Semester | Subjects | Topics |
---|
1 | Introduction to Data Analytics, Mathematics for Data Science, Programming Fundamentals, English and Communication Skills | Basics of Data Analytics, Algebra, Calculus, Python/R programming, Communication principles |
2 | Database Management Systems, Data Structures and Algorithms, Statistical Methods for Data Science, Environmental Studies | SQL, NoSQL, Trees, Graphs, Descriptive Statistics, Inferential Statistics, Environmental awareness |
3 | Operating Systems, Object-Oriented Programming, Data Preparation and Cleaning, Business Communication | Linux, Windows, Java/C++, Data wrangling, Business writing, Presentation skills |
4 | Machine Learning, Web Technologies, Big Data Analytics, Ethics in Data Science | Supervised learning, Unsupervised learning, HTML, CSS, JavaScript, Hadoop, Spark, Ethical considerations in Data Science |
5 | Deep Learning, Cloud Computing for Data Analytics, Visualization Techniques, Elective 1 | Neural networks, AWS/Azure for Data Analytics, Tableau, Power BI, Elective subject based on student's interest |
6 | Capstone Project, Internship, Elective 2, Elective 3 | Project work in Data Analytics, Practical work experience, Two elective subjects based on advanced topics or specialization areas |
This table is a simplified example and does not cover all details such as the elective subjects' options, practical lab sessions, or the specific tools and software that might be used. For detailed and accurate information, please refer to the official curriculum provided by the IMS Institute of Management Study.