M.Sc. Applied Statistics and Data Analytics
The aim of the post graduate education is to provide high quality education as well as a supportive learning environment for the students to reach their full academic potential. The higher education has to inculcate in students the spirit of hard work and research aptitude to pursue further studies in the nationally/internationally reputed institutions as well as prepare them for a wider range of career opportunities in industry and commerce.
The syllabi are framed in such a way that it provides a more complete and logical framework in almost all areas of Statistics.
By the end of the first year, the students should have
- Acquired the basic as well as advanced knowledge in Probability theory, Distribution theory
- Acquired the basic as well as advanced knowledge in Sampling theory and Estimation theory.
- Gained knowledge in database management system and machine learning
- Gained knowledge regarding statistical computing using excel and R softwares
By the end of the second year, the students should have
- Gained practical knowledge in computer programming and statistical softwareslike SPSS, SAS and Python related to Applied Statistics and Data Analytics.
- Completed a project which helps them to gain knowledge on working conditions and industrial requirements when they are employed.
- 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.
- Gained competency in the preparation of national level scholarship tests such as UGC/CSIR – NET and GATE.
STRUCTURE OF M.Sc. APPLIED STATISTICS AND DATA ANALYTICS
There are twenty courses including two practical courses spread equally in all four semesters in the M.Sc. Programme. Distribution of courses is as follows. There are seventeen core courses common to all students. Semester I, Semester II and Semester III will have five core courses each and Semester IV will have two core courses and three elective courses. The three elective courses can be chosen as per the interest of the students, availability of faculty and academic infrastructure.
The project of the PG program should be relevant and innovative in nature. The type of project can be decided by the student and the guide (a faculty of the department or other department/college/university/institution). The project work should be taken up seriously by the student and the guide. The project should be aimed to motivate the inquisitive and research aptitude of the students. The students may be encouraged to present the results of the project in seminars/symposia. The conduct of the project may be started at the beginning of Semester III, with its evaluation scheduled at the end of Semester IV. The project is evaluated by external and internal examiners.
A viva voce examination will be conducted by the two external examiners at the time of evaluation of the project. The components of viva consists of subject of special interest, fundamental concepts, topics covering all semesters and awareness of current and advanced topics.
ST1C01TM Probability and Measure Theory
ST1C02TM Distribution Theory
ST1C03TM Analytical Tools for Statistics
ST1C04TM Sampling Theory
ST1C05TM Database Management System
ST2C06TM Estimation Theory
ST2C07TM Stochastic Processes
ST2C08TM Multivariate Distributions
ST2C09TM Statistical Computing I
ST2C10TM Machine Learning
ST3C11TM Testing of Hypotheses
ST3C12TM Design and Analysis of Experiments
ST3C13TM Multivariate Analysis
ST3C14TM Time Series Analysis
ST3C15TM Data Mining and their Applications
ST4C16TM Big Data Analytics and Artificial Intelligence
ST4E01TM Operation Research
ST4E02TM Statistical Quality Control
ST4C17TM Statistical Computing II