M. Sc. (Data Science and Analytics)

In the proposed curriculum for m. Sc. Data science and analytics, apart from teaching core subjects, the students are also offered training in programming languages and open softwares such as r, python, etc to handle real life problems through the practical classes. 

As part of the program the students are also given training in computing softwares like SPSS, SAS etc. The program is so designed that on successful completion, the students would be able to pursue research or higher studies in the areas of Data Science, Data Analytics, Statistics, Mathematics, Computer Science, Economics, Management and allied fields. Moreover, emerging areas like Bayesian Inference and Computing, Stochastic Modeling, Time Series Forecasting, Internet of Things and Fraud Analytics are included in the curriculum. There will be an open course during third semester to encourage interdisciplinary studies and research. This can be selected from among courses offered by any other school/department/centre in the university. 

In the emerging data driven world, Data Science and Data Analytics are receiving much acceptance in the national and international levels for making wise decisions. Data analytics is  an essential field that brings together Data, technology, information, statistical analysis all in one platform. Every organization in private/ public sector creates a large volume of data from almost every area of daily activity. 

Analyzing such data has huge potential to predict the future of the organization. A good amount of knowledge is very necessary in the field of data management, machine learning, natural language processing as they are the key factors in Data Science. The decision to establish a School of Data Analytics along with the School of Mathematics & Statistics in the Mahatma Gandhi University, Kottayam during 2020-21 is a most appropriate decision and will help the research and career opportunities for prospective students.

The following are some of the emerging areas of applications in this respect.

• Internet of Things (IoT): Analytics tools and techniques for dealing with the massive amounts of structured and unstructured data generated by IoT will continue to gain importance.
• Finance and Banking: Creating newer business models or frameworks that leverages the available data to facilitate financial institutions to monetize data to deliver superior customer values.
• Data Security: Fraud Security Analytics are already transforming intrusion detection, differential privacy, digital watermarking and malware countermeasures.
• Health Care: Health Care Analytics and Predictive Analytics enable the examination of patterns in various healthcare data in order to determine how clinical care can be improved while limiting excessive spending.

There has been much interest in Bayesian Inference in Data Analytics in recent years. It is a way to get sharper predictions from the data, particularly when there is not much data available and when one want to juice every last bit of information from it. During the last three decades, Actuarial Science has gone through revolutionary changes due to the implementation of high speed computers and modern theory. It applies mathematical and statistical methods to assess risk in insurance, finance and other industries. Official Statistics make information on economic and social development accessible to the public, allowing the impact of government polices to be assessed and thus improving accountability.

With the emergence of new diseases like AIDS, SARS, COVID 19 etc. more interest has come to analyze such epidemic data to study the dynamics of its spread. In the modern data driven world Data Science and Data Analytics are the principal tools for making wise decisions and drawing valid conclusions. We expect students to select the elective courses to ensure employment and research opportunities in emerging areas. The main thrust of the program will be to enable students to understand basic concepts and applications through real data sets using computer programs like R and Python in the data analytics lab rather than just imparting theory. Industry interaction, case studies and projects will be main components of all courses.

Eligibility for admissions:

• B.Sc. Degree in Mathematics or Statistics or Computer Science main / B.Sc. (triple main) with Mathematics, Statistics and Computer Science as main subjects or BCA, B.Sc./B.Voc. Data Science or Data Analytics, B.E./ B. Tech.( CS or IT) with at least 50% marks (CGPA 5.0 out of 10.00 under grading system) for the optional subjects taken together, provided the candidate has studied at least 2 courses in Probability / Statistics at degree level.
• Admission will be made through a common admission procedure (CAP) on the basis of a Common Admission Test (CAT) or a special test conducted for specific programmes by the Departments, as the case may be. Admission may be based on the written test alone or written test and interview or on the basis of the marks obtained in the qualifying examinations as well as the marks obtained in the written test, the interview as decided by the Faculty Council of Schools / Centres / Institutes from time to time in accordance with CSS regulations 2020.
• This is a regular program of study in which no private / distance mode will not be conducted. However, under extreme situations like COVID 19 pandemic, classes may be conducted online as per UGC guidelines and University regulations . The duration of PG program shall be 4 semesters. The duration of each semester shall be 90 working days (18-20 weeks) including internal and external examinations. Odd semesters are normally from July to December and even semesters from January to June. A student shall be permitted to register for the program at the time of admission. A student who registers for the course shall complete the course within a period of 4 years from the date of commencement of the program.

