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M.Tech.

DEGREE PROGRAMMESData Science and Engineering

Prepare for career in Data Science with the most comprehensive masters 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.

The programme features face-to-face classes conducted over weekends in Bengaluru, Delhi NCR & Hyderabad by BITS Pilani faculty.

 

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  • Programme Highlights
  • Programme Curriculum
  • Learning Methodology
  • Eligibility Criteria
  • Fee Structure
  • How to Apply
Programme Highlights
  1. Learn without a career break by attending face-to-face weekend lectures conducted by BITS Pilani faculty in Bengaluru, Delhi NCR & Hyderabad

  2. 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.

  3. 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.

  4. Semesters 1st, 2nd, and 3rd cover four courses each. The 4th semester covers Dissertation/ Project Work.

  5. The Dissertation (Project Work) in the final semester enables students to apply concepts and techniques learnt during the programme.

  6. 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.

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

  • Elective 1

  • Elective 2

Third Semester

  • Elective 3

  • Elective 4

  • Elective 5

  • Elective 6

Fourth Semester

  • Dissertation

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

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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

.

Learning Methodology

CONTINUOUS ASSESSMENT

Continuous Assessment includes graded Assignments/ Quizzes, Mid-semester exam, and Comprehensive Exam

EXPERIENTIAL LEARNING

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:

  • Tools & Technologies: 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.

  • Case Studies and Assignments: Carefully chosen real-world cases & assignments are both discussed and used as problem-solving exercises during the programme

  •  

Face-to-Face Classroom Sessions

Learners get an opportunity to interact with expert faculty in-person during weekend classroom sessions conducted at Bengaluru, Delhi NCR & Hyderabad

Eligibility Criteria

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.

Fee Structure
  • The following fees schedule is applicable for candidates seeking new admission during the academic year 2018-19

    Application Fees (one time) : INR 1,500

    Admission Fees (one time) : INR 16,500

    Semester Fees (per semester) : INR 52,500

  • 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. 69,000/-. You may choose to make the payment using Netbanking/ Debit Card/ Credit Card through the Online Application Centre. Fee payment by easy-EMIs is now available. Click here to learn more

  • 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 go to the BITS Pilani Online Application Center. Create your login at the Online Application Center by entering your unique Email ID and create a password of your choice. Once your login has been created, you can anytime access the Online Application Center using your email ID and password.

  • Once you have logged in, you will see a screen showing 4 essential steps to be completed to apply for the programme of your choice.

  • 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.

  • Take the next step by clicking on Step 2 - 'Download Application PDF Copy’. This will download a pdf copy of the application form on your computer.

  • Now, click on Step 3 - 'Pay Application Fee’ to pay INR 1,500/- using Netbanking/ Debit Card/ Credit Card

  • Take a printout of the downloaded Application Form and note down the Application Form Number that appear on the top-right corner of the first page. This Application Form Number should be referred in all future correspondence with BITS Pilani.

  • In the printout of the downloaded Application Form, you will notice on page no. 3 a section called the Employer Consent Form. Complete the Employer Consent Form. This form needs to be signed and stamped by your organisation’s HR or any other authorised signatory of the company.

  • Further on page no. 4 of the printed Application Form is a section called the Mentor Consent Form. The Mentor Consent Form needs to be signed by the Mentor. Click here to know who could be a Mentor.

  • Further on page no. 5 of the downloaded Application Form, is a Checklist of Enclosures/ Attachments.

  • Make photocopies of the documents mentioned in this Checklist

  • Applicants are required to self-attest all academic mark sheets and certificates

  • Finally, click on Step 4 - 'Upload & Submit All Required Documents’. This will allow you to upload one-by-one the printed Application Form, Mentor Consent Form, Employer Consent Form, and all mandatory supporting documents 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.

  • If your application form is considered complete, the Admission Cell will e-mail you a link for an Online Learning Readiness Evaluation. This is a one-hour objective-type exercise which will ascertain minimum mathematical and programming acumen needed to pursue the programme. You will be given typically 48-hours to complete this online evaluation. A sample model paper will also be provided to help you understand the format of Online Learning Readiness Evaluation.

  • After the Online Learning Readiness Evaluation is completed, the Admission Cell will intimate selected candidates by email within one week. The selection status can also be checked by logging in to the Online Application Centre.