B. Sc (Data Science ) Program 2019-20
Duration: Three Years
Overview
- Every facet of modern life from social networking to scientific research, finance to healthcare , produce an immense amount of data which needs to be analysed and turned into intelligence for actionable insight.
- B.Sc Data Science is a unique Program designed to impart the essential skills to cater to the fastest growing trends in the industry.
- The program will provide students with an opportunity to get exposed to the broad range of subjects leading to a high-level of data science skills.
- The curriculum is a blend of core and advanced specialised courses in statistics and predictive analytic using R, Python, Machine Learning , Data Visualisation with a strong foundation of Mathematics, Communication skills and entrepreneurship.
- Choice based electives from other Programs diversify the domain of knowledge in an interdisciplinary manner.
- Project intern-ship give the much needed hands on learning experience in the final semester, where they can analyse and draw actionable inferences from a raw data using the analytics based on statistical and mathematical understanding.
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- Expertise faculty with great experience in industry, academics, research, development and innovation
- Curriculum is based on the latest trends in the ever changing and evolving fields of Data Science
- Excellent placement cell for further opportunities
- State-of-the-art Computer Laboratory and Infrastructure
- Large numbers of Extra-curricular and Co-curricular engagement opportunities
“To be announced shortly”
| Course Structure | ||
|---|---|---|
| Semester – I | ||
| Sr.No. | Course Name | Credits |
| 1 | Python Programming | 4 |
| 2 | Databases and My SQL | 4 |
| 3 | Statistics-I | 4 |
| 4 | Elementary Calculus | 2 |
| 5 | Matrix Theory | 2 |
| 6 | HR/ Management | 3 |
| 7 | Communication-I | 3 |
| Semester – II | ||
| Sr.No. | Course Name | Credits |
| 1 | Data Structures | 4 |
| 2 | Statistics-II | 4 |
| 3 | Object Oriented Programming | 4 |
| 4 | Ordinary differential equations | 2 |
| 5 | Advanced Calculus | 2 |
| 6 | Statistical modelling | 2 |
| 7 | Humanities | 2 |
| 8 | Communication-II | 2 |
| Semester – III | ||
| Sr.No. | Course Name | Credits |
| 1 | R programming for Data Science | 4 |
| 2 | Regression analysis and Bayesian statistics | 5 |
| 3 | Data Visualization and Tableau | 5 |
| 4 | Discrete Mathematics and Linear Algebra | 5 |
| 5 | General Elective-I | 3 |
| 6 | Operation Research | 2 |
| 7 | ID course | 2 |
| Semester – IV | ||
| Sr.No. | Course Name | Credits |
| 1 | Data mining and warehousing ( Using R / Python) | 3 |
| 2 | Data analytic using Hadoop | 3 |
| 3 | Machine Learning | 5 |
| 4 | Cloud computing | 3 |
| 5 | General Elective-II | 3 |
| 6 | Optimization Techniques | 2 |
| 7 | Environment Science | 2 |
| 8 | Khoj | 3 |
| 9 | ID course | 2 |
| Semester – V | ||
| Sr.No. | Course Name | Credits |
| 1 | Internet of things for Data Analytic | 4 |
| 2 | Deep Learning | 4 |
| 3 | Soft Computing | 4 |
| 4 | Big Data | 4 |
| 5 | Numerical methods and simulation | 5 |
| 6 | Entrepreneurship | 3 |
| 7 | Research methodology | 3 |
| Semester – VI | ||
| Sr.No. | Course Name | Credits |
| 1 | Intern-ship | 17 |
| Semester | List of Electives |
| Sem-III | Biostatistics |
| Image processing | |
| Econometrics | |
| Text analysis | |
| Sem-IV | Artificial intelligence |
| Bioinformatics | |
| Cyber Security |