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NSHM KNOWLEDGE CAMPUS
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 | Lump Sum Payment | Yearly Payment Payment Deadline | Half-Yearly Payment |
| Within 15 days of Admission | INR 1,58,000 | INR 1,01,100 | INR 53,700 |
| By 30th Nov, 2021 | INR 53,700 | ||
| By 31st May, 2022 | INR 67,400 | INR 35,800 | |
| By 30th Nov, 2022 | INR 35,800 | ||
| Total Fees: | INR 1,58,000 | INR 1,68,500 | INR 1,79,000 |
Durgapur Admission Fee of Rs. 45000/- and Alumni Fee of Rs 5000/- to be paid during Admission
Payment Date | Lump Sum Payment | Yearly Payment Payment Deadline | Half-Yearly Payment |
| Within 15 days of Admission | INR 83,000 | INR 53,100 | INR 28,200 |
| By 30th Nov, 2021 | INR 28,200 | ||
| By 31st May, 2022 | INR 35,400 | INR 18,800 | |
| By 30th Nov, 2022 | INR 18,800 | ||
| Total Fees: | INR 83,000 | INR 88,500 | INR 94,000 |
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