Master of Arts / Master of Sciences in Analytics (Self-Financed Programme)

Description

Programme Objectives:

Develop Advanced Analytical Skills: Equip students with cutting-edge analytical tools and techniques to evaluate and solve complex sustainability challenges critically.

Foster Multi-Dimensional Understanding: Promote a deep comprehension of how environmental, developmental, scientific, technological, and justice-related issues intersect and influence each other.

Enhance Research Competence: Encourage rigorous research skills to develop innovative and practical solutions contributing to sustainable development and social justice.

Integrate Analytics with Sustainability: Blend analytical methodologies with sustainability concepts to address dynamic challenges at both local and global levels.

Promote Innovative Problem-Solving: Inspire students to think creatively and develop innovative approaches to applying analytics in solving sustainability issues.

Engage in Interdisciplinary Learning: Implement a multidisciplinary teaching approach that combines perspectives from various fields, providing a holistic education on sustainability and analytics.

Cultivate Global and Local Perspectives: Encourage students to analyze sustainability challenges from both global and local perspectives, appreciating their unique and interconnected nature.

Develop Ethical Leadership: Instil a strong sense of ethical responsibility and leadership in students, motivating them to advocate for and implement sustainable and just practices in their professional lives.

Solve Real Life Problems: To equip students with the ability to solve real-world problems across business, organizational, and institutional domains by integrating classroom learning with hands-on exposure through three internships totalling six months.

Data Driven Understanding of Rural India: To familiarize students with rural socio-economic contexts and the role of data analysis through a fifteen-day rural immersion focused on primary survey design, data collection, and documentation.

Programme Outcomes

Proficiency in Analytical Tools and Techniques: Graduates will use advanced analytical tools and methodologies to critically assess and address complex sustainability challenges, ensuring they can provide data-driven solutions in various professional contexts.

Interdisciplinary and Holistic Perspective: Graduates will possess a deep, multidisciplinary understanding of the connections between environmental, developmental, scientific, technological, and justice-related issues, enabling them to approach sustainability challenges from a comprehensive and integrated viewpoint.

Research Excellence and Innovation: Graduates will demonstrate outstanding research capabilities, contributing to developing innovative solutions that promote sustainable development and social justice. They will be skilled in designing and conducting research projects that address real-world problems.

Global and Local Contextual Understanding: Graduates will be adept at analysing sustainability challenges from both global and local perspectives, understanding their unique and interconnected nature. This will enable them to develop and implement contextually relevant and impactful solutions.

Enhanced Employability in Analytics: Graduates will be highly employable in analytics and equipped with the skills and knowledge to excel in various roles such as data analysts, sustainability consultants, research analysts, and policy advisors. Their ability to integrate analytics with sustainability principles will make them valuable assets in organisations committed to sustainable practices.

Industry Relevant Analytical Competence: Students will be able to apply analytical concepts and tools to real-life problem contexts, translate theoretical knowledge into practical solutions, and enrich classroom learning through insights gained from field experience, thereby developing industry-ready analytical competence.

Rural immersion: Students will be able to design and conduct primary surveys in rural settings, collect and transcribe field data, and apply analytical techniques to generate insights from rural data.

Distribution of Credit Hours:

Semesterwise Courses:

Semester wise and Course wise Distribution Credits

Overview of Courses and Credits

COURSE CONTENTS

Social Network Analysis and Organisations:

The Idea of social Network Analysis: Basic Concepts and History

Social Networks and Organization: Organisational Contexts and Applications

Research Design & Methodology: Social Network Analysis Research Design, Relations and Attributes, Development of Social Network Analysis, Fundamental concepts in Network Analysis

Analytics: Sociogram and Graphs, Node, Line, Counting Rules, Directed and Undirected Tie, Density, Notations: Graph Theoretic, Sociometric and Algebraic, Actor Centrality, Degree Centrality, Closeness Centrality, Betweenness Centrality, Main Component, K Cores, N Cliques, Clan

Analytics and Visualization (UCINET & R): Data handling, transformation, visualization, analysis of cohesion, region, sub-groups, ego-centric networks, centrality and power, roles and position, and 2-mode network

Mathematical Foundations for Analytics:

