The Bachelor of Technology in Information Technology (Data Science) (B.Tech-IT (DS)) is a four-year undergraduate program that equips students with the knowledge and skills to extract valuable insights from data. The program covers various topics, including data mining, machine learning, statistical analysis, and big data. Students also gain hands-on experience with data science tools and technologies.
Duration: Four Years | Full Time
Eligibility: Applicants should have passed 10+2 or equivalent examinations with Physics, Mathematics, and English as compulsory subjects along with Computer or Chemistry or Biotechnology Biology or any technical vocational subjects as optional with a minimum of 50% marks (45% in case of reserved categories) taken together in Physics, Mathematics and any one of the optional subjects
Helping students apply Data Science principles, tools, and techniques to model real-world business problems, analyze them, and suggest a suitable solution by communicating relevant findings.
Acquainting students with the recent development and application of Big Data Analytics in social and web media firms for predictions and recommendations.
Helping students recognize various issues in everyday business, apply Data Science to understand, and make well-reasoned, data-driven management decisions to help organizations get an edge over their competition.
Providing insight into leading analytic practices so that students can design and lead iterative learning and development cycles and ultimately produce new and creative analytical solutions
Mathematics and Statistics
Fundamentals of mathematics and statistics, including linear algebra, calculus, and probability theory, form the foundation for data science algorithms.
Programming Languages
Proficiency in programming languages commonly used in data science, such as Python or R, for data manipulation, analysis, and visualization.
Data Analysis and Visualization
Techniques for exploring and visualizing data sets to communicate insights effectively.
Machine Learning
Introduction to machine learning algorithms for classification, regression, clustering, and recommendation systems.
Data Mining and Big Data Technologies
Understanding techniques for mining patterns and knowledge from large datasets and having exposure to big data technologies like Hadoop and Spark.
Database Systems:
Knowledge of database systems and SQL for adequate data storage, retrieval, and management.
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Classroom Lectures
Traditional classroom lectures provide theoretical foundations in mathematics, statistics, programming languages, and core data science concepts.
Practical Labs and Workshops
Hands-on practical sessions and workshops where you apply concepts learned in lectures. This could involve coding exercises, data analysis projects, and using relevant tools and technologies.
Projects
Individual and group projects to apply data science techniques to real-world problems. These projects help develop practical skills and provide experience working with large datasets.
Case Studies
Analyzing real-world case studies allows students to understand how data science methods are applied in different industries and scenarios.
Programming Assignments
Assignments and coding exercises to strengthen programming skills in languages like Python or R, which are commonly used in data science.
Machine Learning Applications
Practical application of machine learning algorithms through projects and exercises involving classification, regression, clustering, and recommendation systems.
Data Visualization
Learning to create compelling data visualizations using tools like Matplotlib, Seaborn, or Tableau to communicate insights.
Data Engineer
Data Architect
Data and Analytics Manager
Business Intelligence Analyst
Data Mining Specialist
Statistician
Machine Learning Engineer
Database Administrator
Database Developer
Cloud Solutions Architect
Information Security Analyst
Penetration Tester (Ethical Hacker)
Cybersecurity Consultant
Network Security Engineer
Cloud Security Specialist
Security Software Developer
Compliance Analyst
IT Risk Analyst
Security Researcher