New age digital technologies are transforming the world and spawning a massive demand of specialists in areas like data analytics, IoT, Embedded systems, Security, Networks and cloud etc. While specialists in data analytics are powering organisations with transformative capacities to win in their businesses, the IOT and Embedded systems specialists are revolutionising our lives and society. Massive growth in connectivity means greater need for security specialists and huge connected infrastructure needs specialists in network and cloud.
M.Tech Software Systems is a unique programme that enables working professionals to specialise in many new age technology areas and be ready to transition into high demand careers. The programme enables the learners to specialize in some of the fastest growing domains like Data Analytics, Internet of Things, Embedded Systems, Security, Networks and Cloud. A comprehensive curriculum, extensive emphasis on experiential learning using remote labs and cloud labs and a flexible education methodology that enables working professionals to acquire a prestigious post graduate engineering degree while pursuing their careers, the M.Tech Software Systems is just the right programme for career growth in the software industry.
M.Tech Software Systems is a BITS Pilani Work Integrated Learning Programme (WILP). BITS Pilani Work Integrated Learning Programmes are UGC approved.
Admission Enquiry
Please fill the below fields for fee details, programme information, and application instructions
Profile of M.Tech. Software Systems programme participants
Some of the major Organisations where Participants work
Student Speak
.png)
Vineet Kumar
.png)
Aishwarya
.png)
Ketan Atri
.png)
Student Speak | Sreekanth Laxman | M.Tech. Software Systems for Working Professionals
.png)
Chaithanya Rai
.png)
Sourav Kumar
.png)
Surya
.png)
Venkat Maguluri
.png)
Venkata Raman
- Programme Highlights
- Programme Curriculum
- Eligibility
- Fee Structure
- How to Apply
- Mode of Examination
- Mode of Learning
Programme Highlights
- The M.Tech. Software Engineering programme is a Work Integrated Learning Programme (WILP) spanning four semesters. BITS Pilani's Work Integrated Learning Programmes are approved by the University Grants Commission (UGC).
- Attend live-lectures from anywhere over an online technology-enabled platform. These live lectures would be conducted by faculty mostly on weekends or after business hours enabling working professionals to pursue the programme along with their jobs.
- The programme offers a set of core courses and elective courses, allowing students to specialize in Data Analytics, Internet of Things, Embedded Systems, Security, Networks and Cloud.
- The programme makes use of Languages, Platforms, and Libraries. These include NS2, Net-SNMP, WireSha, R, Python, Prolog, Lisp, RStudio, Weka, Microsoft Power BI, TensorFlow, Anaconda Navigator, Python Ecosystem – NumPy, SciPi, Pandas, scikit-learn, MatplotLib; Searborn, Keras, NLTK, pgmpy etc., Keil, CCS Studio, Tossim, Cheddar, Jenkins, GitHub, SonarQube, Selenium, Tomcat, Maven, Java, Eclipse, Code::Blocks, Android Studio, Jupyter Notebooks, Spyder, Multisim, CPU-OS Simulator, SQLite, MATLAB, , Gantt Project, Open Project and XAMPP.
- The Dissertation (Project Work) in the final semester enables students to apply concepts and techniques learned 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.
- The education delivery methodology is a blend of classroom and experiential learning. Experiential learning consists of lab exercises, assignments, case studies and work-integrated activities.
- Participants who successfully complete the programme will become members of an elite & global community of BITS Pilani Alumni
- Option to submit fee using easy-EMI with 0% interest and 0 down payment.
Programme Curriculum
The programme offers specialisations in high-demand areas such as Data Analytics, Internet of Things, Embedded Systems, Security, Networks and Cloud.
Electives can be chosen either from the General pool of electives or from across other pools of electives for Specialisations. Specialisations are optional. To earn a Specialization, a participant must select and successfully complete at least 5 courses from that Specialisation pool.