Courses and Credits:

• Every Program conducted under Credit Semester System shall be monitored by the Departmental Academic Committee. In all the programmes, three kinds of courses are offered; Core Courses (3-4 credits), Elective Courses (2-4 credits) and Open elective courses (4 credits). Core courses are offered by the Schools/Department/Centre/Institute conducting the programme. Elective Courses shall be selected either from the same School/Department or from some other School / Centres / Institutes. Any course chosen by a student, generally from an unrelated discipline/subject, from Schools/ Centres / Institutes other than own School/Department/Center, with an intention to seek broad exposure, is called an Open course. Students are required to take one open course in the Third semester. The details are given in Table of Courses and Credits.
• A Semester shall be worth a minimum of 16 credits and maximum of 24 credits. The total credits for a postgraduate program shall be between 80 to 88. The maximum credits for core courses, electives and open courses in postgraduate programmes shall be 64, 20 and 4 respectively. A minimum of 4 credits and maximum of 20 credits shall be set apart for the project work/dissertation. The compulsory project/dissertation to be completed in the 4th semester of a postgraduate programme shall be prepared by the student under the guidance of a member of the faculty or, in the case of subjects, which so demand, an external guide, to be decided by the school‘s faculty council. University Departments / Schools are permitted to offer online UGC approved MOOC courses from SWAYAM platform as electives during 3rd and 4th semesters subject to the condition that the aggregate credits for such online courses shall not exceed 20% of total credits. These shall be coordinated by a faculty coordinator subject to the approval of the faculty council.

Evaluation and Grading

• The CSS rules and regulations 2020 for University Departments and Schools will be applicable. Evaluation scheme for each course shall contain 2 parts (a) Internal Continuous Assessment (CA) and (b) End Semester Assessment (SA). 40% weightage is given to Continuous internal Assessment and 60 % to End Semester external Assessment. Both internal and external evaluation is carried out in accordance with the grading system as given in the CSS regulations. Normally odd semester ESA is through examiners in the department. But for even semesters, external examiners will also be included in the board of examiners and there will be double valuation. Questions will be mainly of applied nature rather than just theory.

Objectives:

• To provide advanced level teaching and training in theory and applications of Data Science and Analytics as well as skills in computer programming and data analytics and interpretation.
• To provide a platform for talented students to become leaders in this discipline by undergoing higher studies in the subject as well as to train them to suit for the needs of the society as job providers by establishing startups and business.
• To allow more flexibility to branch out into other emerging areas of Statistics, Computer Science, Data Science and Data Analytics.
• To draw together a variety of subject areas to enable students to model real-world data from various contexts by exploring a blend of Applied Mathematics and Statistics with appropriate computing tools including free softwares like R, Python etc.
• To provide special attention to interdisciplinary areas of research and applications in describing, exploring, analyzing and comparing data with an innovative research mind in a data driven world.
• To make them familiar with emerging developments in Data Science and Big Data Analytics and their applications in various areas.
• To provide the students on the job training in industrial applications and professional development with a view to enable them to get opportunities for teaching, research and employment in India and abroad through industry academia collaborations and linkages with reputed research institutes and industries in India and abroad.

Outcomes:

After undergoing this program, students will get advanced knowledge in theory and applications in all areas of Data Science, Data Analytics, Statistical Learning, Machine Learning, Data Base Management, Artificial Intelligence, etc.

• Students have secured practical skills in statistical methods and computer programming to plan and execute projects and decision making using Data Science, Data Analytics, Machine Learning etc.
• Students are well equipped to undertake any work involving exploratory data analysis, fraud analytics, data learning, text mining etc. as future entrepreneurs.
• Students have developed skills in advanced computing softwares like R and Python for big data analytics, computing and data analysis.
• Students are well trained to take up jobs in reputed firms and MNCs etc as Data Scientists, Data Analysts, Data Engineers, Risk Analysts, Business Analysts, Financial Analysts, Decision Makers, Entrepreneurs etc.
• Students are motivated to pursue teaching and research in all emerging areas of research in theoretical and applied branches of Data Science, Data Analytics and related areas.