Basics Of Data Visualization, Probability For Data Science, Linear Algebra For Data Science, Working Across The Entire Data Analysis Pipeline, Getting, Cleaning & Manipulating The Data, In-Depth Numpy & Pandas, Installation Of Python, Jupyter Notebook, Packages, Introduction To List, Dictionary, Numpy And Data Frame, Importing Data In Python, Data Processing Using Pandas - Groupby, Sort_Values, Reset_Index, Merging Data Sets.

Getting, cleaning & manipulating the data, Data in pictorial form, Text Analytics NLP and web scrapping, Understanding web scraping tools such as Beautiful soup and Selenium, Applying Web Scraping Tools To Extract The Data From Static And Dynamic Website To Perform Data Analysis, Time-Series Datasets, Textual Data, Databases In Python, Phases Of Data Analysis, Hypothesis And Data, Scales, Relations, Similarity And Dissimilarity Measures, Statistical Data Analysis, Visualization Of Numeric Data, Visualization Of Non-Numeric Data, Tools Available For Visualizations.  Hypothesis Testing, Pairwise Comparisons, Wilcoxon Signed-Rank Test, Kruskal-Wallis Test, A/B Testing        

Simple Linear Regression Case-let Overview, Introduction to Regression, Model Development, Model Validation, Multiple Linear Regression, Estimation of Regression Parameters, Model Diagnostics, Matplotlib – Introduction to Data Visualization with matplotlib, Pandas Visualization, Time Series Visualization.Dummy, Derived & Interaction Variables, Multi-collinearity,  Heteroscedasticity, Auto regression, Model Deployment, Logistic Regression Discrete choice models, Logistic Regression, MLE Estimation of Parameters, Logistic Model Interpretation, Logistic Model Diagnostics, Logistic Model Deployment, Decision Trees, Developing Decision Trees, Random Forests.SVM’s, Ensemble methods, Automatic Interaction Detectors (CHAID), Classification and Regression Tree (CART), Analysis of Unstructured data, Naive Bayes algorithm, Cluster analysis.                                                 

Basics Of Machine Learning With Scikit-Learn, Introduction To Machine Learning, Fitting A First Model, Cost Functions & Outliers, Gradient Descent, Feature Engineering, Regularization, Advanced Machine Learning Techniques, K-Nearest Neighbours, Bias-Variance Trade-Off,  Steps Of The Decision-Making Process, Decision Making Under Uncertainty, Risk Management.

GLPK (Generalized Linear Programming Kit), IPOPT (Interior Point OPTimizer), CBC (Coin Or Branch Cut), COUENLE (Convex Over and Under Envelopes for Non-linear Estimation), Framework towards business decisions using already learnt tools.

Course Description This course attempts to develop a conceptual and analytical understanding of framework of evaluating financial instruments & markets and inculcates investment intelligence in students.

Course Objectives The course develops an investment attitude providing an in-depth knowledge of the theory and practice of alayzing stocks and portfolio management

Overview of the Investment Environment and Investment Process; Organization and Functioning of securities markets, orders and trading strategies; securities trading (trading cost, short sales, margin trading); Security market indices; Bond market indices. The investment setting, The Asset Allocation decision, Individual investor life cycle; objectives based investing, Code of Ethics and Standards of Professional Conduct. Mutual fundsstructure, types, risk and return.

Economic activity and security markets, Global and domestic market economy- Macro market analysis –; the cyclical indicator approach Understanding the demand and supply shocks- Government policies and the implications, Industry structure and performance, Interconnection between the financial statements, estimating the profitability, past versus future ROE- Du Pont Analysis- Modified Du Pont – Efficiency ratios-Working Capital cycle and the relationship with cash flows-Comparability issues of financial statementsAccounting standards – International Accounting Conventions- Value Investing. Valuation by comparable- Intrinsic Value versus Market price- Dividend discount models, Constant growth and multistage growth -Price Earnings Ratio and other comparative valuation ratios-Free Cash flow valuation approaches- Comparing valuation modelsAnalysis of growth companies; Valuation of alternative Investments. Technical analysis – Advantages of technical analysis; Technical trading rules and indicators. Basic Tenets of Technical Analysis - Dow Theory - Behavior of Stock Prices - Major Trends - Charts and Trend Lines - Resistance and support Lines - Different Patterns, Elliot Wave Theory, Efficient market theory. Efficient Capital Markets – Why should capital markets be efficient? Alterative efficient market Hypotheses; Tests and results of EMH; Implications of efficient capital markets; 

The Analysis and Valuation of Bonds – The fundamentals of Bond Valuation; computing bond yields; term structure of interest rates; interest rates risk, duration and convexity.

