Master of Arts/Master of Science(Analytics) (Self Financed)
Location: Mumbai
School: School of Management and Labour Studies
Intake: 34
Description
Program 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: Instill 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.
Program 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.
Semesterwise Courses:
Summary of the Programme Credits
Semester |
Credits |
I |
20 |
II |
16 |
III |
24 |
IV |
20 |
Total |
80 |
Breakup of Credit across Components
Type of Courses (NEP Format) |
Credits |
Basic Course: Multi-Disciplinary |
32 |
CBCS Courses |
4 |
Community Engagement related Courses |
4 |
Internship and Fieldwork |
14 |
Research based Course |
12 |
Skill based Course |
8 |
Thematic Course (Electives) |
6 |
Semester wise and Course wise Distribution of Credits
Semester |
I (Credits) |
II (Credits) |
III (Credits) |
IV (Credits) |
Total (Credits) |
Type of Courses (NEP Format) |
|||||
Basic Course: Multi-Disciplinary |
12 |
4 |
16 |
|
32 |
CBCS Courses |
|
4 |
|
|
4 |
Community Engagement Related Courses |
|
|
|
4 |
4 |
Internship and Fieldwork |
|
4 |
4 |
6 |
14 |
Research Based Course |
2 |
|
2 |
8 |
12 |
Skill based Course |
6 |
0 |
|
2 |
8 |
Thematic Course (Electives) |
|
4 |
2 |
|
6 |
Total (Credits) |
20 |
16 |
24 |
20 |
80 |
Overview of Courses and Credits
Semester |
Course |
Credits |
Course Type |
1 |
Foundation Course-1 |
2 |
Basic Course: Multi Disciplinary |
1 |
Foundation Course-2 |
2 |
Basic Course: Multi Disciplinary |
1 |
Mathematics for Analytics |
4 |
Basic Course: Multi Disciplinary |
1 |
Statistics for Analytics |
4 |
Basic Course: Multi Disciplinary |
1 |
Introduction to Tools for Data Analysis & Visualization |
2 |
Skill based Course |
1 |
Introduction to Computational Tools |
4 |
Skill based Course |
1 |
Introduction to Research Methods |
2 |
Research Based Course |
2 |
Open Elective (CBCS) |
4 |
CBCS Courses |
2 |
Disciplinary Elective (HR Analytics, Financial Analytics, SDG Analytics, Digital Marketing, Applied Game Theory): Any two courses |
4 |
Thematic Course (Electives) |
2 |
Data Science & Database Management |
4 |
Skill based Course |
2 |
Multivariate Predictive Analysis I |
4 |
Basic Course: Multi Disciplinary |
2 |
Internship |
4 |
Internship and Fieldwork |
3 |
Multivariate Predictive Analysis II |
4 |
Basic Course: Multi Disciplinary |
3 |
Introduction to Machine Learning |
4 |
Basic Course: Multi Disciplinary |
3 |
Bayesian Statistics |
2 |
Basic Course: Multi Disciplinary |
3 |
Disciplinary Elective (Environmental Analytics, Health Analytics) |
2 |
Thematic Course (Electives) |
3 |
Dissertation Stage 1: Review of Literature & Research Design |
2 |
Research Based Course |
3 |
Natural Language Processing |
4 |
Basic Course: Multi Disciplinary |
3 |
Data Ethics |
2 |
Basic Course: Multi Disciplinary |
3 |
Internship/Field Work |
4 |
Internship and Fieldwork |
4 |
Introduction to GIS and Spatial Analytics |
2 |
Skill based Course |
4 |
Rural Immersion |
4 |
Community Engagement Related Courses |
4 |
Dissertation Stage II: Analytics and Thesis Submission |
8 |
Research Based Course |
4 |
Internship/Field Work |
6 |
Internship and Fieldwork |
COURSE CONTENTS
Mathematics 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, eigenanalysis of real symmetric matrices, matrix calculus
Dissertation Stage I: Introduction
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
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
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
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)
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
Sustainable Development Goals (SDGs) Analytics:
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
Applied Game Theory
Introduction: 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, Signaling Games
Applications: Sustainability, Cooperation, Justice, Understanding of Markets
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, Modelling Count Data: Poisson Regression, Negative Binomial and Ordinal Regression, Simultaneous Equation Modelling (Nature and Identification)
Simultaneous Equation Methods (ILS, 2SLS, 3SLS, IV), Autoregressive and Distributive Lag models, Time Series Econometrics: Concepts, Time Series Econometrics: Forecasting
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 Comparision and Specification, Applications of Bayesian Models: GLR, VA, State-Space, Non Linear Models
Environmental Analytics
Introduction to Environmental Issues: Data, Measurements and Characteristics, Economic Aspects of Environmental Problems
Statistical Descriptions of Environmental Data: Central Tendency, Dispersion,Skewness, Outliers, Data Visualization Approaches for Environment: Water, Air Quality, Land-use, Applied Spatial Econometrics, Analysis of Open Source Data in Environment (Advanced Application of GIS)
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 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
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, selectional 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
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, principle 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 application
Dissertation Stage II: Review of Literature & Research Design
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
Introduction to GIS:
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
Dissertation Stage IV: Final Dissertation
This stage involves the student making the final submission of the dissertation.
Fee Structure:
Components | M. A. Analytics | ||||
Fees | Sem I | Sem II |
Sem III | Sem IV |
|
FEE | Tuition Fee | 1,39,000 | 1,39,000 | 1,39,000 | 1,39,000 |
Examination Fee | 1,000 | 1,000 | 1,000 | 1,000 | |
CHARGES |
Field Education / Internship / Experiential Learning Charges | 6,000 | 6,000 | 6,000 | 4,000 |
IT Charges | 2,000 | 2,000 | 2,000 | 2,000 | |
Library Charges | 1,500 | 1,500 | 1,500 | 1,500 | |
Other Charges( ID Card, Convocation & Misc.) * | 2,500 | 0 | 0 | 0 | |
FUNDS | Students' Competency Fund | 0 | 1,000 | 0 | 1,000 |
Lab / Studio Fund | 7,000 | 7,000 | 7,000 | 7,000 | |
Development Fund | 10,000 | 10,000 | 10,000 | 10,000 | |
Students' Union Fund * | 500 | 500 | 500 | 500 | |
Alumni Fund * | 0 | 500 | 0 | 0 | |
Health Care Fund* | 2,000 | 0 | 2,000 | 0 | |
DEPOSITS | Caution Deposits (Refundable at the time of exit from programme on submission of No Dues Certificate) | 10,000 | 0 | ||
Semester wise Course Fee | 1,81,500 | 1,68,500 | 1,69,000 | 1,66,000 | |
Yearly Fees | 3,50,000 | 3,35,000 | |||
Total Course Fee | 6,85,000 | ||||
Institute reserves the right to revise the Fee Structure of programmes if necessary. | |||||
Expenses related to Practicum / Study tour / Rural field work / Urban field work/Winter Institute, if any, will have to be met by the students themselves at the time of the activity. | |||||
No fee concession is available for self-finanaced programmes. |