Philosophy of Mathematical Models, Understanding data: numbers and strings, Basic algebraic operations, Set theory, logics and their applications, Basics mathematical functions: Equations and graphs, Limits, continuity, Differential Calculus and Integration, Differential and Difference Equations, Basics of Optimization of equality functions, An overview of optimization of inequality functions, Matrix Algebra, Mini Project on foundation of mathematics for analytics

Statistics for Analytics:

Data structures, Sampling and Error, Descriptive Statistics, Probability, Probability Distribution, Inferential Statistics and Hypothesis testing, Student Project

Introduction to Tools for Data Analysis & Visualisation:

Advanced Excel and Visual Basics, Tableau and Power Bi as visualization platforms, Mini Project

Introduction to Computational Tools:

Python basics: interface and writing basic scripts, Python dictionaries: an introduction to NumPy, SciPy, matplotlib, and pandas

Logic, control flow, filtering, and loops in python, Introduction to database in python, Manipulating data frames in python

Data visualization in python, Statistical analysis in python, Introducing R : What it is and how to get it, guide to notation, inputting data, Data : Descriptive statistics and tabulation, Introduction to Graphical Analysis, Matrix Algebra: Vectors and matrices, the rank of matrices, determinants, inverse, eigen analysis of real symmetric matrices, matrix calculus

Introduction to GIS and Spatial Analytics:

An overview of GIS- introduction to QGIS, Map making principles and basic cartography, Spatial data properties and data manipulation, Vector based spatial data analysis, Raster data models and their application in spatial analysis, Spatial data editing and map creation, Geoprocessing, Open source and Web GIS applications

Business History of India:

Introducing Oldest Indian Trade and Businesses: Money in Ancient India, The Tamil Merchant: Pioneers of International Trade, Muziris in Kerala. Punjabi Khatri Merchants on the Silk Road, The Gujrati Merchants from Kachh. Merchants of Bombay: Business Pioneers of the Nineteenth Century, The Marwaris in West Bengal: From Jagat Seth to the Birlas.

Impact of East India Company 1700-1850: Transition to Colonialism; Merchants and Bankers in Land Trade; Merchants in Sea Trade; European Merchants; Indian Ocean-Going Merchants; Parsis of Bombay; Inland Merchants (Marwaris, Banjaras, Business community in Bengal etc); Decline of Textile Crafts; Rise of a New State; Business of the ports; Strong Trade and weak Banking; Industrial Firms.

Capital, an Empire and Indian Entrepreneurs, 1850-1930: Meaning of Trade; Global Trading Firms; Indian Traders; Indian Family Firms in Banking; European Industrialists; Indian Industrialists; J. N. Tata and Tata Steel; Other Industries.

Indian Business and Economy, 1930-1950: The origins of Protection for Industry; The success and Failure of Discriminating Protection: Steel and Textiles; Business in the interwar Years—Family led Business groups—Birla, Shri Ram, Walchand Hirachand, Dalmia and other Groups; Multinational Companies; Corporate Banking; Business and Politics after 1940s, Partition and its aftermath.

Modern and Contemporary Business Models in India: Family Business in India— Mafatlal Group, Tata Group, Birla Group, Godrej Group, Goenka Group, Jhindal Group, Ambani Group; Alternative Model: Khadi, Amul, Lijjat Papad, Trade Facilitation Centre-SEWA, Mumbai Dabbawala, SHG led Enterprises.

Human Resource Analytics:

Role of Data in Human Resources, Defining of HR analytics and Problems in HR,HR Data Acquisition and Management, Understanding organizational system: Appraisals, Labour Issues, Time and Budgetary Allocations, Workforce Analytics, Toolkits for HR Analytics (Dashboard, Tableau, Excel, Power BI), Predictive Analytics in HR (Process led Design, transition management, impact analysis, communications), Real Time HR Analytics

Financial Analytics:

Introduction to Applied Economics for Finance and Financial Data: Uncertainty, Portfolio Theory, Index Models, CAPM and APT, Fixed Income Securities, Volatility: Data, Models and Risk, Econometrics of Volatility: Likelihood Assessment, Stochastic Process, Asset, Pricing and Spline GARCH, Modelling and Forecast of Financial Data, Time Series Forecasting, Extreme Value Distribution, Copula and Tail Dependence, Technical Analysis and Efficient Markets, Hypothesis, Overview of Numerical Methods in Finance with Introduction to Financial Instruments (Derivatives, Options, Swaps)

