Overview
B.Sc. (Hons./Hons. with Research) Statistics and Data Science is an undergraduate course in the School of Basic and Applied Sciences of SGT University. The program is created according to the NEP 2020 framework with the focus on high levels of theoretical background, practical, data-driven skills, and research orientation.
B.Sc. (Hons./Hons. with Research) Statistics & Data Science is a four-year (eight semesters) course with multiple entry/exit as per the NEP 2020 that combines the traditional statistical knowledge and skills with contemporary data science, analytics and computational methodologies. The curriculum is directed towards students who are interested in the acquisition, analysis, and interpretation of data to facilitate decision making in science, industry, business, technology and public policy.
The multidisciplinary curriculum provides the students with the knowledge of statistical reasoning, models of probability, the analysis of data, the fundamentals of machine learning, and methods of research to prepare them to work in the industry and to continue their education in higher academic institutions. The program promotes critical thinking, problem solving, ethical data practices and learning-based research.
The B.Sc. (Hons./Hons. with Research) Statistics & Data Science program includes a wide range of subjects that comprehensively cover statistics, mathematics, computing, and data analytics.
Key areas of course modules include:
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- Sampling Technique
- Linear Models and Multivariate Analysis
- Statistical Quality Control and Optimization Technique
- Programming with R / Python for Data Analysis
- Database Management and Data Handling
- Machine Learning Techniques
- Time Series Analysis and Forecasting
- SQL with RDBMS
- Research Project / Dissertation
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Some of the core subjects include:
- Sampling Technique
- Linear Models and Multivariate Analysis
- Statistical Quality Control and Optimization Technique
- Programming with R / Python for Data Analysis
- Database Management and Data Handling
- Machine Learning Techniques
- Time Series Analysis and Forecasting
- SQL with RDBMS
- Research Project / Dissertation
