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The two-year M.Sc. Program on Applied Statistics and Data Analytics is intended for the students who aspire to excel in data analytics. It provides a spectrum of basics to advanced statistical methods and their applications to both conventional and IT industries that enable to tackle emerging problems. The curriculum covers emerging topics such as Big Data Analytics, Pattern Recognition and Marketing Analytics to deal with vast data from business, healthcare, sensors, Internet, surveys, social media, astronomy, human space exploration and operations, and aeronautics. In addition to the regular curriculum, students are encouraged to take up summer/winter In-plant training/Internship. This will help students to learn about using data mining techniques to analyze data, discover patterns, suggest analytical models to identify and respond to various patterns such as structural pattern, creational pattern and behavioral pattern, and then enrich the enactment of business, technological, industrial and healthcare processes by implanting the analytical models within the consistent operational applications. A full length project is included in the curriculum to be completed in the final semester. Students are expected to take up such projects in industries. This helps students to gain knowledge on working conditions and industrial requirements when they are employed.


Semester 1
Course code Course L T P Credit ES
20MAT505 Linear Algebra 3 1 0 4 A
20MAT506 Probability Theory and Estimation 3 1 0 4 B
20MAT507 Data Structures and Algorithms 3 0 2 4 C
20MAT508 Optimization Techniques 3 1 0 4 D
20MAT509 Introduction to Data Science 3 0 2 4 E
20MAT510 Python Programming 3 0 2 4 F
18CUL501 Cultural Education 2 0 0 P/F G
Total 24
Semester 2
Course code Course L T P Credit ES
20MAT515 Statistical Inference and Design of Experiments 3 1 0 4 A
20MAT516 Multivariate Statistics and Regression Analysis 3 1 0 4 B
20MAT517 Machine Learning 3 0 2 4 C
20MAT518 Big Data Analytics and Hadoop 3 0 2 4 D
20MAT519 Data Mining 3 1 0 4 E
20MAT520 Data Security 3 0 0 3 F
18AVP501 Amrita Value Programme 1 0 0 1 G
Total 24
Semester 3
Course code Course L T P Credit ES
20MAT606 Statistical Quality Control and Reliability 3 1 0 4 A
20MAT607 Introduction to Deep Learning 3 0 2 4 B
Elective I 3 0 0 3 D
Elective II 3 0 0 3 E
Elective II 3 0 0 3 F
20MAT690 Live-in-Lab.@/ Open Elective* 2 0 0 2 J
Total 19
Semester 4
Course code Course L T P Credit ES
20MAT696 Dissertation 10 P
Total 10

Total credits for the programme: 77

Course code Course L T P Credit ES
20MAT651 Taugchi Techniques 3 0 0 3 D/E
20MAT652 Special Distribution Functions 3 0 0 3 D/E
20MAT653 Pattern Recognition 3 0 0 3 D/E
20MAT654 Stochastic Process 3 0 0 3 D/E
20MAT655 Queuing Theory 3 0 0 3 D/E
20MAT656 Market Analytics 3 0 0 3 D/E
20MAT657 Survival Analysis 3 0 0 3 D/E
20MAT658 Sampling Techniques 3 0 0 3 D/E
20MAT659 Demography and Actuarial Statistics 3 0 0 3 D/E
Course code Course L T P Credit ES
20MAT660 Official Statistics 3 0 0 3 D/E
20MAT661 Healthcare Analytics 3 0 0 3 D/E
20MAT662 Computational Biology 3 0 0 3 D/E
20MAT663 Computer aided drug designing 3 0 0 3 D/E
20MAT664 Reinforcement Learning 3 0 0 3 D/E
20MAT665 Social Network Analytics 3 0 0 3 D/E
20MAT666 Mining of Massive Datasets 3 0 0 3 D/E
20MAT667 Parallel and Distributed Systems 3 0 0 3 D/E



A pass in B. Sc. in Mathematics / Statistics / Computer Science/ B. Tech. Computer Science with minimum 60% marks.


Based on the marks obtained in the qualifying examination and interview.

