- Industry-relevant curriculum, delivered online or on-site lectures.
- The curriculum covers areas that prepare you for most lucrative careers in the space of Data Science, Data Engineering and Advanced Analytics. It helps learners master critical skills such as Mathematical modeling, Machine learning, Artificial Intelligence, Product development and scripting languages.
- Benefit from Case Studies, Simulations, Virtual Labs & Remote Labs that allow learners to apply concepts to simulated and real-world situations. Tools & Technologies covered include Apache Spark, Apache Storm for Big Data Systems/ Real-time Processing, Tableau for data visualization, Tensorflow for Deep Learning and various packages within Python for data processing, machine learning and data visualization.
- Live weekly online lectures, supplementary online contact sessions comprising of tutorials, doubt-clearing sessions, and industry talks will also be conducted periodically.
- Semesters 1st, 2nd, and 3rd cover four courses each. The 4th semester covers Dissertation/ Project Work.
- The Dissertation (Project Work) in the final semester enables students to apply concepts and techniques learnt during the programme.
- The programme uses a Continuous Evaluation System that assesses the learners over convenient and regular intervals. Such a system provides timely and frequent feedback and helps busy working professionals stay on course with the programme.
*Classes are conducted by a pool of faculty members comprising of academicians from BITS Pilani, and guest faculty who are experienced, industry professionals.
Participants need to take at least 12 courses towards coursework, and complete one Project/ Dissertation. The coursework requirement for the programme would consist of a set of core courses and electives. Core courses are compulsory for all participants, while electives can be chosen based on individual learning preferences.
The programme offers a degree of customisation to address the specific L&D needs of your organisations.
COURSE WISE SYLLABUS
1. Introduction to Data Science
Learn about the need for data science, with emphasis on data; Visualization and ethical aspects involved in data science and engineering processes; Various applications of data science.
2. Mathematical Foundations for Data Science
Learn about concepts in linear algebra and use it as a platform to model physical problems; Analytical and numerical solutions of linear equations; Mathematical structures, concepts and notations used in discrete mathematics.
3. Introduction to Statistical Methods
Learn about basic and some advanced concepts of probability and statistics; Concepts of statistics in solving problems arising in data science
4. Data Structures and Algorithms Design
Learn about applications of basic and advanced data structures & algorithms; How to determine the space and time complexities of various algorithms; Identifying and choosing the relevant data structures and algorithms for a given problem and justifying the time and space complexities involved.
5. Computer Organization & Software Systems
Learn about computer organization, architecture aspects and operating system concepts; Advanced systems and techniques used for data processing.
6. Systems for Data Analytics
Learn about fundamentals of data engineering; Basics of systems and techniques for data processing - comprising of a relevant database, cloud computing and distributed computing concepts.
7. Data Mining
Learn about data pre-processing & cleaning; Association rule mining, classification, clustering techniques.
8. Machine Learning
Learn about basic concepts and techniques of Machine Learning; Using recent machine learning software for solving practical problems; How to do independent study and research in the field of Machine Learning.
9. Data Visualization
Learn about design principles, human perception and effective storytelling with data; Modern visualization tools and techniques.
10. Ethics for Data Science
Learn about the need for data ethics; Challenges of data privacy; Data policies for maintaining the privacy of data; Data Privacy.
11. Graphs - Algorithms and Mining
Learn about concepts of graph theory so as to understand; How graph theory concepts are used in different contexts, ranging from puzzles and games to social sciences/ engineering/ computer science; Model problems in real-world using graphs; Applying mining algorithms to get information from graph structures.
12. Optimization Methods for Analytics
Learn about applying linear programming techniques to complex business problems across various functional areas including finance, economics, operations, marketing and decision making; Implementing optimization techniques to business and industrial problems.
13. Big Data Systems
Learn about concepts related to big data and its processing; Applying the concepts of storage, retrieval, interfaces and processing frameworks to a given problem and design solutions for the same by choosing the relevant ones.
14. Advanced Topics in Data Processing
Learn about advanced strategies for data processing; The relationship between the scale of data and the systems used to process it; The importance of scalability of algorithms as the size of datasets increase.
15. Information Retrieval
Learn about structure and organization of various components of an IR system; Information representation models, term scoring mechanisms, etc. in the complete search system; Architecture of search engines, crawlers and the web search; Cross-lingual retrieval and multimedia information retrieval.
16. Deep Learning
Learn about deep learning techniques, constructing deep network structures specific to applications and tuning for parameters.
17. Natural Language Processing
Learn about natural language processing techniques such as Parts-of-Speech tagging, syntactic and semantic modelling of languages.
18. Artificial Intelligence
Learn about classic AI Techniques
19. Real-time analytics
Learn about Processing frameworks for real-time analytics, and Analytics techniques for real-time streaming data
20. Probabilistic Graphical Models
Learn about representation, learning and reasoning techniques for graphical models.
21. Data Warehousing
Learn about concepts needed to design, develop, and maintain a data warehouse; End user access tools like OLAP and reporting.
Choice of Electives is made available to enrolled students at the beginning of each semester. Students' choice will be taken as one of the factors while deciding on the Electives offered. However, Electives finally offered will be at the discretion of the Institute.
- Data Mining
- Mathematical Foundations for Data Science
- Data Structures and Algorithms Design
- Computer Organization and Systems Software
- Introduction to Statistical Methods
- Introduction to Data Science
- Machine Learning
- Elective 1
- Elective 2
- Elective 3
- Elective 4
- Elective 5
- Deep Learning
- Natural Language Processing
- Real time-Analytics
- Data Visualisation
- Graphs-Algorithms & Mining
- Optimization Methods for Analytics
- Big Data Systems
- Advanced Topics in Data Processing
- Information Retrieval
- Probabilistic Graphical Models
- Data Warehousing
- Systems for Data Analytics
- Ethics for Data Science
LECTURES DELIVERED ONLINE AND ONSITE
Lectures are delivered by BITS Pilani faculty members through live via online classes, or at the organisation's premises, and are designed to offer similar levels of interactivity as regular classrooms at the BITS Pilani campus.
In addition to live weekly online lectures, supplementary live online sessions will be organised periodically comprising of tutorials, doubt-clearing interactions, and industry talks (18-20 hours per semester).
The learners' performance is assessed continuously throughout the semester using various tools such as quiz, assignments, mid-semester and comprehensive exams. The assessment results are shared with the learners to improve their performance. Each course will entail a minimum of 1 Assignment/ Quiz, a Mid-semester exam and a final Comprehensive exam. Your semester calendar will indicate the dates of the Mid-semester and Comprehensive exam.
Typically, a Mid-semester or Comprehensive examination for a course is for 2-3 hours duration. The examinations are typically conducted over a weekend, i.e. Saturday and Sunday.
Minimum eligibility to apply - Employed professionals holding B.E. / B.Tech. / MCA / M.Sc. or equivalent with at least 60% aggregate marks or more in their qualifying exam, and minimum one-year relevant work experience are eligible to apply.
Applicants should possess basic programming knowledge and adequate background in Mathematics.