M.Sc. ‑ Applied Statistics & Analytics

Overview

The program will build the foundation in mathematics and Statistics so that participants will be able to learn and apply the fundamental Mathematical techniques used for analytics and machine learning; and learn and appreciate the Staistical concepts that form the foundation of data science. To solve problems to new situations using Data Science and Analytics by employing the knowledge of statistics, data modeling, data mining, soft computing, and big data to store, process, analyze, describe, classify business data to discover patterns and insights, and build predictive machine learning models. Besides proposing alternative solutions to complex data science problems with the help of higher order intelligence-backed strategies for data collection, validation, organization, analysis and quantitative modeling to demonstrate discoveries from data analysis for medical, financial, and scientific applications for the benefit of business and sustainable development.

Programme Education Objective

PEO1: Established professional expertise for mathematical and statistical applications to solve complex decision problems with higher insights from data.
PEO2: Demonstrated discoveries in applied statistics and analytics in all areas of human-computer interaction for business, society, and sustainable development
PEO3: Pursued lifelong learning for innovative solutions and new applications for solving real world problems exhibiting values and ethics
PEO4: Attained higher knowledge, skills, abilities, and attitude both as an individual, and as member or leader in diverse teams and in multi-disciplinary settings

Career Opportunities

Biostatistician
Statistical Epidemiologist
Data Scientist
Actuarial Analyst
Business Analyst
Financial Analyst
Economic Statistician
Quantitative Analyst

Programme Structure

Core Curriculum

Mathematics & Statistics

Linear Algebra

Review of n-dimensional vector spaces, linear independence, bases, dimension, subspaces,matrix representations, transformations, matrix decomposition, and use in modeling.

Statistics & Probability

Understanding of the principles, and concepts for probability, limit, continuity, derivative and integrals. Making use of its various applications

Time Series Analysis

Gain understanding of time series, decomposition, smoothing techniques. Apply time series concepts for data analysis and forecasting. Making use of its various applications

Stochastic Process

Gain appreciation of stochastics processes, stationarity, brownian motion, Markov chain. Apply stochastic processes for data analysis.

Computing

Introduction to Programming Languages

Programming languages for analysis – R, Python, learn object oriented concepts, develop programs for machine learning models. Software tools for advanced analytics and programming – Tableau, SPSS, Hadoop, SAS.

Optimization Techniques and Soft Computing

Algorithm complexity and optimization, biology inspired methodologies such as genetics, evolution, ant’s behaviors, particles swarming, human nervous systems to solving computational problems.

Analytics

Statistical Inference

Apply common statistical distributions, confidence intervals, parametric and non-parametric hypothesis testing, p-values, and resampling techniques.

Predictive Modeling

Develop models to predict categorical and continuous outcomes, using such supervised and unsupervised machine learning techniques such as neural networks, decision trees, logistic regression, support vector machines and Bayesian network models. Learn business analytics and big data.

Multivariate Analysis

Multivariate problems, dimension reduction techniques, assumpotions, regression and other techniques to analyze multivariate data.

Applications of Analytics

Applications of analytics in solving social, economis and industrial problems

Machine learning in solving social, economics and industrial problems. Demonstrate the applications in biostatistics, econometrics, supply chain, retail, marketing, and more.

Applications in Research

Research and development in machine learning, define and formulate research problems, perform literature review, acquire, preprocess, analyze data and make inferences.

Programme Type - PG

Duration - 2 Years

Fee Details

Kolkata
Durgapur

Kolkata Admission Fee of Rs. 45000/- and Alumni Fee of Rs 5000/- to be paid during Admission

Payment Date	One Time Payment	Yearly Payment Payment Deadline	Half-Yearly Payment
Within 15 days of Admission	INR 1,40,000	INR 90,000	INR 48,000
By 30th Nov, 2022			INR 48,000
By 31st May, 2023		INR 60,000	INR 32,000
By 30th Nov, 2023			INR 32,000
Tuition Fee Total	INR 1,40,000	INR 1,50,000	INR 1,60,000
Total Fees:	INR 1,90,000	INR 2,00,000	INR 2,10,000

Durgapur Admission Fee of Rs. 45000/- and Alumni Fee of Rs 5000/- to be paid during Admission

Payment Date	One Time Payment	Yearly Payment Payment Deadline	Half-Yearly Payment
Within 15 days of Admission	INR 95,000	INR 60,000	INR 32,500
By 30th Nov, 2022			INR 32,500
By 31st May, 2023		INR 40,000	INR 21,500
By 30th Nov, 2023			INR 21,500
Tuition Fee Total	INR 95,000	INR 1,00,000	INR 1,07,500
Total Fees:	INR 1,45,000	INR 1,50,000	INR 1,57,500