Diversification and Portfolio Return and Risk, Measurement of Co movements in Security Returns, Calculation of Portfolio Risk, Efficient Frontier, Optimal Portfolio. Single Index Model, Capital Asset Pricing Model, The Capital Market Line - Security Market Line, Pricing of Securities with CAPM. FAMA French Model, Limitations for all models. Arbitrage Pricing Theory; Empirical factor models; Equity portfolio management strategies – Passive Vs Active management; An overview of style analysis; asset allocation strategies; Evaluation of Portfolio performance, Composite Portfolio Performance measures; Application of Portfolio performance measures; Evaluation of bond portfolio performance

Standard Finance Versus Behavioral Finance, History of Behavioral Finance, Investor Behavior and asset allocation process, Investor Biases-Overconfidence, Representativeness, Anchoring, Mental Accounting, Loss Aversion, Framing, Availability bias and others. 

2.      Chandra, Prasanna. (2008). Investment analysis and portfolio management. New Delhi: Tata McGraw – Hill Publications.

3.      Fischer.,& Jordan., Security analysis and portfolio management. Prentice Hall Publications.

4.      Bhalla, V. K., Investment management, S. Chand & Co Publications.

5.      Kevin S.(2008). Security Analysis & Portfolio Management, New Delhi: PHI Learning Pvt Ltd Publications.

6.      Brealey.,& Myers., Principles of corporate finance (7^th ed.). Tata McGraw Hill Publications.

Continuous Internal Assessment (CIA – 1) 20%

Mid Semester Examination (CIA-2) 25%

Continuous Internal Assessment (CIA - 3) 20%

End Semester Examination (ESE) 30%

Attendance 05%

Total 100%

Course Description The course aims to provide the students with a deep understanding of financial management issues in a multinational enterprise (MNE). It equips the students with analytical tools and techniques for sound financial decision making in a global setting.

Course Objectives The course explores the complexities of corporate financial management in an international setting, where companies are subject to exchange rate risk. Exchange rate theories and their practical implications are analysed, as well as the merit of foreign exchange risk management. The course also aims to provide students with a thorough understanding of international investment and financing decisions. The course emphasises the practical implications of finance theory and its application in international financial management

Globalization and the multinational enterprise (MNE); Environment of International Financial Management; Complexities and issues in financial decision making of MNEs; Decisions in a global setting; Foreign Exchange Market- Spot and Forward market; Participants in foreign exchange market; Arbitrage, hedging and speculation; Covered interest rate arbitrage; Contemporary issues in international financial management.

Types of and issues in cross border investment decision; Green field investment vs. cross border M&As; Estimation of cash flows from cross border investment projects; Valuation techniques including adjusted present value method; Risks in cross border investment decision-currency risk, political risk, country risk, inflation risk etc; Techniques for incorporating risks in cross border investment decision.

International Cash management, International Inventory management and International receivables management; International capital structure and cost of capital; Determinants of capital structure of MNEs; Dividend decision and policies of MNEs; International transfer pricing.

Types of risk exposure - Transaction exposure, Economic exposure and Translation exposure; Measurement of risk exposure; Management of currency risk using currency forwards and futures, currency options and currency swaps; Assessment of interest rate risk; Management of interest rate risk using Interest rate futures, interest rate swaps and other financial swaps.

Global markets for equities; Risk factors in international investing; International diversification-risk and return aspects; International CAPM assuming no differences in consumption and no barriers to investment as well as assuming such differences; Identification of optimum portfolio; International Capital Market. Foreign Portfolio.

GDR, ADR, Cross, listing of shares Global registered shares. International Financial Instruments: Foreign Bonds Eurobonds, Global Bonds. Floating rate Notes, Zero coupon Bonds, International Money Markets International Banking services; Correspondent Bank, Representative offices, Foreign Branches. Forward Rate Agreements.

2.      The Financial Times or Wall Street Journal.

3.      The Economist and Bloomberg Business Week.

4.      IMF Staff Papers,

5.      Finance and Development (IMF), OECD Observer,

6.      Transnational Corporations,

7.      Journal of International Business Studies.

8.      http://www.bis.org/statistics/index.htm http://www.rbi.org.in/home.aspx

9.      http://www.fedai.org.in/AboutUs.html http://www.nseindia.com/marketinfo/fxTracker/fxTracker.jsp

10.  http://exim.indiamart.com/act-regulations/fema-2000- ii.html

http://www.cmegroup.com/?ProductType=cur http://www.economist.com/markets/bigmac

Continuous Internal Assessment (CIA – 1) 20%

Mid Semester Examination (CIA-2) 25%

Continuous Internal Assessment (CIA - 3) 20%

End Semester Examination (ESE) 30%

Attendance 05%

Total 100%

Course Description The course will develop the financial modelling skills used in valuing business using he application of Excel spreadsheet functions and visual basic programming.

