- Learn without a career break with live online lectures conducted mostly on weekends or after office hours by BITS Pilani faculty members and experienced industry professionals
- 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.
- Contact-less and safe Online exams facility.
- The Dissertation 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.
- Participants who successfully complete the programme will become members of an elite & global community of BITS Pilani Alumni.
- Fee payment by easy-EMIs with 0% interest.
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.
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.
- 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
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.
For detailed programme curriculum, download the brochure.
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
- Dissertation: The fourth semester offers an opportunity for learners to apply their knowledge gained during the programme to a real-world like complex project. The learner is expected to demonstrate understanding of vital principles learnt across semesters and their ability to successfully apply these concepts.
ATTEND ONLINE LECTURES OVER WEEKENDS
Lectures are conducted live via online classes. These lectures can be attended via the internet using a computer from any location. These online classrooms offer similar levels of interactivity as regular classrooms at the BITS Pilani campus.
Classes for students admitted during the period Jan-Feb 2021 will begin in Mar 2021. The class schedule is announced within 1 week of completion of the admission process.
The online lectures are conducted usually over weekends for a total of 7-8 hours per week. If you miss a lecture, you can also access the recorded lecture on the internet.
Lectures are conducted on Sat/Sun as per Indian Standard Time.
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.
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).
Online Exams Option
Examinations Mode Options for M.Tech. Data Science And Engineering applicable for students admitted in Batch starting in March 2021
Every semester has Mid-semester Examinations and Comprehensive Examinations. These examinations are mostly scheduled over weekends. In addition to the mid-semester and comprehensive examinations, student will need to also participate in an online quiz or do an assignment and submit it online as per the course plan.
Two Options on Mode of Examinations:
Institution offers a choice between taking the examination online or taking them at a designated examination center. The student will choose one of the option depending on his or her own preference and circumstances. Both options are explained below:
Online Examinations: Students choosing this option can take the examinations online from any location e.g. office or home. To take an online examination, student must possess a laptop or desktop with a web cam, a smart phone and good internet connectivity. As per the examination schedule, the student is expected to login to the institution’s online examination platform and take the examinations in compliance with institution’s defined guidelines and rules announced before the examinations. For full details about hardware, software and connectivity requirements to take online examination, click here.
Examinations at Designated Examination Centers: Students choosing this option will need to appear in person for taking the examinations at institution’s designated examination centers. These designated examination centers are at the following locations: Bangalore, Chennai, Hyderabad, Pune, Mumbai, Goa, Delhi NCR, Pilani and Kolkata. In addition to these location, Institution also has a designated examination center at Dubai. Please note that offering of examinations at designated examination centers is subject to institution’s assessment of the safety conditions as per prevailing pandemic conditions and also subject to a required minimum number of students preferring this option. The institution may choose to not offer this option, if as per its own assessment the safety situation due to pandemic conditions is not conducive to conduct examinations at designated examination centers or if as per its assessment, adequate no of students have not preferred for this option. In circumstances as explained, Institute will then conduct the examinations only in the online mode.
Important: The option of taking Online Exams will remain available throughout the duration of the programme. However, in case a student chooses to take a break in the programme, the options on the mode of examination available will be as prevailing at the time the student resumes the programme.
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.
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 60,000
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. 76,500/-. You may choose to make the payment using Netbanking/ Debit Card/ Credit Card through the Online Application Centre.Fee payment by easy-EMIs with 0% interest. 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.
For the examination centre at Dubai, in addition to the semester fees, for each semester there will be an examination centre fees of 1000 UAE Dirhams or equivalent per semester out of which 500 UAE Dirhams is to be paid at the time of appearing in Mid-semester examinations at Dubai Centre for that semester and the remaining 500 UAE Dirhams is to be paid at the time of appearing in comprehensive examinations at Dubai centre for that semester.
All the above fees are non-refundable.
