Computer Science Course by Mannu Kumar Jha

The Computer Science Master Program by Mannu Kumar Jha is a comprehensive online course designed to build strong foundations in programming, data structures, databases, operating systems, networking, web development, data science, and big data technologies.

This course is ideal for school students, college learners, engineering aspirants, and professionals who want complete computer science knowledge from fundamentals to advanced data engineering concepts. The curriculum combines theory, coding practice, mini projects, and real-world applications to make learners industry-ready.

What Student Will Learn

Module 1: Programming Fundamentals

• Algorithms & Flowcharts

• C Programming (Basics to Intermediate)

  • Data Types, Operators

  • Conditional & Looping Statements

  • Functions

  • Arrays & Strings

  • Pointers (Basics)

• Python Programming

  • Data Types & Operators

  • Conditional & Looping

  • Functions & Modules

  • Lists, Tuples, Sets, Dictionaries

  • File Handling

  • Exception Handling

Module 2: Data Structures & Algorithms (DSA)

• Arrays & Linked Lists

• Stack & Queue

• Recursion

• Searching (Linear, Binary)

• Sorting (Bubble, Selection, Insertion)

• Introduction to Time & Space Complexity

• Basics of Problem Solving

Module 3: Database Management System (DBMS)

• Introduction to DBMS

• ER Model & Relational Model

• SQL

  • SELECT, INSERT, UPDATE, DELETE

  • WHERE, GROUP BY, ORDER BY

  • JOINS

• Normalization

• Transactions (ACID Properties)

Module 4: Web Development Basics

• HTML

• CSS

• Introduction to JavaScript

• Forms & Basic Validation

• Creating Simple Projects

Module 5: Operating Systems

• Introduction to OS

• Processes & Threads

• Memory Management

• File Systems

• Deadlock (Basics)

Module 6: Computer Networks

• Types of Networks

• OSI & TCP/IP Model

• IP Addressing

• Network Devices

• Basics of Internet & Protocols

Module 7: Data Science

• Introduction to Data Science

• Python for Data Science

• NumPy & Pandas

• Data Cleaning & Preprocessing

• Data Visualization (Matplotlib, Seaborn Basics)

• Statistics for Data Science

  • Mean, Median, Mode

  • Probability Basics

• Introduction to Machine Learning

  • Supervised vs Unsupervised Learning

  • Linear Regression

  • Classification Basics

• Mini Data Science Projects

Module 8: Data Engineering

• Introduction to Data Engineering

• Data Lifecycle & Data Pipelines

• ETL (Extract, Transform, Load)

• Working with Large Datasets

• SQL for Data Engineering

• Introduction to Big Data

• Basics of Hadoop & Spark

• Cloud Basics (AWS/GCP Overview)

• Data Warehousing Concepts

Teaching Methodology

• Live online interactive sessions
• Hands-on coding practice
• Real-world mini projects
• Interview-oriented DSA practice
• GitHub portfolio guidance
• Assignments and assessments
• Doubt-clearing sessions

Who Should Join

• School and college students
• B.Tech / BCA / MCA aspirants
• Beginners in programming
• Students preparing for tech interviews
• Learners interested in Data Science & Big Data
• Professionals upgrading to AI & ML technologies

Learning Outcomes

• Strong foundation in C and Python programming
• Solid understanding of Data Structures and Algorithms
• Practical knowledge of SQL and DBMS
• Exposure to Operating Systems and Networking fundamentals
• Hands-on experience in Data Science tools
• Introduction to Big Data and Cloud concepts
• Improved logical and analytical problem-solving skills