Sustainable Development Goals (SDGs) Analytics:

Introduction to SDGs: Concept of Sustainability and its philosophy and evolution, History and Process of SDGs- Previous, 17 Goals, 169 Targets and 231 Indicators, SDG Global indicator framework, SDG Monitoring (International and National Levels), SDGs working structures, UNDP _UNSDS, other vital offices, Voluntary National Report_ India SDG Report-NITI Aayog. Analytics and Methodological considerations in SDGs: UNSD Database on SDGs, SDG Dashboards, SDG Monitoring, Methodology of Index creation, Interpreting SDG Index and distance maps, Big Data possibilities for SDGs. Localisation of SDGs: SDGs to the micro situation –developing SDG targets and indicators for local context from Global Indicator Framework, mapping SDGs in organisations and communities, developing monitoring frameworks for SDG reporting, Contextualising SDGs in our daily life.

Digital Marketing:

The context of digital marketing: Connected Customers , Digital Subcultures, Traditional to Digital Marketing. Marketing in the Digital Economy: New Customer Path (Aware, Appeal, Ask, Act, Advocate). Performance: Purchase Action Ratio, Brand Advocacy Ratio Channel, Brand, Sales and Service: Typology and Practices Brands as Friends: Digital Anthropology (Social Listening, Netnography, Emphatic Research), Human Centric Brands Content Marketing: Goal Setting, Audience Mapping, Ideation and Planning, Content Creation, Distribution, Amplification, Marketing Evaluation, Marketing Improvement, Integrating Traditional and Digital Media: Omnichannel Marketing, Webrooming, Role of Analytics, Search Engine Optimisation (SEO): History, Exploring SEO strategy, Content, Mobile, Location, Penalisation

Applied Game Theory:

Games in Normal Form: Beliefs and Best Responses, Dominant and Dominated Strategies, Mixed Strategies, Nash Equilibrium, Prisoner’s Dilemma, Two Person Zero- Sum games, Games in Extensive Form: Backward Induction, Credibility and Subgame Perfection, Repeated Games, Other Applications, Games of Incomplete Information: Bayes-Nash equilibrium, Auctions, Signalling Games, Applications: Sustainability, Cooperation, Justice, Understanding of Markets

Health Analytics:

Introduction to Data in Health Care, Data types in Health care: Surveys, Clinical trials, Medical records, Claims, Other sources, Uses and limitations, Healthcare Data Acquisition and Management, Applied Statistics for Healthcare Analytics: Epidemiological concepts, Mortality, Morbidity, DALY and Risk Adjustment, Measurements of Health Care Performance, Quality and Costs, Data Mining in Health Care (Administrative and Clinical System Data, Standards, and Protocols, Ethical issues), Visualization of Health Analytics

Data Science & Database Management:

Law of large numbers, Multidimensional Layers of Data: Cross Section, Time Series, Panel, Data Preprocessing, Cleaning and Reduction, Stationarity in Data, Network and Spatial Data, Introduction to Database, Database Management, Concept of Relational Database (Relation, Attributes, Constraint), Querying Database (Filtering, Merging, Data Search, Structured Query Language). Database Model Design (Entity-Relationship Modelling), Data Storage (High Level and Low Level), Accessing and Merging Records across Database, Structuring of Database by removing redundancies (unstructured to structured), Query Optimization and Transactions Management, Lab Based Application

Introduction to Micro Macro Data:

Introduction to Micro and Macro Data: Data Genesis, Philosophy and Importance of Data, Micro Data vs Macro Data , issues, challenges features. Introduction to Indian and International Statistical Systems: Origins and Development, Scope and importance, Census of India, NSSO Databases, ASI Data, PLFS Data, Economic Census, UN Databases, Penn World Table. Working with a Macro Database: descriptive statistics, time series analysis, distributions and inferential statistics. Working with a Micro Database: descriptive statistics, time series analysis, distributions and inferential statistics

