The Python Programming Laboratory is designed to provide students with hands-on experience in programming using Python, one of the most widely used high-level programming languages today. This lab complements theoretical knowledge by focusing on problem-solving, algorithm development, and implementation using Python’s syntax and libraries.

Objectives:

• To familiarize students with Python programming syntax, constructs, and core libraries.

• To enhance algorithmic thinking and problem-solving skills.

• To implement real-world applications such as file handling, data structures, and object-oriented programming.

• To develop skills in using Python for data analysis, automation, and simple graphical user interfaces.

This laboratory course provides students with in-depth knowledge and hands-on experience in Object-Oriented Programming (OOP) using Java. It focuses on building a solid foundation in Java syntax and applying core OOP principles such as abstraction, encapsulation, inheritance, and polymorphism. Through practical exercises and mini-projects, students will gain confidence in writing modular, maintainable, and scalable Java applications.

Objectives:

• To introduce the principles of object-oriented programming using Java.

• To implement real-world problems using Java classes and objects.

• To understand Java’s standard libraries, exception handling, and basic GUI programming.

The Operating Systems & Linux Tools and Utilities Lab aims to provide students with a hands-on understanding of operating system concepts and the practical use of Linux tools. Through shell programming, system call experimentation, and utility usage, students will learn how the operating system manages hardware, processes, memory, and files. The lab also emphasizes proficiency with Linux/Unix-based environments, focusing on commands, scripting, and essential development tools.

Objectives:

• To understand the internal workings of operating systems through experiments.

• To develop shell scripts for automation and task scheduling.

• To work with process control, memory management, and file handling using system calls.

• To gain proficiency in using Linux tools like awk, sed, grep, find, diff, and vi.

• To build foundational skills for systems programming and administration.

The Database Management Systems Laboratory offers practical exposure to the concepts of database design, development, and management. Students will gain hands-on experience in creating and manipulating databases using SQL and PL/SQL, and learn how to apply normalization, indexing, and transactions. This lab is essential for understanding how modern data-driven applications are built and maintained, with a focus on relational database models.

Objectives:

• To understand the principles of relational database design and implementation.

• To apply Structured Query Language (SQL) for data definition, manipulation, and control.

• To write PL/SQL programs with procedures, functions, and triggers.

• To explore database integrity, constraints, and transaction control.

• To implement normalization techniques and analyze query performance.

The Machine Learning Laboratory equips students with practical experience in applying machine learning algorithms to real-world problems. The course covers a broad range of supervised, unsupervised, and reinforcement learning techniques, using Python and popular ML libraries. Students will learn how to preprocess data, train models, evaluate performance, and apply ML techniques to various domains such as classification, regression, clustering, and recommendation systems.

Objectives:

• To understand the core concepts of machine learning and its types.

• To implement various ML algorithms using Python.

• To apply data preprocessing, feature selection, and model evaluation techniques.

• To build and deploy machine learning models on structured datasets.

• To gain experience with tools such as scikit-learn, pandas, matplotlib, and tensorflow (optional).

The Data Mining Using Python Laboratory provides hands-on training in discovering patterns, relationships, and insights from large datasets using Python. The lab emphasizes the practical implementation of data preprocessing, pattern discovery, association rule mining, classification, clustering, and data visualization. Students will explore real-world datasets using Python libraries and understand the data mining lifecycle from preparation to interpretation.

Objectives:

• To understand data mining techniques and apply them using Python.

• To perform data preprocessing including cleaning, integration, and transformation.

• To implement data mining algorithms such as association rule mining, classification, and clustering.

• To analyze and visualize patterns extracted from data.

• To evaluate model performance and interpret mining results for decision-making.

The Web Programming Laboratory provides students with practical experience in designing, developing, and deploying dynamic and interactive web applications. The lab covers both front-end and back-end web technologies, focusing on client-server communication, web standards (HTML, CSS, JavaScript), server-side scripting (using PHP or Node.js), and database connectivity. Students will build full-stack web applications and gain exposure to current web development frameworks and best practices.

Objectives:

• To understand the fundamentals of web development and internet protocols.

• To design responsive and accessible web pages using HTML, CSS, and JavaScript.

• To implement dynamic web applications using server-side programming.

• To connect web applications to relational databases using SQL.

• To build and deploy complete full-stack web applications.