- Integrated M.Tech. Software Systems is a BITS Pilani Work Integrated Learning Programme (WILP). BITS Pilani Work Integrated Learning Programmes are UGC approved.
- The programme is of eight semesters, with online classes conducted mostly on weekends or after business hours. You can pursue the programme without any career break.
- The programme offers a set of core courses and elective courses, allowing students to specialize in Data Analytics, IoT, Embedded Systems, Security, Networks and Cloud.
- The programme makes use of Languages, Platforms, and Libraries. These include NS2, Net-SNMP, WireShark, 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.
- Semesters 1-7 cover four courses each. The 8th semester covers Dissertation/ Project Work
- The Dissertation (Project Work) in the final semester enables students to apply concepts and techniques learnt during the programme in real-world situationssemester enables students to apply concepts and techniques learned during the programme in real-world situations.
- The programme uses a Continuous Evaluation System that assesses the learners over convenient and regular intervals. Such as 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
- Lab exercises consist of programming exercises, experiments using simulation tools, analysis and design of systems, etc. Some of the tools used in assignments are Code::Blocks, SQLite, Star UML, NS2, WireShark and Keil
- Participants who successfully complete the programme will become members of an elite & global community of BITS Pilani Alumni
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.
The programme offers a degree of customisation to address the specific L&D needs of your organisations.
- Discrete Structures for Computer Science
- Linear Algebra & Optimization
- Computer Programming
- Digital Electronics & Microprocessors
- Object Oriented Programming and Design
- Systems Programming
- Computer Organization & Architecture
- Data Structures & Algorithms
- Probability & Statistics
- Database Systems & Applications
- Operating Systems
- Elective 1
- Compiler Design
- Software Engineering
- Computer Networks
- Software Testing
- Distributed Computing
- Elective 2
- Elective 3
- Elective 4
- Software Architectures
- Elective 5
- Elective 6
- Elective 7
- Elective 8
- Elective 9
- Elective 10
- Elective 11
- Artificial Intelligence
- Computer Organization and Software Systems
- Distributed Data Systems
- Software Engineering and Management
- Usability Engineering
- Object-oriented Analysis & Design
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.
Learners can access engaging learning material which includes recorded lectures from BITS Pilani faculty members, course handouts and recorded lab content where applicable.
Continuous Assessment includes graded Assignments/ Quizzes, Mid-semester exam, and Comprehensive Exam.
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
- 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
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
Final 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.
Employed professionals holding B.Sc./BCA Degree or its equivalent in relevant disciplines with minimum 60% aggregate marks and adequate background in Mathematics, with a minimum one-year work experience in the relevant domains.