Admissions 2024

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About the Program

Data science is a “concept to unify statistics, data analysis, machine learning, and their related methods” to “understand and analyze actual phenomena” with data. In other words, the detailed study of the flow of information from structured and unstructured data available in an organization is called data science. It primarily involves obtaining meaningful insights from the data which is processed through analytical study. The current era is becoming a digital space where each organization deals with large amounts of structured and unstructured data daily. Evolving technologies are leading to cost-saving solutions for the storage and analysis of such large data. In the current era, for career progression, one needs to understand the language of data through analytical skills. Hence, it is necessary nowadays, to develop manpower with the skill to perform data analysis to get meaningful information from the data of different domains such as banking and finance, insurance, agriculture, healthcare, retail, education, social media, manufacturing, transportation, entertainment, and so on. 
With the availability of modern technologies of data storage, cleaning, and computing, the study of data science expanded beyond the boundaries of mathematics and statistics. In modern days the study of data science is constituted with the knowledge of mathematics, statistics, and computer science. Data science brings together a lot of skills of these disciplines with adequate domain knowledge to help any organization find ways to i) make major business decisions, ii) reduce costs, iii) get into new markets, iv) launch a new product or service, v) find the sentiment of the customers, vi) recruiting the best talent and so on.





Programe Objective

The primary objective of the M.Sc. in Data Science program is to develop a skilled professional workforce that is prepared to address the increasing needs in the rapidly expanding area of big data analytics. The program aims to provide skills in quantitative data analysis, data mining, data modeling and prediction, data storage and management, machine learning, big data processing, data visualization, multimedia big data, programming and communication skills. Value Added Course/ training and a large number of practical case studies have been integrated into the program to boost the learner's confidence and market acceptability.


Salient Features

  • Curriculum based on Research and Development and industry needs
  • Career prospects in major sectors such as Agriculture. Healthcare, education and Infrastructure, Banking etc,


To excel in Computer Science and Engineering education, research and project management by empowering the students with strong conceptual knowledge.


  • M1: To educate the students with basic foundation blocks of core and allied disciplines of Computer Science. 
  • M2: To provide practical skills in the advancements of the Computer Science field required for the growing dynamic IT and ITES industries.
  • M3: To sculpt strong personal, technical, research, entrepreneurial, and leadership skills. 
  • M4: To inculcate knowledge in lifelong learning, professional ethics and contribution to the society.

Eligibility Criteria

For M.Sc. (Data Science): Bachelor’s degree in Mathematics / Computer Science / Information Technology/ Data Science/Artificial Intelligence/ Computer Application from any recognized University/institute with a minimum of 50% aggregate marks or equivalent grade
Final year students can also apply


Science (Statistics, Mathematics, Physics)/ IT/ Computer Science/ Data Science/ Economics/ Engineering Graduates or its equivalent with good mathematical aptitude, basic programming skills, and inclination to pursue a career in data science. Professionals who are interested in upskilling in the field of data science.


Career Opportunities

Data Sciences: Job Roles

  • Data Science jobs for freshers may include the job of a business analyst, data scientist, statistician or data architect.
  • Big Data Engineer: Big data engineers develop, maintain, test, and evaluate big data solutions within organizations.
  • Machine Learning Engineer: Machine learning engineers have to design and implement machine learning applications/algorithms to address business challenges.
  • Data Engineer/Data Architect: Data engineers/architects develop, construct, test, and maintain highly scalable data management systems.
  • Data Scientist: Data scientists have to understand the challenges of business and offer the best solutions using data analysis and data processing.
  • Statistician: The statistician interprets the results, along with strategic recommendations or incisive predictions, using data visualization tools or reports.
  • Data Analysts: Data analysts are involved in data manipulations and data visualization.
    Business Analysts: Business analysts use predictive, prescriptive, and descriptive analyses to transform complex data into easily understood actionable insights for the users.


What can I do with an MSc in data science?
Is an MSc in data science useful?
What are the future prospects of MSc data science?
Does data science have coding?
Is there any scope in data science?
Which branch is good for data science?
Is data science easy to get a job?
Is a data science job an IT job?
Is a career in data science safe?
Which is better, data science or IT?
Is data science a stable career?


  • PE O1: To prepare graduates to be successful professionals in industry, government, academia, research, entrepreneurial pursuit, and consulting firms.
  • PE O2: To prepare graduates to achieve peer recognition, as individuals and as a team player, through demonstration of good analytical, design, implementation and interpersonal skills.
  • PE O3: To prepare graduates to contribute to society as broadly educated, expressive, ethical, and responsible citizens with proven expertise.
  • PE O4: To prepare graduates to pursue life-long learning to fulfil their goals.


  • PO 1: Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
  • PO 2: Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
  • PO 3: Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
  • PO 4: Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
  • PO 5: Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations.
  • PO 6: The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
  • PO 7: Environment and sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
  • PO 8: Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
  • PO 9: Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
  • PO 10: Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
  • PO 11: Project management and finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
  • PO 12: 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.


  • PSO 1: To model computational problems by applying mathematical concepts and solving real-world problems using algorithmic techniques.
  • PSO 2: To apply the mathematical and statistical approaches for analyzing, designing and development of computing systems in interdisciplinary applications.
  • PSO 3: To work as a socially responsible professional by drawing statistical inference using software tools in real-world problems.