Course Objectives This course attempts to enhance the modelling skill level of the students through in financial analyse, valuation skills and valuation report writing. 

The importance of valuation Understanding enterprise value and equity value, Introduction to use of MS Excel in Finance; Overview of different finance concepts; Creating basic finance models (PV, FV, FD, RD, Loan Amortization schedule), Project Finance Modelling (NPV, IRR, MIRR, WACC, CAGR, BEP), Financial Statement Modelling (Financial Statement Analysis, Projecting BS, PL and Cash Flows).

Recap of Financial Ratios, Calculation of Financial Ratios, Discounted cash flows, Free Cash Flow to Equity and Free Cash Flow to Firm- Calculation of unlevered beta- Understanding role of Working capital in valuation – Concepts of valuation techniques relative valuation, Residual Income and Replacement Value 

Comparable Company Analysis, Selecting comparable companies, Spreading comparable companies, Analyzing the valuation multiples, Concluding and understanding value; Precedent Transactions Analysis, Selecting comparable transactions, Spreading comparable transactions, Concluding value, Discounted Cash Flow (DCF) analysis, Understanding unlevered free cash flow, Forecasting free cash flow, Forecasting terminal value, Present value and discounting, Understanding stub periods, Performing sensitivity analysis, Weighted Average Cost of Capital (WACC), Using the CAPM to estimate the cost of equity, Estimating the cost of debt, Understanding and analyzing WACC, Concluding valuation, Aggregating the three methodologies, Concluding value

Understanding the links between the financial statements, Understanding circularity, Setting up and formatting the model, Selecting model drivers and assumptions, Modeling and projecting the financial statements, Projecting the income statement, Projecting the balance sheet, Projecting the cash flow statement, Geographic Revenue Sheet, Segment Revenue Sheet, Cost Statement, Revenue Drivers, Creating the debt and interest schedule, Revolver modelling, Analyzing and concluding the model, Analyzing the output, Stress testing the model, Fixing modeling errors, Advanced modeling techniques, Using the model to create a Discounted Cash Flow (DCF) Analysis. 

Performing sensitivity analysis of the model – Arriving at the range of equity value of the company – Calculating the market multiple, Investment Note writing- principles of logic and structure of the report- Use of Infographic and use of linking words in report writing, Valuation of young companies, privately held companies, distressed companies- the principles and the challenges

2.      Business Analysis and Valuation: Using Financial Statements Text and Cases,

3.      Krishna G. Palupu, Paul M. Healy & Victor L. Bernard, South-Western Pub.

4.      The Handbook of Advanced Business Valuation, Rovert F. Reilly & Robert P. Schweihs, McGraw Hill.

5.      Corporate Finance: A Valuation Approach, O.H. Sarig & S.Z. Benninga, Irwin McGraw Hill.

Valuation Workbook: Step-by-Step Exercise and Tests to Help your Master Valuation, Tom Copeland, Tim Koller, Jack Murrin and William Foote, John Wiley & Sons.

Continuous Internal Assessment (CIA – 1) 20%

Mid Semester Examination (CIA-2) 25%

Continuous Internal Assessment (CIA - 3) 20%

End Semester Examination (ESE) 30%

Attendance 05%

Total 100%

The course aims to introduce students with classes of stochastic processes, analyse their behavior over a finite or infinite time horizon and help them enhance their problem solving skills with various applications in finance.

The course will equip students with theoretical knowledge and practical skills for the analysis of stochastic dynamical systems in finance.

Axiomatic construction of probability spaces, random variables and vectors, probability distributions, functions of random variables; mathematical expectations, transformation and
moment generating functions, modes of convergence of sequences of random variables, laws of large numbers, central limit theorem.

Definition and examples of SPs, classification of random processes according to state space and parameter space, types of SPs, elementary problems. Stationary Processes: Weakly
stationary and strongly stationary processes, moving average and auto regressive processes.

Definition and examples of MCs, transition probability matrix, Chapman-Kolmogorov equations; calculation of n-step transition probabilities, limiting probabilities, classification of states, ergodicity, stationary distribution, transient MC; random walk and gambler’s ruin problem, applications.

Kolmogorov- Feller differential equations, infinitesimal generator, Poisson process, birth- death process, stochastic Petri net, applications to queueing theory and communication
networks.