Multivariate Predictive Analysis I:

Nature and Data in Predictive Analysis, Regression: Basic Ideas, Problem of Estimation and CNLRM, Two variable regression (Interval Estimation and Hypothesis Testing), Extension of two variable regression and multiple regression, Dummy Variable Regression, Relaxing assumptions of CNLRM (Multicollinearity, Heteroscedasticity and Autocorrelation), Econometric Modelling (Specification and testing) Introduction to Panel Data, Fixed and Random Effects, Revisiting CNLRM in Panel Data

Multivariate Predictive Analysis II:

Dynamic Panel Data Models, Non Linear Regression Models and concept of ML, Qualitative Response Regression Models LPM, Logit and Probit Models, Tobit and Beta Regression, Multinomial and Multivariate Modelling, Time Series Econometrics: Concepts, Time Series Econometrics: Forecasting

Introduction to Machine Learning:

An overview of essential mathematics for machine learning, Regression approaches in machine learning

Statistical learning theory: binary classification, Classification and decision tree, Clustering algorithms (k-means clustering, hierarchical clustering), Data pre-processing and dimensionality reduction-factor analysis, principal component analysis

Introduction to Supervised learning, Application of random forest, support vector machine, K-nearest neighbour approaches: Model selection - bootstrapping and their applications, Semi-supervised machine learning algorithms. Introduction to information theory and its applications.

Bayesian Statistics:

Philosophical Discussion on Bayesian Approach: Belief, Exchangeability, Representation and Conditional Probability, Basics: Bayes Theorem, Prior Beliefs, Updating Prior with Data, Posterior Beliefs, Bayesian Decision and Point Estimation, Bayesian Inference and Credible Sets, Model Performance and best fits, Bayesian Analysis using Simulation: Direct Sampling and Importance Sampling, Sample Generation Using Bayesian Approaches: MCMC, Computing Marginal Likelihood, Model Comparison and Specification, Applications of Bayesian Models: GLR, VA, State-Space, Non Linear Models

Dissertation Stage 1: Introduction to Research Methods and Designs

Basic Understanding on Nature and Process of Research: Definition and characteristics of research, The scientific method and research process, The role and significance of research in various fields, Fundamentals of Types of Research.

Quantitative Research: Definition and characteristics of quantitative research, Key methods: surveys, experiments, and secondary data analysis, Introduction to statistical analysis

Qualitative Research: Definition and characteristics of qualitative research, Key methods: interviews, focus groups, and ethnography, Data collection and analysis techniques

Mixed Methods Research: Definition and characteristics of mixed methods research, Integration of quantitative and qualitative data, Designing a mixed methods study

Identification of Research Problem: Understanding the importance of research problems, Techniques for identifying research problems, Evaluating the significance and feasibility of research problems

Problem Identification/Conceptual Note: Developing a clear and concise conceptual note, Justifying the research problem, Formulating research questions and objectives

Natural Language Processing:

Introduction: Origins and challenges of NLP – Language Modelling: Grammar-based LM, Statistical LM – Regular Expressions, Finite-State Automata – English Morphology, Transducers for lexicon and rules, Tokenization, Detecting and Correcting Spelling Errors, Minimum Edit Distance, Fundamental of Speech Processing.

Word Level Analysis: Unsmoothed N-grams, Evaluating N-grams, Smoothing, Interpolation and Backoff – Word Classes, Part-of-Speech Tagging, Rule-based, Stochastic and Transformation-based tagging, Issues in PoS tagging – Hidden Markov and Maximum Entropy models.

Syntactic Analysis: Context-Free Grammars, Grammar rules for English, Treebanks, Normal Forms for grammar – Dependency Grammar – Syntactic Parsing, Ambiguity, Dynamic Programming parsing – Shallow parsing – Probabilistic CFG, Probabilistic CYK, Probabilistic Lexicalized CFGs – Feature structures, Unification of feature structures.

Semantics and Pragmatics: Requirements for representation, First-Order Logic, Description Logics – Syntax-Driven Semantic analysis, Semantic attachments – Word Senses, Relations between Senses, Thematic Roles, selection restrictions – Word Sense Disambiguation, WSD using Supervised, Dictionary & Thesaurus, Bootstrapping methods – Word Similarity using Thesaurus and Distributional methods.

