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Admissions Open for PG Diploma in Business and Data Analytics and Masters in Data Analytics

Postgraduate Diploma in Data and Business Analytics (One Year)

The program consists of ten courses and a project. These courses are divided in two semesters. There are eight core courses and two elective courses. A wide range of electives will be offered to best suite the choice and interest of a student. The appropriate training and usage of SPSS Statistics and SAS Enterprise Miner will be part of the course contents and course structure. The customer-based industrial project will help in broadening and in-depth understanding of the theoretical concepts, and use a practical approach to solve a real-life complex problem. In certain courses, case-based teaching will be adopted.

Master of Science Degree in Data Analytics (Two Years)

The program consists of ten courses in the first and second semester. Out of ten courses, eight are core courses and two are elective courses. The elective courses will have a wide range in the big data analytics to suite the research interest of the student. The training and the analytical usage of SPSS Statistics and SAS Enterprise Miner will enhance and update the knowledge of student in the relevant domains. The summer internship is for two months, based on available choices and the interest of the student. Third and fourth semesters will involve Master’s thesis. Collaborative industrial research projects will provide a suitable base for the Master’s thesis in accomplishing the objective of quality research work.

Course Details

Core Courses

Data Collection and Management – Principles, Tools and Platforms / (Database Management Systems): Database concepts, Basic components of DBMS, sources of data, logging, cleaning data, data representation, data models – (hierarchical, network, XML), and Stores, NoSQL database, design for performance / quality parameters, documents and information retrieval, related tools – (Postgres, OLTP, OLAP, Hadoop, Mapreduce)

Data Visualization / (Visualization and Reporting): Purpose of visualization, Multidimensional visualization, tree visualization, graph visualization and time series data visualization techniques, visual perception, cognitive issues, evaluation as well as other theory and design principles behind information visualization, understanding analytics output and their usage, basic interaction techniques such as selection and distortion, evaluation, examples of information visualization applications and systems, user tasks and analysis

Mathematics for Data Analytics: Basic probability theory, distributions and their properties, Simple and multiple regression analysis, hypothesis testing and sampling, estimation theory, least square methods, SVD, transformations, stochastic models compression techniques, Markov Models, Markov decision process and its application in sequential decision making, Poisson, Cumulative Poisson Process and its generalization, applications in different business domain, ARMA and ARIMA, Monte Carlo Simulations, application of data analytics in different domains.

Business Statistics: Descriptive statistics – uni-variate and bi-variate, residual analysis, confidence and prediction intervals regression, associations, sequencing, introduction to forecasting, design of experiments and performing basic statistical analysis of data experiments (both field and laboratory) to investigate business issues, tools for conducting basic statistics (for example SPSS and SAS), conducting the analytics on (laboratory and / or field ) data using the tools (for example, SAS, JMP, KNIME)

Systems / Business Analysis: Introduction to information system components, types of information systems, roles of business analyst, evolution and definition, industry needs and applications, process and methodologies, tools and technologies, roles and responsibilities, impact of digital marketing and unstructured data, Systems planning: Objectives, preliminary investigation, other fact-finding techniques, recording facts, Analyzing, requirements: Data flow diagrams, data dictionary, process description, evaluation alternatives, Data analytics Life Cycle: discovery, data preparation, model planning, model building, communication results and operationalization, Implementation: quality assurance, documentation, management approval, Installation / implementation, Acceptance.

Data Mining: Clustering, Association rules, factor analysis, scale development, survival analysis, data reduction using PCA, scoring new data and model implementation, improving predictive models, association and market basket analysis, advanced regression models: concepts and applications, conjoint and discrete choice analysis, design and analysis of experiment.

Operation Research: Introduction to optimization, gradient descent method, convex optimization, linear programming and its generalization (Goal Programming and multi criteria decision analysis), integer programming, dynamic programming, assignment problem, transportation problem and their applications.