Conditional expectations, definition and examples of martingales.

Wiener process as a limit of random walk; process derived from Brownian motion, stochastic differential equation, stochastic integral equation, Ito formula, Some important
SDEs and their solutions, applications to finance. Gaussian vector, Gaussian process.

Renewal function and its properties, renewal theorems, cost/rewards associated with renewals, Markov renewal and regenerative processes, non Markovian queues, applications of Markov regenerative processes.

Definition and examples branching processes, probability generating function, mean and variance, Galton-Watson branching process, probability of extinction.

CIA-2=25%

CIA-3=20%

ESE=30%

Attendance=5%

Total=100%

Prescriptive analytics relies on machine learning, which encompasses algorithms and models that allow computers to make decisions based on statistical data relationships and patterns.

Prescriptive analytics is the most powerful branch among the fields of Data Analytics.

This course will extensively cover how prescriptive analytics tools which can be used to analyse real-life business data

Phases of data analysis, hypothesis and data, Scales, relations, similarity and dissimilarity measures, sampling process, types of sampling, sampling strategies, error mitigation       

Visualization of numeric data, visualization of non-numeric data, tools available for visualizations.  Hypothesis testing, pairwise comparisons, t-test, ANOVA, Wilcoxon signed-rank test, Kruskal-Wallis test, A/B testing        

Analytics and BI, data sources, data warehouse, data stewardship, meta data management.  Data and forecasting, super-forecasting, S-curve (lifecycle), moving average, exponential smoothing, error in forecasting.      

Correlation and causality, spearman’s rank correlation, Linear regression, logistic regression, robust regression. Hierarchical clustering (Euclidean & Manhattan), Review of k-means clustering, Nearest neighbour, decision trees.        

Basics, customer lifetime value, customer probability model, Net promoter score, survival analysis. Product lifecycle analysis, Ansoff’s matrix, competitive map, Fundamentals of simulation, simulation types, Monte-Carlo simulation.

Provost, F., and Fawcett, T., Data Science for Business, O’Reilly

Trevor Hastie, Robert Tibshirani, Jerome Friedman The Elements of Statistical Learning - Data Mining, Inference, and Prediction, Second Edition , Springer Verlag, 2009

Provost, F., and Fawcett, T., Data Science for Business, O’Reilly

Trevor Hastie, Robert Tibshirani, Jerome Friedman The Elements of Statistical Learning - Data Mining, Inference, and Prediction, Second Edition , Springer Verlag, 2009

The world of finance offers a range of opportunities to profit from. This requires a good understanding of the financial concepts, and their application to real-world data and analysis. This course will enhance the ability of all finance professionals who are engaged or interested in learning how to evaluate opportunities in financial investments.

Probability, Normal, Lognormal distribution properties, Decision making under uncertainty

Cleaning and pre-processing financial data, Exploratory Data Analysis in Finance.

Use of Regression in Finance, Building Models using Accounting Data, Understanding stock price behaviour, time series analysis in finance.

Quick introduction to R and Python, Understanding data in finance, sources of data, Using R for analysis of data.

Cash flow statement – Prepare and Analyse, Modelling and forecasting of financial statements.

NPV, IRR – Concept, application, and issues, Use of real options for better financial outcomes.

Cash flow simulation using a real financial statement, Credit risk modelling

Valuation of Stocks, Forecasting stock prices using machine learning, News analytics 

(accessing news using web scrapping) and sentiment analysis in finance

Introduce to students the concepts of strategic management, and sensitize them to ethical norms in their professional functioning.

The course will help students to understand the concepts and discuss the issues of strategic management enabling them to apply various strategy formulation techniques in decision making.

Concept of Strategy, Nature, Scope, Process and importance of Strategic Management, Formal Planning and Strategic Intent, Strategic Planning Process, Strategic Management Vs. Operational Management, Ethics in Strategic Management, Concept of Mission and VMOST model-Vision, Mission, Goals & Objectives, Strategy and Tactics, Hierarchy of strategy – Corporate, SBU, Functional, Culture and Strategy, Importance of culture in strategy execution. Strategic Intent through Vision and Mission Statements.

Introduction, Strategy Formulation, Process in Strategy Formulation, Strategy Implementation and its Stages, Reasons for Strategy Failure and Methods to Overcome, Strategy Leadership and Strategy Implementation, Strategic Business Units (SBUs): Operational Strategy– Financial Strategy– Marketing Strategy– Human Resource Strategy. Environmental Scanning, SWOT analysis, TOWS , PESTEL Analysis, Competition Analysis: Porter’s Five Forces Theory, Generic strategies, Competitive Advantage, Value chain analysis, McKinney’s 7s frame work.