Discourse Analysis and Lexical Resources: Discourse segmentation, Coherence – Reference Phenomena, Anaphora Resolution using Hobbs and Centering Algorithm – Coreference Resolution – Resources: Porter Stemmer, Lemmatizer, Penn Treebank, Brill’s Tagger, WordNet, PropBank, FrameNet, Brown Corpus, British National Corpus (BNC).

Lab Applications: Tokenization, Filtering Stopwords, POS tagging, Stemming, Lemmatization, Text analysis, Speech recognition & analysis

Data Ethics:

Introduction to Data Ethics and Governance: Understanding the importance of ethics and governance in data analytics. Overview of ethical issues in data collection, analysis, and decision-making. Legal and regulatory landscape related to data ethics and governance. Case studies highlighting ethical challenges in data analytics

Privacy and Consent: Understanding privacy rights and regulations (e.g., GDPR, CCPA), Ethical considerations in data privacy and consent management, Techniques for obtaining informed consent in data collection and analysis, Privacy-enhancing technologies and practices.

Social and Cultural Implications of Data Analytics: Understanding the societal impact of data analytics, Ethical considerations in using data analytics for social good, Addressing bias and discrimination in algorithmic decision-making, Ethical challenges in emerging technologies (e.g., AI, IoT, blockchain)

Case Studies on Data Ethics, Privacy Issues and Governance Models

Dissertation Stage II: Analytics and Thesis Submission

Introduction to Research Design: Overview of research design, Importance of research design in a dissertation, Types of research designs Literature Review: Importance, Identification, and Analysis.

Web of Science and Scopus Literature Search: Introduction to Web of Science and Scopus databases, Effective search strategies Managing search results. Bibliometric Analysis: Introduction to bibliometric analysis, Tools and techniques for bibliometric analysis, Interpreting bibliometric data.

Introduction to LaTex and Bibliography: Basics of LaTex, Creating and managing bibliographies with LaTex, Using LaTex for scientific writing. Integrating Theory into Research: The role of theory in research, Selecting appropriate theoretical frameworks, Integrating theory into research design.

Formulation of Research Instrument: Types of research instruments, Designing questionnaires and interview guides, Validity and reliability in research instruments. Pooled Data and Analysis: Understanding pooled data, Techniques for pooled data analysis, Interpreting pooled data results.

Scientific and Systematic Writing: Principles of scientific writing, Structure of a research paper, Writing systematically and coherently.

This stage involves the student making the final submission of the dissertation.

Perspectives on Science, Technology and Society (course content) ask Praveen habitat school:

The Origins of Modern Science: Preliminary definitions of science and technology, Ubiquity of attempts to understand the natural world in the pre-modern era, The transition to "science", The separation of mental and manual labour, The transmission of knowledge in pre-modern times.

The First Scientific Revolution: Its origins in the Renaissance, The Newtonian revolution, The rise of the mechanistic world view.

Science in the Era of the Industrial Revolution: Definitive separation of science from philosophy, The Darwinian revolution, The disenchantment of Nature, The advance of secularisation

Science in the Modern Era: The Second Scientific Revolution, The scientific revolutions of the 20th century, The crisis of positivism and the response, The realist/materialist response, Science as an institution, Scientific institutions

Technology and the Industrial Revolution: The making of the Industrial Revolution, The Industrial Revolution and the role of craftsmen, The development of machines and the labour process

Technology, Innovation, and Advanced Capitalism: Technology in the economic perspective, Technical change and technological advance in economic, theory, Inducements to technical change, Economics of scientific research and inventions

Science, Technology, and Development: The emergence of science as a public good, Path-dependency and lock-in, Innovation at the firm-level, Innovation and institutions, Science, technology, and development in the Indian context

Critical Overview of Indian Science and Technology: Science in the colonial era, The Nehruvian perspective and Gandhian critique, Post-independence era in Indian S&T, S&T and self-reliance, Critical views of Indian S&T policy and technology, transfer in the era of globalisation, Comparisons with other nations (Korea, China, Brazil, etc.)

Note:The curriculum structure is tentative and subject to change.

Fee Structure:

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