Big Data Technologies: Big data definition, enterprise / structured data, social / unstructured data, unstructured data needs for analytics, Big data programming (Hadoop / HDFS, Map-reduce, event stream processing, complex event processing), evolution, purpose and use, application data stores, (NSQL databases, in-memory databases), data computing appliance (DCA) and OLAP, massive parallel processing, in-memory computing / analytics, data science, enterprise / external search, HDFS – Overview and concepts, data flow (read and write), interface to HDFS (HTTP, CLI and Java API), high availability and Name Node federation, Map Reduce developing and deploying programs, optimization techniques, Map Reduce Anatomy, Data flow framework programming Map Reduce best practices and debugging


Understanding Enterprise Processes and Analytics: Overview of domain, understanding of business pain points, understanding different types of analytics applications, financial services – claims, renewal, sales force, collections, fraud, compliance, risk, pricing, customer loyalty, pricing and promotion effectiveness etc, healthcare – evidence based medicine, comparative effectiveness research, clinical analytics, fraud/waste/abuse management etc., telecom – network optimization, subscriber profiling, churn management, collection management etc., manufacturing – demand forecasting and SKU rationalization, plant analytics, route and distribution optimization, vendor performance etc, Overview of analytics view chain – data source, ETL Data integration, data migration, MDM, modeling, reporting and visualization etc., process of scoping analytics project / use case, steps in hypothesis creation, establish critical success factors, identify reports and deliverables, data privacy and security

Machine Learning and Knowledge discovery: Supervised learning, decision trees, linear discriminant functions (SVM), neural networks, deep belief networks, density estimation methods, Bayes’ decision theory, expectation and minimization, ensemble methods, feature engineering, association rule mining, clustering techniques. Practical: evaluation of ML Techniques – cross validations, ROC, precision, recall, F-value, introduction to use of ML and KD tools such as Weka, Octave, SciLab/ equivalent libraries/ tools

Time Series and Forecasting: A survey of the theory and application of time series methods in different domains with special emphasis on econometrics. Univariate stationary and non-stationary models, vector auto-regressions, frequency domain methods, models for estimation and inference in persistent time series, and structural breaks, different methods of estimation and inferences of modern dynamic stochastic general equilibrium models (DSGE): simulated method of moments, maximum likelihood and Bayesian approach. The empirical applications will be drawn primarily from macroeconomics and different domains.

Evolutionary Programming: Introduction to evolutionary and heuristic techniques. Principles and Historical Perspectives; Application potential in optimization, dimensionality reduction, data mining and analytics, Genetic Algorithms, Evolutionary Strategies, Evolutionary Programming Introduction to Representations, Binary Strings, Real-Valued Vectors, Various Selection Strategies Introduction to Search Operators, Crossover and Mutation, Ant Colony Optimization, Pheromone mediated search and Exploration and Exploitation strategies, Particle swarm optimization basic PSO strategies and variants, different neighborhood topologies, Biogeography Based Optimization; Immigration and Emigration Strategies, Monte Carlo Methods Simulated annealing and advanced annealing strategies, Differential Evolution, Group Search Optimization, Glow worm Optimization, Firefly and other novel heuristic algorithms, Applications of evolutionary & Heuristic techniques in large scale Optimization, Combinatorial & Function optimization, Multi-objective Optimization, Pareto Front and Non-dominated Solutions NSGA and related solution strategies, Applications to large scale clustering classification, rule mining and Data driven Modeling, Variable Selection and Informative Data reduction and parameter optimization in predictive data analytics with evolutionary and heuristic techniques, Evolutionary Computing in discovering Structure and modularity of large scale networks

Multi-core Programming: Fundamental aspects of shared-memory and accelerator-based parallel programming, such as shared memory parallel architecture concepts, programming models, performance models, parallel algorithmic paradigms, parallelization techniques and strategies, scheduling algorithms, optimization, composition of parallel programs, and concepts of modern parallel programming languages and systems. Practical exercises help to apply the theoretical concepts of the course to solve concrete problems in a real-world multicore system

Game Theory: Rigorous investigation of the evolutionary and epistemic foundations of solution concepts, such as rationalizability and Nash equilibrium. Classical topics on repeated games, bargaining, and super-modular games, games, heterogeneous priors, psychological games, and games without expected utility maximization. Applications and case studies from different domains

Text Analytics: Introduction to text mining, text representation and turning into features, exploratory analysis: frequency and co-occurrence, clustering, categorization, bag of features, predictive analysis for categorization, predicative analysis for sentiment analysis, analyze data from extracted text from web, such as social media and tweets, Develop prototypes for identifying the entities mentioned in text, the relations between them, and the opinions expressed about these entities.

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