Developing Alternative Strategies - Stability, Growth, Turnaround, Retrenchment, Diversification, vertical integration, Horizontal integration, Strategic alliance, merger and acquisition, Divestment. Strategic Business Portfolio analysis– BCG & GEC matrix, and Arthur D Little, Differentiation, Game Theory. Strategic Choice, Strategy communication and activation. Balance Scorecard framework. Strategic Implementation: Resource Allocation –budgets - Organization structure – Matching structure and strategy- Behavioural issues – Corporate Culture.

Strategic Control and Evaluation: Introduction, Strategy Evaluation, Strategic Control, Difference Between Strategic Control and Operational Control, Concept of Synergy and its Meaning, Key Stakeholder’s Expectations

Strategies for Multinational Corporations: Introduction, Multinational Corporations (MNCs), Benefits of MNCs, Limitations of MNCs, Business Strategies of MNCs, Techniques Employed by MNCs to Manage Markets, MNC, transnational companies (TNCs) and Global Companies.

2. Strategic Management: An Integrated Approach, Charles W.L. Hill and Gareth R. Jones Cengage Publishers, 12e

3. Hill, Charles W L (2014): International Business, McGraw Hill Publication, 10th Edition

4. Varma, Sumati (2014): International Business, Pearson

2. Rao, P Subba (2014): International Business, Himalaya Publishing House, 4th Edition

3. Hill, Charles W L (2011): Global Business Today, 6th Edition, TMH

4. Daniel and Radebaugh (2010): International Business, Pearson Education, 12th Edition

5. Cherunilam, Francis (2011) International Business, PHI Learning Pvt Ltd, 5th Edition

Course Description:

Derivatives, including options, futures and forwards, are financial instruments that can be used for risk management, speculation, and for arbitrage activities. This course covers derivatives valuation and risk management and demonstrates the strengths and weaknesses of different models and illustrates and exemplifies how valuation models and risk measures are applied in the financial industry.

Course Objectives:

The aim of the course is to introduce the students to various tools and techniques of financial risk management and applications thereof

Types, Participants and functions, Development of exchange traded derivatives, Global derivatives markets, Exchange traded vs. OTC derivatives markets, Derivatives trading in India. Understanding Forwards and Futures -Markets Introduction, Key features of futures contracts, Futures vs. Forwards, Trading, Clearing and Settlement Systems, Determination of futures and forward prices, Hedging Strategies using Futures, Interest rate, Commodity and Currency Futures

Options terminology, Types of Options, Options pay off, Properties of Stock Options, Trading Strategies involving options. Greeks and Factors affecting options pricing, Option pricing models including Binomial Option Pricing Model and Black Scholes Option Pricing Model, Pricing of index options

Interest Rate Swaps, Cross Currency Swaps, FRA, Pricing of Swaps, Flavoured Swaps, Equity and Commodity Swaps

Exotic Options, Using Monte Carlo Methods to Price Exotic Options, Swaptions, Credit Derivatives including Credit Linked Notes; Credit Default Swaps; Total Return Swaps, HJM and LMM model of Interest Rate Derivatives, Real Options

Financial risks management through Options, futures and derivative securities. Assessment of financial asset risks, credit risks, interest rate & debt securities, value at risk. Capital adequacy risk, Operational risks in banks, IFRS & Basel norms.

1. Hull, John C., Options, Futures and Other Derivatives, Prentice Hall, Latest Edition

Recommended Readings

1. Dubofsky, D.A. and Miller, T.W., Jr., Derivatives: Valuation and Risk Management, Oxford, Latest Edition 2. Broyles, J., Financial Management and Real Options, Wiley, Latest Edition 3. Bhalla, V.K., Financial Derivatives: Risk Management, New Delhi: S. Chand, Latest Edition

Students are evaluated for each course on the basis of written examination and continuous internal assessments. Each paper carries maximum of 100 marks and is evaluated as follows:

·       Written Examinations consists of:

•        Mid Semester Exam – 50 Marks (2hours duration)

•        End Semester Exam –50 Marks(2hoursduration)

•    In aggregate for each paper, for internal and end semester put together, at-least 45% Marks must be secured to pass in that paper.

Course Description: This course will introduce students to the world of fixed-income securitiesand their markets, yield measures, risk factors, and valuation measures anddriverswith the treasuryfunctions.

Course Objectives: Provide in-depth knowledge of fixed income securities market and an understanding of fixed income, equity and other structured products along with its applications.Understanding the treasury function and raising different forms of capital.