Program Overview

Salient Features of the Program
  • Gain knowledge in computer programming and statistical software related to Applied Statistics and Data Analytics.
  • Placements in both conventional and software Industries.
  • Scope for doing research for those who aim to be a teacher, scientist or research associate in highly reputed national and international institutions.
  • Gain competency in the preparation of national level scholarship tests such as UGC/CSIR – NET and GATE.
About the curriculum and syllabi

The curriculum and syllabi for this program are on par with any reputed educational institution in India and abroad. Syllabus is framed in such a way that the candidates will be competent enough to take up the tests like NET, SLET, GATE etc. The Program offers comprehensive instruction in the theory, methods and application of Statistics. The courses include computer-intensive classes as a tool to support the analysis and interpretation of statistical data.

Job opportunities

Employment opportunities for people qualified with M.Sc. in Applied Statistics and Data analytics are available in the form of teaching positions in various educational institutions all over India and abroad. They are also eligible to work as Scientists and Research Associates in highly reputed organizations such as ISRO, DRDO and CSIR. In addition, lucrative jobs are available in software industries. In our integrated mathematics batch, while some of the students have got jobs in both public and private sector banks, many current final year students have already secured internships/jobs in industries (manufacturing/ software/insurance). The prospective recruiters for the students of Applied Statistics and Data Analytics are:

Current Students’ Feedback

The department always focuses on evolving with the changing needs of our stakeholders (students/parents/recruiters). The feedback from our stakeholders reveals that the curriculum and syllabi for Applied Statistics and Data Analytics are on par with any reputed educational institution in India and abroad. Students are finding it more interesting as the program is a combination of both Statistics and Data Analytics. They learn advanced programming languages and application of statistical software which are useful in industries and IT companies. The intersection of Statistics and Data Analytics has enabled more sophisticated ways in learning. They have learnt to implement, and do experiments with data analysis techniques and algorithms which will lead them to be effective practitioners in data handling at the end of second year. Students can identify and deploy appropriate modeling and methodologies in order to extract meaningful information for decision making. They can also analyze big data and make data-driven predictions through probabilistic modeling and statistical inference.

Program Outcomes
  • Knowledge in Statistics and Data Analytics: Understand the basic concepts, fundamental principles and the scientific theories related to Statistics and Data Analytics.
  • Abstract thinking: Ability to absorb and understand the abstract concepts that lead to various advanced theories in mathematics and Statistics.
  • Modelling and solving: Ability in modelling and solving problems by identifying and employing the appropriate existing theories and methods.
  • Advanced theories and methods: Understand advanced theories and methods to design solutions for complex statistical problems in Data Science.
  • Applications in Engineering and Sciences: Understand the role of statistics and apply the same to solve the real life problems in various fields of study.
  • Modern software tool usage: Acquire the skills in handling scientific tools towards problem solving and solution analysis in Data Science.
  • Environment and sustainability: Understand the significance of preserving the environment towards sustainable development.
  • Ethics: Imbibe ethical, moral and social values in personal and social life leading to highly cultured and civilized personality. Continue to enhance the knowledge and skills in applied statistics and data analytics for constructive activities and demonstrate highest standards of professional ethics.
  • Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
  • Communication: Develop various communication skills such as reading, listening, and speaking which will help in expressing ideas and views clearly and effectively.
  • Project management and Research: Demonstrate knowledge, understand the scientific and management principles and apply these to one’s own work, as a member/ leader in a team to manage projects and multidisciplinary research environments. Also use the research-based knowledge to analyse and solve advanced problems in data sciences.
  • Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.

Fee Structure

To be Updated

Program Fees for the year 2019-2021
Campus Tuition Fees
per sem
Tuition Fees
Hostel & Mess
Per Year
  Updating Soon * #

Facilities at a glance

  • Central Library
  • Hostel Accomodation
  • Sports Facilites
  • Banking Facilities
  • Transport
  • Medical Services
  • Canteen
  • General Store
  • ICTS
  • Reprographic Facility
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