Course Duration | Course Level | Course Tuition Fees | Mode of Study | Institute Type |
---|---|---|---|---|
3 Years | Undergraduate | Varies by Year | Full Time | Private |
Semester | Course Name | Syllabus Overview |
---|---|---|
Semester 1 | Introduction to Course | Basic concepts and principles |
Semester 2 | Advanced Topics in Course | Detailed study of advanced topics |
Semester 3 | Applied Course Concepts | Practical applications and case studies |
Course | Description |
---|---|
Programming Fundamentals | Introduction to programming languages like Python or R, focusing on syntax and basic programming concepts. |
Mathematics for Data Science | Covers mathematical concepts essential for data science, including statistics, probability, linear algebra, and calculus. |
Data Structures and Algorithms | Study of data organization, management, and operations that can be performed on data for efficient algorithm implementation. |
Database Management Systems | Introduction to database concepts, SQL, and NoSQL databases, focusing on data storage and retrieval. |
Data Analysis and Visualization | Techniques for analyzing and visualizing data to extract insights, using tools like Tableau, Power BI, or Python libraries. |
Machine Learning | Introduction to machine learning algorithms and their applications in data science. |
Big Data Technologies | Overview of big data concepts and technologies like Hadoop and Spark for processing large datasets. |
Deep Learning | Advanced machine learning techniques focusing on neural networks and deep learning frameworks. |
Cloud Computing for Data Science | Introduction to cloud platforms and their services for storing, processing, and analyzing data. |
Capstone Project | Practical project where students apply data science concepts and techniques to solve real-world problems. |