Analysis of Fixed Income Securities Bond Valuation & Interestrate Models;Yield Curveand Term Structure Analysis Determinants of bond yields; Fixed income derivatives: T-bill, t-note, t-bond derivatives, Euro-bond and municipal bond derivatives, Pricing fixed income derivatives: cost of Carry model– under perfect & imperfect market conditions.

Measuring & managing Interest rate risk, currency risk & credit risk; Micro hedging &Macro Hedging; Concept of yield curve risk – passive & active portfolio strategies

Introduction to structured finance products; Concept of Securitization; Fixed income structured finance products;Mortgage backed and asset backed securities; collateralized bond obligations(CBO); Application of structured products in various financial areas.

Principle protected note;Equity trading strategies involving options: Call & Put spreads, Straddle, Calendar spreads. EquityExotics: Asianoptions, Barrier options.

Other Structured Derivatives: Complex structured derivatives based on multi-asset payoff (Hybrid Products based on instruments from different asset classes); Effect of correlation between various asset classes on product price. Credit derivatives Default Probabilities; Credit derivatives; Credit Default swap(CDS); Credit Linked note(CLN); Modeling default correlations; First to default baskets; Convertible bonds.

Concept, Functions, Meaning, Structure, overview of risk management, Treasury organisation and structure, A treasury control framework, Treasury performance management, Complying with Sarbanes-Oxley, Corporate credit ratings.

Cash Management, Objectives, Cash Flow forecasting, working capital management, excess liquidity, short term investing, using long term instruments, rolling down the curve,short term borrowing etc

Loan Finance, Debt securities, Bond risk management, equity funding, alternative sources of financing, Asset Finance, Project Finance, Leasing, Hire Purchaseetc.

Foreign currency risk management, other risks involved in treasury operations,corporate treasury, bank treasury, managing financial risk, Enterprise risk management, Accounting for treasury. Issues relating to LIBOR transition and its impact.

2.     Fixed Income Securities: Valuation, Risk, and Risk Management by Pietro Veronesi

2.     Suresh M. Sundaresan. Fixed Income Markets and Their Derivatives, International Thomson Publishing, Latest Edition

3.     Frank AFabozzi.The hand book of Mortgage backed Securities. Probus Publishers, Latest Edition

4.     F. J. Fabozzi. The Handbook of Fixed Income Securities. Tata Mc Graw Latest Edition

Students are evaluated for each course on the basis of written examination and continuous internal assessments.Each paper carries maximum of 100 marks and is evaluated as follows:

•        Written Examinations consistsof:

•        Mid Semester Exam – 50 Marks(2 hours duration)

•        End Semester Exam –50 Marks(2 hours duration)

•        In aggregate for each paper, for internal and end semester put together,atleast 45% Marks must be secured to pass in that paper.

Organizations, large and small, are inundated with data about consumer choices. But that wealth of information does not always translate into better decisions. Knowing how to interpret data is the challenge -- and marketers in particular are increasingly expected to use analytics to inform and justify their decisions. 

Course Objectives

Marketing analytics enables marketers to measure, manage and analyse marketing performance to maximize its effectiveness and optimize return on investment (ROI). Beyond the obvious sales and lead generation applications, marketing analytics can offer profound insights into customer preferences and trends, which can be further utilized for future marketing and business decisions. This course, gives students the tools to measure brand and customer assets, understand regression analysis, and design experiments as a way to evaluate and optimize marketing campaigns.

Analysing Sales at True Colors Hardware, Analysing Sales at La Petit Bakery, Analysing How Demographics Affect Sales, Combination Charts, Using a PivotChart to Summarize Market Research Surveys, Ensuring Charts Update Automatically When New Data is Added, Making Chart Labels Dynamic, Summarizing Data with a Histogram, Using Statistical Functions to Summarize Marketing Data

Estimating Linear and Power Demand Curves, Using the Excel Solver to Optimize Price, Pricing Using Subjectively Estimated Demand Curves, Price Bundling, Why Bundle? Using Evolutionary Solver to Find Optimal Bundle Prices

Regression and Neural Nets, Using Neural Networks, Using NeuralTools to Predict Sales, Using NeuralTools to Forecast Airline Miles.

Why Logistic Regression Is Necessary, Logistic Regression Model, Maximum Likelihood Estimate of Logistic Regression Model, Using StatTools to Estimate and Test Logistic Regression Hypotheses, Performing a Logistic Regression with Count Data, Clustering Cities, Using Conjoint Analysis to Segment a Market.

