Course Duration | Course Level | Fee | Mode of Study | Institute Type |
---|---|---|---|---|
3 Years | Undergraduate | Not Specified | Full Time | University |
Semester | Subjects | Topics |
---|---|---|
1 | Introduction to Programming, Mathematics for Data Science, Fundamentals of Computer and IT | Programming basics, Algebra, Statistics, Computer hardware and software basics |
2 | Data Structures and Algorithms, Database Management Systems, Principles of Data Science | Arrays, Linked Lists, Sorting and Searching, SQL, NoSQL, Data Science lifecycle |
3 | Object-Oriented Programming, Operating Systems, Data Analysis and Visualization | Classes and objects, Memory management, Linux basics, Python/R for data analysis, Tableau/PowerBI |
4 | Machine Learning, Web Technologies, Statistical Methods for Data Science | Supervised and Unsupervised Learning, HTML/CSS/JavaScript, Probability, Hypothesis testing |
5 | Big Data Analytics, Cloud Computing for Data Science, Elective I | Hadoop, Spark, AWS/Azure for Data Science, Elective based on specialization |
6 | Project Work, Elective II, Elective III | Capstone project, Advanced topics based on chosen electives |
Course Module | Important Topics |
---|---|
Introduction to Data Science | Basics of Data Science, Data Types, Data Science Lifecycle |
Programming for Data Science | Python Programming, R Programming, Data Structures |
Mathematics for Data Science | Linear Algebra, Calculus, Statistics & Probability |
Data Management and Manipulation | SQL, NoSQL, Data Cleaning, Data Transformation |
Data Analysis and Visualization | Exploratory Data Analysis, Tableau, Matplotlib, Seaborn |
Machine Learning | Supervised Learning, Unsupervised Learning, Model Evaluation |
Deep Learning | Neural Networks, TensorFlow, Keras |
Big Data Technologies | Hadoop, Spark, Big Data Analytics |
Capstone Project | Real-world Data Science Project, Project Management, Presentation Skills |