About the Program
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.
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.
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:
* and many other software companies and industries.
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.
- 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.
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