Modeling the Relationship between Spending and Customer Acquisition and Retention, Basic Model for Optimizing Retention and Acquisition Spending, An Improvement in the Basic Model.

Conditional Probability, Bayes’ Theorem, Naive Bayes Classifier, Linear Discriminant Analysis, Model Validation, The Surprising Virtues of Naive Bayes, Text Mining Definitions, Giving Structure to Unstructured Text, Applying Text Mining in Real Life Scenarios.

Marketing Analytics, A Practical Guide to Improving Consumer Insights Using Data Techniques (2nd ed.). Mike Grigsby. ISBN: 9780749482176.

Marketing Analytics, A Practical Guide to Improving Consumer Insights Using Data Techniques (2nd ed.). Mike Grigsby. ISBN: 9780749482176.

Course Description

HR Managers must align HR data and initiatives to the organization’s strategic goals. Understanding of HR analytics enables HR professionals to make data-driven decisions to attract, manage, and retain employees, which improves ROI. It helps HR managers and leaders make informed decisions to create better work environment and maximize employee productivity. It has a major impact on the bottom-line when used effectively.

Course Objectives

This programme aims at developing analytical skills which will help HR professionals to conduct comprehensive analysis of data to develop and streamline their HR policies and strategic decisions of organisation. HR analytics entails data-based understanding and management of employees in an organization. It enables data-driven decisions about people's issues in an organization. The topics covered under this course include not just descriptive, prescriptive and predictive analysis; but would also enable the understanding of the various forms of employee-related data available in an organization and their contribution to informed decision-making. 

Introduction to HR Analytics, Essential Formulas, Types of HR Metrices

Working with quantitative and qualitative data, Basic HR processes and importance of Analytics, HR Audit and Benchmarking using Analytics

Data Driven Staffing, Diversity, Human Resource Planning Metrics, Employee Engagement (Talent retention)

HR Cost Benefit, Data Driven Performance Management System, Training and Development Metrics

Competency modeling using Data Analytics, Labor relations, HR analytics for decision making using SPSS

Essential Readings:

1.     The New HR Analytics, Jac Fitz-Enz. ISBN: 9780814416440

Fundamentals of HR Analytics: A Manual on Becoming HR Analytical. Fermin Diez, Mark Bussin, Venessa Lee. ISBN: 9781789739640

Essential Readings:

1.     The New HR Analytics, Jac Fitz-Enz. ISBN: 9780814416440

Fundamentals of HR Analytics: A Manual on Becoming HR Analytical. Fermin Diez, Mark Bussin, Venessa Lee. ISBN: 9781789739640

Course Description

Supply Chain Analytics-the art and science of applying data analytics to assess and improve supply chain performance. A supply chain is a complex system with conflicting objectives of cost efficiency and customer satisfaction. Supply chain management is becoming increasingly data-driven.

Course Objectives

The students will obtain hands-on experience on the application and the financial impact of analytics in integrated supply chain and logistics planning, Inventory and Transportation, Competitive Analysis and Benchmark, Data Analysis, Supply Chain Planning, Distribution and Logistics.

Introduction to Supply Chain Analytics, Business analytics

Getting Started with Supply Chain Analytics, Big data and data visualization, Supply chain performance measurement systems, Business analytics models;

Using Supply Chain Analytics to Enhance Supply Chain Strategy Processes

Using Supply Chain Analytics to Enhance Supply Chain Design Processes

Using Supply Chain Analytics to Enhance Supply Chain Execution Processes

Using Supply Chain Analytics to Enhance Supply Chain People Processes

Issues in Big Data, Issues in Systems, User satisfaction levels, How to Deal Effectively with Organisational Social Issues

The future of supply chain analytics, Places to use and implement supply chain analytics, Where not to use supply chain analytics

 As per the curriculum of the MSc Finance and Analytics program, students must undergo a mandatory Industry Internship Training for a period of 60 days after they complete the semester II in Accounting / Finance / Investment / Banking / Insurance /Auditing & Taxation.

• Profile of the organization.
• Mission, objectives & strategies of the organization.
• Organization design & structure
• Policies & procedures followed.
• Products, competitors.
• SWOT analysis of the organization.
• Key result areas of the organization.
• Sales development.
• System of accounting followed.
• Significant factors for success.
• Performance appraisal system.
• Financial highlights.
• Future plans for growth of the organization.
• Sustainability.
• Students should interact with the faculty mentor & report the progress made.