First Semester
- Data Structures & Algorithms Design
- Database Design & Applications
- Distributed Computing
- Elective 1
Second Semester
- Software Architectures
- Elective 2
- Elective 3
- Elective 4
Third Semester
- Elective 5
- Elective 6
- Elective 7
- Elective 8
Fourth Semester
- Dissertation
Electives
- Artificial Intelligence
- Computer Organization and Software Systems
- Distributed Data Systems
- Software Engineering and Management
- Usability Engineering
- Object-oriented Analysis & Design
Specialisation in Data Analytics
Participants that earn a specialization in Data Analytics will learn how to apply principles behind modern data analytics techniques; Apply statistical and machine learning methods to real data; Evaluate their performance and e?ectively communicate the results; and Build expertise in advanced Artificial Intelligence topics such as Deep Learning and Natural Language Processing
Pool of Electives
- Advanced Statistical Techniques for Analytics
- Applied Machine Learning
- Metaheuristics for Optimization
- Data Mining
- Data Warehousing
- Deep Learning
- Information Retrieval
- Mathematical Foundations For Data Science (Mandatory Course for Specialization)
- Natural Language Processing
Specialisation in Embedded Systems
Participants will gain expertise in key areas of Application (Domain) Specific System Design such as scope of a Processor (Embedded processors, Desktop systems, Servers, and Supercomputers), the target application (general-purpose versus domain-specific), the characteristics of the design objectives (Speed, Power consumption, Cost, Reliability, Availability, and Reconfigurability), and the measurement and analysis of resulting designs.
Pool of Electives
- Embedded Middleware Design
- Embedded System Design (Mandatory Course for Specialization)
- Fault Tolerant Embedded System
- Hardware Software Co-Design
- Networked Embedded Applications
- Parallel Embedded Architectures
- Real Time Scheduling
- Real Time Systems
- Software for Embedded Systems
Specialisation in Networks and Cloud
Participants will build expertise in how to design, and manage software and hardware that control digital networks; Conceptualize and solve Engineering problems with reference to wireless and mobile networks effectively and arrive at the feasible optimal solution, individually and in teams; Master formal techniques for network analysis, design and operate data centers; Network Security aspects Storage Area Networks, Virtualizations, and Cloud Computing Concepts which has great scope and opportunities in Industry; Apply advanced software engineering techniques (e.g., software-defined networks, containerization, etc.) to compute, improve and master the development of distributed networks.
Pool of Electives
- Advanced Computer Networks
- Cloud Computing (Mandatory Course for Specialization)
- Computer Networks (Mandatory Course for Specialization)
- Data Storage Technologies and Networks
- Design and Operation of Data Centres
- Edge Computing
- Mobile Networks
- Network Programming
- Network Security
- Software Defined Networks
- Wireless and Mobile Communication
- Middleware Technologies
Specialisation in Security
Participants will build expertise in implementation of core software engineering principles and the best cyber security practices in terms of policies, models and mechanisms; gain knowledge about securing computer networks and systems; learn to examine secure software design and development practices in cyber security; understand the prevalent network and distributed system attacks; incorporate approaches for incident response and security risk management; understand the key concepts in domain specific security.
Pool of Electives
- Cyber Security (Mandatory Course for Specialization)
- Cryptography
- Network Security
- Ethical Hacking
- Identity and Access Management Technologies
- Cyber Crimes, Forensics and Incident Handling
- Cloud, IoT and Enterprise Security
- Secure Software Engineering
- Blockchain Technologies & Systems
- AI and ML Techniques in Cyber Security
Specialisation in Internet of Things
Participants will build expertise in the building blocks of IoT technology and explore the vast spectrum of IoT applications; Assess, select and customize technologies for IoT applications; Connect the cyber world with the physical world of humans, automobiles and factories; Integrate geographically distributed devices with diverse capabilities; Design and implement IoT applications that manage big data
Pool of Electives
- Embedded Systems Design (Mandatory Course for Specialization)
- Cyber Physical Systems (Mandatory Course for Specialization)
- Networked Embedded Applications
- Cross Platform Application Development
- Cloud Computing
- Data Management for IoT
- Stream Processing and Analytics
- Embedded Network Security
For more information on programme curriculum download the programme brochure.
