COURSE CURRICULUM

The course begins with Block 1: Data Analytics Infrastructure, introducing Business Intelligence (BI) and Data Infrastructure. Part 1 covers the foundations of BI systems within organizations, focusing on BI concepts, architecture, and the alignment of data strategies with business strategies. Part 2 delves into data warehouse design, building on students' prior knowledge of relational databases and database management systems (DBMS). Topics such as ER modeling, dimensional modeling, and data warehouse architecture are covered, with an emphasis on design considerations and emerging research in data warehousing. Block 2: Big Data Applications introduces students to advanced Big Data techniques using Hadoop Distributed File System (HDFS) and Apache Spark. The module explores data mining, machine learning, graph processing, real-time analytics with Spark Streaming, and natural language processing like sentiment analysis. It also covers business reporting tools and time series analysis with tools like SAS Enterprise Guide. Block 3: Data Mining Techniques and Methods focuses on various data mining techniques for extracting valuable insights from large datasets. Students will gain expertise in applying these techniques and interpreting statistical results in relevant domains. Tools such as SAS Enterprise Miner, Weka, and R are used for data mining applications. Block 4: Project Proposal, Planning, and Project Management (PPPM) provides the necessary grounding in research methods, including quantitative and qualitative approaches, such as surveys and case studies. Students will develop statistical literacy and practical skills in statistical applications like SAS Studio. Blocks 5 and 6: PGT Project offer students the opportunity to undertake a self-managed, in-depth research project. This allows students to apply the knowledge and skills learned throughout the course, conducting a detailed investigation into a chosen problem or research topic.