How to Apply
- Click here to visit the BITS Pilani Online Application Center. Create your login at the 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 the 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 o2n 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 displayed 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.
Important: In view of work-from-home policies mandated by many organisations, a few candidates may not be able to get the physical forms signed by their HR/ other authorised organisational representative. Such candidates may instead request an email approval to be sent to their official email ID by the HR using the format available through this link.
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.
Important: In view of work-from-home policies mandated by many organisations, a few candidates may not be able to get the physical forms signed by their Mentor. Such candidates may instead request an email approval to be sent to their official email ID by the Mentor using the format available through this link.
Who is a Mentor:
Candidates applying to Work Integrated Learning Programmes must choose a Mentor, who will monitor the academic progress of the candidate, and act as an advisor & coach for successful completion of the programme. Candidates should ideally choose the immediate supervisor or another senior person from the same organisation. In case a suitable mentor is not available in the same organisation, a candidate could approach a senior person in another organisation who has the required qualifications. Wherever the proposed Mentor is not from the same employing organization as that of the candidate, a supporting document giving justification for the same should be provided by the candidate’s employer.
Candidates applying to B.Tech. programmes should choose a Mentor who is an employed professional with B.E./ B.S./ B.Tech./ M.Sc./ A.M.I.E./ Integrated First Degree of BITS or equivalent
Candidates applying to M.Tech., M.Sc., MBA, M.Phil programme should choose a Mentor who is an employedprofessional with:
B.E. / M.Sc. / M.B.A. / M.C.A. / M.B.B.S. etc. and with a minimum of five years of relevant work experience
M.E./ M.S./ M.Tech./ M.Phil./ M.D./ Higher Degree of BITS or equivalent
- 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.
- 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.
Note: It is also possible that some candidates may receive a link to take an Online Learning Readiness Evaluation from the Admission Cell, after filling up the Online Application Form. 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.
Yes, UGC has approved the continued offering of M.Tech. Data Science & Engineering as a part of WILP on the recommendations of Empowered Expert Committee. Click here to learn more.
This programme is designed for working professionals. At the time of submitting the application, candidates must be employed in another organization. Professionals who are owners of a registered business are also eligible to apply. For detailed information, including academic background, work experience, etc. refer to the programme eligibility criteria.
Lectures are conducted live via online classes. These lectures can be attended via the internet using a computer from any location. These online classrooms offer similar levels of interactivity as regular classrooms at the BITS Pilani campus. The online lectures are conducted usually over weekends for a total of 7-8 hours per week. If you miss a lecture, you can also access the recorded lecture on the internet.
Each semester has a Mid-semester Exam and a Comprehensive Exam, which are conducted over weekends. Participants will need to appear in-person to take these exams at exams centers in the following locations:
The programme provides high degree of interactivity between participants and the faculty members and programme instructors. Q&A sessions during the live online lectures allow participants to pose questions to the faculty members, and seek guidance through voice and chat. Further interaction with faculty members and peers is enabled through the e-learning portal, by using discussion forums and message boards.
The programme features high usage of experiential learn components such as Simulations, Virtual Labs, and Remote Labs, in order to mimic the on-campus experience.
Participants will be given access to portals, that will allow them to access both cloud-based labs, as well as Campus-based physical labs. Using leading industry-recognised Software tools, Programming languages, and Simulation software, participants will be able to perform experiments and run simulations to advance their knowledge
Candidates applying to the programmes must choose a Mentor, who will monitor the academic progress of the candidate, and act as an advisor & coach for successful completion of the programme. Candidates should ideally choose the immediate supervisor or another senior person from the same organisation. In case a suitable mentor is not available in the same organisation, a candidate could approach a senior person in another organisation who has the required qualifications. For detailed information, please refer to the programme brochure.
The programme is designed for working professionals, and participants are not required to travel to a BITS campus. However, certain programme offer Campus Immersion Modules such as workshops or seminars, which are highly recommended but not mandatory.