Choice of Electives is made available to enrolled students at the beginning of each semester. A limited selection of Electives will be offered at the discretion of the Institute.
Eligibility
The programme is designed for highly driven and ambitious engineers working for software services or product companies and wish to advance their careers in hyper-growth areas of Software Engineering, Embedded systems, Data Analytics, Telecommunications or Networking.
If you are an IT professional in a technical role such as Software Developer, Test Engineer, Lead Engineer, Architect, or techno-managerial role such as Product Manager and Project Manager, you should consider applying to the programme.
Fee Structure
For fee details, programme information and application instructions, please click here to download the Programme Brochure.
How to Apply
For application instructions, programme information and fee structure, please click here to download the Programme Brochure
Mode of Examination
For mode of examinations, please click here to download the programme brochure.
Mode of Learning
Mode of Learning
The Mode of Learning is based on a powerful educational approach called Work Integrated Learning. For detailed description of work integrated learning Click here
The benefits of the Work Integrated Learning Mode are as follows:
1) It enables Working Professionals to pursue the programme without any career break and along with their jobs.
2) The programme curriculum is designed for high relevance to sectors, industries and organisations the students are presently employed in.
3) Learning experience design also endeavours to integrate support and value addition by the Industry Mentors and employer organizations.
4) Enables working professionals to attend live-lectures from anywhere over an online technology-enabled platform. For the benefit of working professionals these live lectures are conducted by faculty mostly on weekends or after business hours.
5) Leverages the latest educational technologies to provide easy access to asynchronous learning materials, Learner support services and peer to peer collaboration etc.
6) Great emphasis on experiential learning by providing access to state of the art remote labs, virtual labs and cloud labs and simulations as applicable to individual courses.
EXPERIENTIAL LEARNING
The programme emphasises on Experiential Learning that allows learners to apply concepts learnt in the classroom in simulated, and real work situations. This is achieved through:
Simulation Tools, Platforms & Environments: Some or all of the following would be utilised across the programme.
- Cloud based virtual lab which supports the following programming languages/tools/simulators:
- Networks: NS2, Net-SNMP and WireShark
- Data Analytics:
- Languages: R, Python, Prolog and Lisp
- Platforms: RStudio, Weka, Microsoft Power BI, TensorFlow and Anaconda Navigator
- Libraries: Python Ecosystem – NumPy, SciPi, Pandas, scikit-learn, MatplotLib; Searborn, Keras, NLTK, pgmpy etc.
- Embedded and IOT: Keil, CCS Studio, Tossim and Cheddar
- Devops: Jenkins, GitHub, SonarQube, Selenium, Tomcat and Maven
- Programming Environments: Java, Eclipse, Code::Blocks, Android Studio, Jupyter Notebooks and Spyder
- Others: Multisim, CPU-OS Simulator, SQLite, MATLAB, , Gantt Project, Open Project and XAMPP
- Remote Lab facility caters to the needs of resource intensive requirements of Big Data Analytics applications with the following platforms:
- Apache Hadoop
- Apache Storm
- Apache Spark
- Apache Kafka
- MongoDB
- CockroachDB
- MPI
- Remote Lab facility caters to the needs of Embedded Systems and IoT. It supports the following:
- Hardware / Software tools: MultiCore STM32 microcontroller based development boards.
- Simulation tools: Tossim, Cheddar and Keil
CONTINUOUS ASSESSMENT
Continuous Assessment includes graded Assignments/ Quizzes, Mid-semester exam, and Comprehensive Exam.
CASE STUDIES AND ASSIGNMENTS
Carefully chosen real-world cases & assignments are both discussed and used as problem-solving exercises during the programme.
DISSERTATION/ PROJECT WORK
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.