The industry internship would be assessed on the following parameters:

 Part-A: Report Evaluation (carries 50% weightage)

·       Reporting regularity with your mentor during internship : 10%

·       Internship completion certificate from the corporate : 10%

·       Internship coverage : 30%

 Part-B:  Presentation and Viva-Voce (carries 50% weightage)

·       Student Presentation : 30%

·       Viva by two internal/external faculties other than the mentor : 20%

  Total : 100 marks

Course Description The course helps students to understand the moral, social, and ethical considerations regarding the privacy and control of consumer information and big data, from data collection and storage to understand feedback loops in analysis.

Course Objectives The course aims to help students to answer questions such as who owns data, how do we value privacy, how to receive informed consent and what it means to be fair, before using it.

What are Ethics, agreement about right and wrong, Difference between Ethics and Regulations, Data Science Needs Ethics, Human Subjects Research and Informed Consent, Limitations of Informed Consent, Data Ownership, Limits on Recording and Use. 

Definition of privacy, History of Privacy, Degrees of Privacy, Modern Privacy Risks, Anonymity. Types of data privacies like PI data, HIPAA (healthcare) compliance data, financial data.

Defining validity, Choice of Attributes and Measures, Errors in Data Processing, Errors in Model Design, Managing Change. Algorithmic Fairness, Correct but Misleading Results, P Hacking, reporting and interpretation.

Societal Impact, Ossification, Surveillance. (Laws pertaining to data protection in India), international laws. 

Students are evaluated for each course on the basis of written examination and continuous internal assessments. Each paper carries maximum of 100 marks and is evaluated as follows:

·       Written Examinations consists of:

•        Mid Semester Exam – 50 Marks (2hours duration)

•        End Semester Exam –50 Marks(2hoursduration)

•    In aggregate for each paper, for internal and end semester put together, at-least 45% Marks must be secured to pass in that paper.

Course Description The data management and cybersecurity course will help students to learn how to deal with the increasing threat of cybercrime that is associated with the rapid technological advancement of today’s world.

Course Objectives The course will help students in identify, analyze and remediate computer security breaches by learning and implementing the real-world scenarios in Cyber Investigations, Network Security.

Threats that are related to current and emerging trends, cybersecurity awareness, high profile cybercrime statistics and methods, the importance and functions of Governance, Risk Management, and Compliance in Cybersecurity program management, best practices in risk management including the domains of risk assessment and risk treatment, the structure and content of Cybersecurity-related strategy, plans, and planning. types of vulnerabilities and frauds in different domains eg. Financial and Banking, Ecommerce, Telecom, GDPR.

International and industry-specific cybersecurity regulations, challenges to organisation, multiple security regulations, Define key concepts and terminology in Cybersecurity, threats to cybersecurity, strategies to identify and remediate vulnerabilities in information assets, the systemic components (including personnel) necessary for an effective cybersecurity program, NIST Framework. 

Data Integrity and Security, digital security, Data volume and velocity, Big data, multiple data sources, data diversity, Data (dis)organization, Unique data storage requirements, Security tools, Inflexible reporting and query systems. 

The threats to data from information communication technology (ICT), the issues and practices associated with managing network security, Identify the practices, tools, and methodologies associated with assessing network security, the components of an effective network security program. Phishing attacks on sites, digital advertising spoofing, Search indexing

Hacking attempts, web site defacement, denial of service attacks, information disclosures, natural and man-made cybersecurity disasters, the components of a cybersecurity contingency planning program, contingency strategies including data backup and recovery and continuity of cybersecurity operations, the components and structure of an effective cybersecurity disaster recovery program, the components and structure of an effective cybersecurity incident response program. Digital ecosystem, Cloud computing.

1. The Realities of Securing Big Data 1st Edition, by Davi Ottenheimer

2. Big Data: A Primer (Studies in Big Data), by Hrushikesha Mohanty, Prachet Bhuyan and Deepak Chenthati

1. Identity and Access Management: Business Performance Through Connected Intelligence by Ertem Osmanoglu

2. Blue Team Handbook: Incident Response Edition: A condensed field guide for the Cyber Security Incident Responder by Don Murdoch GSE

3. The Practice of Network Security Monitoring: Understanding Incident Detection and Response by Richard Bejtlich

4. Data Management for Researchers: Organize, Maintain and Share Your Data for Research Success by Kristin Briney 

Students are evaluated for each course on the basis of written examination and continuous internal assessments. Each paper carries maximum of 100 marks and is evaluated as follows:

·       Written Examinations consists of:

•        Mid Semester Exam – 50 Marks (2hours duration)

•        End Semester Exam –50 Marks(2hoursduration)

•    In aggregate for each paper, for internal and end semester put together, at-least 45% Marks must be secured to pass in that paper.

As per the instruction given in the class

The student is required to work diligently on the given live project.