B.E. Computer Science and Engineering
- Data Science (CSE-DS)

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Laboratories

The Data Science program at MVJCE trains the students to acquire the skills necessary to perform intelligent data analysis which is a key component in numerous real-world applications. The entire Data Science program is credit-based, and every subject is taught not only through lectures but also through seminars, guest lectures, and workshops. Eminent personalities from both Academia as well as Industry are invited to address the students on various topics related to technological advances in the Data Science field. This ensures that our students become assets for the organization they are employed in.

The course content is updated every year, as per the industry requirements and it measures up to world-class standards. State-of-the-art Labs enhance the students’ learning experience and help them gain practical knowledge.

Operating Systems Laboratory

Operating Systems Laboratory

Students learn about fundamental concepts such as processes, threads, memory management, file systems, and input/output operations. It can include writing code for process scheduling algorithms, memory allocation techniques, file system operations, etc. Through lab activities, students gain skills in analyzing system behavior, identifying performance bottlenecks, and debugging issues related to process management, memory leaks, or file system corruption

Data Structures Laboratory

The lab usually begins with an introduction to fundamental data structures such as arrays, linked lists, stacks, queues, trees, and graphs. Students learn about their properties, operations, and applications. It involve analyzing the time and space complexity of algorithms associated with different data structures.

Advanced Java Laboratory

Students explore advanced topics in Java programming language such as generics, collections framework. Students learn how to build dynamic web applications using Java technologies such as Servlets, JavaServer Pages (JSP), and Enterprise JavaBeans (EJB). database connectivity and interaction using Java Database Connectivity (JDBC) API. Students learn how to connect to relational databases, execute SQL queries, and perform database operations from Java applications.

Analysis and Design of Algorithms Laboratory

Students implement algorithms sorting, searching, graph traversal, dynamic programming, divide and conquer etc. Students learn how to calculate Big O notation and understand the efficiency of algorithms in terms of worst-case, average-case, and best-case scenarios. Focuses on specific algorithm design paradigms such as greedy algorithms, dynamic programming, divide and conquer, or backtracking.

Database Management Laboratory

Students learn about the principles of database design, including entity-relationship modelling, normalization, and schema design. It involves writing and executing SQL queries to retrieve, insert, update, and delete data from databases. Students learn about SQL syntax, data manipulation language (DML), and data definition language (DDL) commands. They learn techniques for securing databases against unauthorized access, data breaches, and other security threats with various authentication, authorization, encryption, and auditing mechanisms. This could involve developing database-backed web applications, e-commerce platforms, or information management systems.

Computer Network Laboratory

The set of algorithms based on the network security implemented in java language. The objective of this lab is to understand the working of Network software.

Full stack development Laboratory

Full stack development is the end-to-end development of applications. It includes both the front end and back end of an application. The front end is usually accessed by a client, and the back end forms the core of the application where all the business logic is applied.

Machine Learning Lab

This lab is a centralized hub for development teams to seamlessly build, deploy, and operate machine learning solutions at scale. It is designed to cover the end-to-end machine learning lifecycle from data processing and experimentation to model training and deployment.

Scalable computing Laboratory

The scalable computing lab focus on advancing the state of the art in scalable computing systems, architectures, and applications. The lab mainly focuses on research and development that pioneers innovative solutions for scalable computing, enabling advancements in engineering through the creation of efficient, high-performance computational infrastructures and algorithms. Create high-performance computing (HPC) solutions tailored to engineering applications, improving the speed and accuracy of computations. Integrate machine learning and artificial intelligence with scalable computing to automate and enhance engineering processes and designs.

Statistical Machine Learning for Data Science Lab Laboratory

The vision for a Statistical Machine Learning for Data Science Lab is to be a pioneering centre that advances the understanding and application of statistical machine learning techniques to solve complex data science problems. This lab aims to bridge the gap between theoretical research and practical applications, driving innovation and fostering interdisciplinary collaboration. This lab mainly focuses in statistical machine learning, advancing the field of data science through the development of robust, scalable, and interpretable algorithms and methodologies that address real-world challenges across diverse domains.

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