Program
M.Tech Data Science
APPLY NOWData science is an interdisciplinary field that uses techniques and theories from mathematics, statistics, computer science, domain knowledge, and information science. This program combines knowledge of Machine Learning, Data Analytics, and Business Intelligence with skills in modern tools and techniques in order to prepare students for the ever-changing demands of modern industry. Students gain hands-on experience building end-to-end solutions to computational problems as part of the curriculum, which provides a deep understanding of algorithms and their complexity. In addition, the specialization will provide exposure to fundamental research problems inspired by newly developed data science techniques.
![b.tech](https://presidencyuniversity.in/uploads/course/64f9a08950c3f1694081161.webp)
-
Duration
2 Years Full-Time Program
Eligibility
Eligibility Norms For Admission To M.Tech Programs The candidate seeking admission for M. TECH program should have bachelor's degree or equivalent in Engineering [B.E/B. Tech] in the relevant field from any Indian university or Foreign University recognized by UGC/AIU, having obtained at least 50% of marks on the aggregate. 45% aggregate marks required in case of candidates belonging to SC/ST and other Reserved Categories. The candidate should also submit score details of any State/Central Entrance Examination/Presidency University admission qualifying examination for ADMISSION TOME/M. TECH program. Kindly note that taking one of the above tests is a pre-requisite for admission to the M. TECH Program.
Career
![img](https://presidencyuniversity.in/assets/images/aime-career.webp)
- Data scientists
- Data engineer
- Analytics manager
Details For Courses
Sl. No. | Course Name |
---|---|
1 | Advanced Engineering Mathematics |
2 | English for Employability |
3 | Seminar - I |
4 | Seminar - II |
5 | Disseration/ Internship - I |
6 | Disseration/ Internship - II |
Sl. No. | Course Name |
---|---|
1 | Artificial Intelligence |
2 | Knowledge Engineering and Expert Systems |
3 | Machine Learning Algorithms |
4 | Deep Learning |
5 | Natural Language Processing Techniques |
Sl. No. | Course Name |
---|---|
1 | Data Analytics and Visualization |
2 | Robotic Process Automation |
3 | Machine Vision |
4 | AI in Cloud Computing |
5 | Soft Computing Techniques |
6 | Ontology Engineering for the Semantic Web |
7 | Big Data Analyics Tools And Techniques |
8 | Time Series Analysis and Forecasting |
9 | Intelligent Information Retrival |
10 | AI in Internet of Things |
11 | Essentials for Machine Learning |
12 | Application of Probability theory in Computer Science |
13 | NoSQL Databases |
13 | Recommender Systems with Machine Learning and AI |