Course Duration | Course Level | Fee | Mode of Study | Institute Type |
---|---|---|---|---|
3 Years | Undergraduate | Varies | Online/On-campus | University |
Semester | Subjects |
---|---|
Semester 1 | 1. Introduction to Data Analytics 2. Programming in Python 3. Mathematics for Data Science 4. Principles of Management 5. Communicative English |
Semester 2 | 1. Advanced Python for Data Analytics 2. Database Management Systems 3. Statistical Methods for Data Science 4. Organizational Behavior 5. Environmental Studies |
Semester 3 | 1. Data Visualization Techniques 2. Machine Learning Fundamentals 3. Big Data Analytics 4. Operations Research 5. Professional Ethics |
Semester 4 | 1. Deep Learning 2. Cloud Computing for Data Analytics 3. Business Intelligence 4. Elective I 5. Project Work I |
Semester 5 | 1. Advanced Machine Learning 2. Data Mining 3. Elective II 4. Elective III 5. Project Work II |
Semester 6 | 1. Internship/Project |
Module | Topics |
---|---|
1. Introduction to Data Analytics | Basics of Data Analysis, Data Types, Data Analytics Process |
2. Programming for Data Analytics | Python/R Programming, Data Structures, Algorithms |
3. Database Management | SQL, NoSQL, Data Warehousing |
4. Data Mining | Classification, Clustering, Association Analysis |
5. Statistical Methods for Data Analysis | Descriptive Statistics, Inferential Statistics, Hypothesis Testing |
6. Machine Learning | Supervised Learning, Unsupervised Learning, Reinforcement Learning |
7. Big Data Analytics | Hadoop, Spark, Big Data Processing Frameworks |
8. Data Visualization | Tableau, Power BI, Data Presentation Techniques |
9. Business Intelligence | BI Tools, Decision Making, Strategic Planning |
10. Capstone Project | Real-world Data Analytics Project |