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Introduction to the Integrated M.Sc. in Computational Statistics and Data Analytics

The demand for data analytics and processing experts is on the rise as the need to handle large amounts of data increases across all domains. Multinational companies rely on data scientists for the proper usage of the vast amounts of data they gather. This program is designed to equip students with the skills to learn, understand, and practice advanced statistical software related to data science and machine learning approaches, with a focus on rendering solutions to industrial applications.

Program Objectives and Highlights

The integrated program is designed with an objective to offer fundamental concepts of computational statistics and data analytics, as well as advanced subjects related to data science applications. This will provide better placement opportunities for students. The program focuses on subjects in statistics, machine learning, and offers an opportunity for a clear understanding of related tools in the data science domain. Key highlights of the program include:

• A curriculum designed for applied learning
• Students learn through real-time applications and projects
• Soft skill training
• A capstone project in the final semester

Curriculum and Course Structure

The program covers a wide range of subjects, including:

• Fundamentals of statistics
• Probability of random variables
• Statistical methods for data analysis
• Distribution theory and its applications
• Sampling techniques
• Linear algebra and numerical methods
• Discrete mathematics
• Statistical inference
• Predictive modeling
• Design and experiments
• Programming for data analysis
• Computational statistics for data science
• Statistics computing for data analysis
• Applied forecasting methods
• Operations research for data analysis
• Multivariate statistical analysis
• Statistical quality control
• Database management systems
• Design and analysis of algorithms
• Total quality management
• Non-parametric statistics
• Machine learning for data analysis
• Modeling and simulation
• Applied econometric analysis
• Biostatistics
• Big data analytics
• Decision modeling techniques
• Actuarial statistics
• IoT data analytics
• Deep learning for analytics
• Data warehousing and data mining
• Web technologies
• Decision support systems
• Artificial intelligence for data analytics
• Data engineering for analytics
• Software quality and testing
• Cloud computing techniques
• Programming in C
• Objective-oriented programming
• Advance Java

Facilities and Resources

The program features specially designed, world-class laboratories for SPSS, MATLAB, R, and Python programming, among others.

Career Opportunities

The master's program in computational statistics and data analysis is designed to cater to the fastest-growing job scenarios, with a big demand for analytics, data analysis, and data mining. The curriculum emphasizes the principles of statistics and analytics and encompasses courses like time series analysis, statistical quality control, data visualizations, and machine learning. Students are exposed to intuitively analyze data and are equipped with marketable skills built on a solid foundation to take up positions like:

• Data analyst
• Quality control manager
• Business analyst
• Data engineer
• Data manager
• Statistician
• System analyst Or pursue a research career.