Module 1: Business Intelligence Description: This module introduces students to the fundamental concepts of Business Intelligence (BI) and its applications in various industries. Topics covered include data visualization, reporting techniques, data warehousing, and decision support systems. Students will gain hands-on experience with BI tools and learn how to extract valuable insights from data to support organizational decision-making processes.

Module 2: Machine Learning Description: In this module, students will delve into the principles and techniques of machine learning. They will explore supervised and unsupervised learning algorithms, including regression, classification, clustering, and dimensionality reduction. Practical applications of machine learning in areas such as predictive analytics, pattern recognition, and recommendation systems will be emphasized through case studies and projects.

Module 3: Big Data Analytics Description: Big Data Analytics module focuses on the processing, analysis, and interpretation of large and complex datasets. Students will learn various techniques for handling big data, including distributed computing frameworks like Hadoop and Spark. Topics include data preprocessing, exploratory data analysis, predictive modeling, and text mining. Real-world case studies and hands-on exercises will provide students with practical skills in extracting meaningful insights from massive datasets.

Module 4: Mathematics for Analytics Description: This module provides the mathematical foundation necessary for understanding advanced analytics techniques. Topics covered include linear algebra, calculus, probability theory, and statistics. Students will learn how to apply mathematical concepts to solve problems in data analysis, optimization, and machine learning. Practical exercises and applications in analytics will reinforce theoretical knowledge.

Module 5: Big Data Architecture Description: Big Data Architecture module focuses on the design and implementation of scalable and efficient data infrastructures. Students will study concepts such as data storage, distributed file systems, data processing pipelines, and architectural components like data lakes and data warehouses. Through hands-on projects and case studies, students will gain practical skills in designing, deploying, and managing big data architectures to support analytics applications.

Module 6: Data Science Description: This module provides a comprehensive overview of the interdisciplinary field of data science. Students will learn about the entire data science lifecycle, including data acquisition, cleaning, exploration, modeling, and interpretation. Topics covered include feature engineering, model evaluation, ensemble methods, and ethical considerations in data science. Practical projects and real-world datasets will allow students to apply data science techniques to solve complex problems across various domains.

Module 7: Dissertation Description: The dissertation module provides students with an opportunity to conduct in-depth research on a topic of their choice within the field of analytics. Under the guidance of a faculty advisor, students will design and execute a research project, analyze data, draw conclusions, and present their findings in a formal dissertation. This module allows students to demonstrate their analytical skills, critical thinking, and ability to contribute to the advancement of knowledge in the field of analytics.