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.

This Program is offered in Blended Learning (BL) Mode

From the Academic Session 2021-22 a select list of full-time and regular MAKAUT degree programs, mainly at the post-graduate levels shall be offered in Blended Learning (BL) Mode

Blended Learning (BL) Mode Highlights

  • More flexible, agile, & cost-effective. Targetted for Working Professionals.
  • 100% Full-Time Regular Degree – this is not an Online Degree Program.
  • At least 40% of the learning in face-to-face online mode will be with handpicked subject-area experts from all over the world.
  • Online Evening Classes from Monday to Friday. (From 6pm – 9pm)
  • Practicals & Lab sessions will be On-Campus (Weekends, 10 a.m – 4 p.m)
  • University examinations and assessments will be as per the norms applicable to the traditional full-time and regular degree courses of MAKAUT.

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.


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.


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 - Hybrid Mode (BL)PG

Duration - 2 Years

Fee Details

  • Kolkata


Payment Date

One Time Payment Yearly Payment Payment Deadline Half-Yearly Payment
Admission Fee INR 50,000 INR 50,000 INR 50,000
Within 15 days of Admission INR 2,51,100 INR 1,57,500 INR 83,600
on or before 30th Nov, 2023 INR 83,600
on or before 31st May, 2024 INR 1,10,000 INR 55,900
on or before 30th Nov, 2024 INR 60,800
Total Fees: INR 3,01,100 INR 3,17,500 INR 3,33,900
Course Fee including Admission :


Payment Date

One Time Payment Yearly Payment Payment Deadline Half-Yearly Payment
Within 15 days of Admission INR 64,250 INR 41,100 INR 21,900
By 30th Nov, 2022 INR 21,900
By 31st May, 2023 INR 27,400 INR 14,500
By 30th Nov, 2023 INR 14,500
Total Fees: INR 64,250 INR 68,500 INR 72,800
Course Fee including Admission :

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