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M.Tech. Programmes

DEGREE PROGRAMMES M.Tech. Data Science and Engineering

Prepare for career in Data Science with the most comprehensive Master’s degree programme in Data Science & Engineering without taking a break from your career.

M.Tech. Data Science and Engineering is a four-semester programme designed for working professionals that helps learners build mathematical and engineering skills required to advance their career as a Data Scientist or Data Engineer.

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  • Programme Highlights
  • UGC Approval
  • Programme Curriculum
  • Learning Methodology
  • Eligibility Criteria
  • Fee Structure
  • How to Apply
Programme Highlights
  • The programme is offered by BITS Pilani, a top-ranked institution, recently announced as an Institution of Eminence by MHRD, Govt. of India

  • The programme is of four semesters, and can be pursued without a career break

  • Classes will be conducted by BITS Pilani faculty over weekends through live online sessions.

  • 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.

  • 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.

  • The programme emphasizes on experiential learning through Simulations, Online Labs, Case Studies, Group Discussions, Assignments and Project work

  • Dissertation/ Project Work in the final semester enables learners to apply concepts and techniques learnt during the programme

UGC Approval

BITS Pilani is an Institution of Eminence under UGC (Institution of Eminence Deemed to be Universities) Regulations, 2017. The Work Integrated Learning Programmes (WILP) of BITS Pilani constitutes a unique set of educational offerings for working professionals. WILP are an extension of programmes offered at the BITS Pilani Campuses and are comparable to our regular programmes both in terms of unit/credit requirements as well as academic rigour. In addition, it capitalises and further builds on practical experience of students through high degree of integration, which results not only in upgradation of knowledge, but also in up skilling, and productivity increase. The programme may lead to award of degree, diploma, and certificate in science, technology/engineering, management, and humanities and social sciences. On the recommendation of the Empowered Expert Committee, UGC in its 548th Meeting held on 09.09.20 has approved the continued offering of BITS Pilani’s Work Integrated Learning programmes.

Programme Curriculum

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.

First Semester

  • Data Mining

  • Mathematical Foundations for Data Science

  • Data Structures and Algorithms Design

  • Computer Organization and Systems Software

Second Semester

  • Introduction to Statistical Methods

  • Introduction to Data Science

  • Machine Learning

  • Elective 1

Third Semester

  • Elective 2

  • Elective 3

  • Elective 4

  • Elective 5

Fourth Semester

  • Dissertation

Electives

  • Deep Learning

  • Natural Language Processing

  • Artificial-Intelligence

  • 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

Course Wise Syllabus

1. Introduction to Data Science

  • Learn about the need for data science, with emphasis on data; Visualization and ethics 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 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 story telling 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 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

Electives finally offered will be at the discretion of the BITS Pilani, and will be decided in consultation with HCL. Offered electives will be made available to enrolled students at the beginning of each semester.

Learning Methodology

CLASSROOM SESSIONS

  • Classroom sessions in this programme will be conducted through live online sessions which can be accessed by the learners from any location using a computer and a high-speed internet connection.

  • Classes will be conducted by BITS Pilani faculty over weekends. A typical weekend classroom session per subject is of 1.5-2 hours duration. Since students typically pursue 4 courses in a semester, they will be expected to attend approximately 4 classroom sessions over a weekend.

  • These classroom sessions will be typically scheduled over 16 weekends per semester.

  • The schedule of the classroom sessions, will be announced at the beginning of each semester.

PROJECT WORK
During the final semester participants carryout a semester-long intensive project work applying the various concepts learnt throughout the program guided by the organisation mentor and supervisor. Participants are provided access to virtual labs where applicable, and faculty expertise to support the project work.

EXPERIENTIAL LEARNING & LABS

The programme emphasises on Experiential Learning that allows learners to apply concepts learnt in classroom in simulated and real work situations. This is achieved through Simulations, Online Labs, Case Studies, Group Discussions, and Assignments, etc.

Some or all of the following would be utilised across the programme:

Apache Spark, Apache Storm for Big Data Systems/ Real time Processing; Tableau for data visualisation; Tensorflow for Deep Learning; Various Packages within Python for data processing, machine learning, data visualization etc.

Examinations & CONTINUOUS ASSESSMENT

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 clearly 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.

Eligibility Criteria

Eligibility Criteria

To apply, candidates must be 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 two years relevant work experience within HCL are eligible to apply.

Applicants should possess basic programming knowledge and adequate background in Mathematics.

The above are only the minimum criteria to apply. The final decision to offer admission to an applicant rests with BITS Pilani which will be made based on an overall review of your application information.

Fee Structure
  • The following fees schedule is applicable for candidates seeking new admission during the academic year 2020-21

    Application Fees (one time) : INR 1,500

    Admission Fees (one time) : INR 16,500

    Semester Fees (per semester) : INR 55,000

  • The one-time Application Fee is to be paid at the time of submitting the Application Form through the Online Application Centre.

  • Admission Fee (one-time) and Semester Fee (for the First Semester) are to be paid together once admission is offered to the candidate. Thus, a candidate who has been offered admission will have to pay Rs. 71,500/-. You may choose to make the payment using Netbanking/ Debit Card/ Credit Card through the Online Application Centre.

  • Semester Fee for subsequent semesters will only be payable later, i.e. at the beginning of those respective semesters.

  • Any candidate who desires to discontinue from the programme after confirmation of admission & registration for the courses specified in the admit offer letter will forfeit the total amount of fees paid.

  • All the above fees are non-refundable.

How to Apply
  • Click here to visit the Online Application Center. Create your login at the Online Application Center by entering your official HCL Email ID only and create a password of your choice. Once your login has been created, you can anytime access the Online Application Center using your official email ID and password.

  • Begin by clicking on Step 1 - ‘Fill/ Edit and Submit Application Form’. This will enable you to select the programme of your choice. After you have chosen your programme, you will be asked to fill your details in an online form. You must fill all details and press ‘Submit’ button given at the bottom of the form.

  • Now, click on 'Pay Application Fee’ to pay INR 1,500/- using Netbanking/ Debit Card/ Credit Card

  • Finally, click on 'Upload & Submit All Required Documents’. This will allow you to upload one-by-one all the mandatory supporting documents such academic certificates and transcripts, photograph, etc. and complete the application process. Acceptable file formats for uploading these documents are .DOC, .DOCX, .PDF, .ZIP and .JPEG

  • Upon receipt of your Application Form and all other enclosures, the Admissions Cell will scrutinise them for completeness, accuracy and eligibility.

  • Admission Cell will intimate selected candidates by email within two weeks of submission of application with all supporting documents. The selection status can also be checked by logging in to the Online Application Centre.