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B-TECH in Computer Science And Engineering Data Science Ds at Keshav Memorial Institute of Technology
Hyderabad, Telangana
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About the Specialization
What is Computer Science and Engineering - Data Science (DS) at Keshav Memorial Institute of Technology Hyderabad?
This B.Tech CSE Data Science program at KMIT, Hyderabad, specializes in advanced skills for data analysis, machine learning, and big data technologies. It meets India''''s growing demand for data scientists, offering a curriculum that integrates theory with practical applications, preparing students for data-driven roles across diverse industries.
Who Should Apply?
This program is ideal for graduates passionate about mathematics, statistics, and programming, aspiring for data-driven careers. It targets analytical minds eager to solve complex problems. Freshers can pursue roles in AI/ML engineering, data analysis, or business intelligence within India''''s thriving tech sector, contributing to critical digital transformation initiatives.
Why Choose This Course?
Graduates can expect promising career paths in data science, machine learning, and analytics. Entry-level salaries in India typically range from INR 4-8 lakhs annually, with experienced professionals earning significantly more. The program aligns with key industry certifications, enhancing growth trajectories in Indian companies spearheading innovation and data-centric solutions.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Focus intensely on C/Python programming basics, data structures, and algorithms. Dedicate daily time to coding practice and problem-solving to build a strong foundation.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, NPTEL courses on programming
Career Connection
A strong programming foundation is crucial for all IT roles, especially for cracking technical coding rounds in campus placements and interviews.
Build a Strong Mathematical & Statistical Base- (Semester 1-2)
Pay close attention to Linear Algebra, Calculus, Probability, and Statistics. These subjects are the bedrock of Data Science. Practice problems regularly to solidify understanding.
Tools & Resources
Khan Academy, NPTEL, University Textbooks, Coursera/edX online courses
Career Connection
Essential for understanding machine learning algorithms, statistical modeling, and data interpretation, which are core data science competencies.
Engage in Early Problem Solving & Collaboration- (Semester 1-2)
Participate in coding competitions and form study groups with peers. Work on small academic projects to apply learned concepts in practical scenarios.
Tools & Resources
CodeChef, Google Kick Start, GitHub for collaborative projects, Peer study circles
Career Connection
Develops teamwork, problem-solving abilities, and competitive programming skills, highly valued by top tech companies and startups in India.
Intermediate Stage
Dive Deep into Data Science Core- (Semester 3-5)
Focus on Data Structures, Object-Oriented Programming, DBMS, Operating Systems, Data Visualization, and introductory Machine Learning. Actively implement concepts in labs.
Tools & Resources
Python libraries (Pandas, NumPy, Matplotlib, Scikit-learn), SQL, Tableau/Power BI tutorials, Jupyter Notebooks
Career Connection
Directly builds the technical expertise required for Data Analyst, Junior Data Scientist, and entry-level Machine Learning Engineer roles.
Seek Industry Exposure through Internships/Mini-Projects- (Semester 4-5)
Actively search for summer internships or undertake mini-projects leveraging your data science skills. Connect with industry mentors to gain insights.
Tools & Resources
LinkedIn, Internshala, College placement cell, Industry networking events, GitHub portfolio
Career Connection
Gains practical experience, builds a professional network, enhances your resume, and often leads to pre-placement offers from companies.
Participate in Data Science Competitions- (Semester 4-5)
Join platforms like Kaggle or Analytics Vidhya to work on real-world datasets and challenge your skills in a competitive environment.
Tools & Resources
Kaggle, Analytics Vidhya, DataCamp, Dedicated Discord/Telegram groups
Career Connection
Develops advanced analytical thinking, problem-solving, and model building skills, highly attractive to recruiters in the data science domain.
Advanced Stage
Specialize and Build Advanced Portfolio- (Semester 6-8)
Choose electives strategically (e.g., Deep Learning, Big Data, Cloud Computing, NLP) and work on a significant major project. Contribute to open-source data science projects.
Tools & Resources
TensorFlow, PyTorch, Apache Spark, AWS/Azure, Personal website/blog to showcase projects
Career Connection
Showcases specialized knowledge and practical application, crucial for senior data scientist or research-oriented roles in the industry.
Intensive Placement Preparation- (Semester 7-8)
Practice mock interviews (technical and HR), aptitude tests, and coding challenges rigorously. Refine your resume and LinkedIn profile for recruiters.
Tools & Resources
InterviewBit, PrepInsta, LinkedIn Learning, College placement training modules, Alumni network mentorship
Career Connection
Maximizes chances of securing placements in top-tier companies with competitive salaries, establishing a strong career launchpad.
Focus on Ethical AI and Continuous Learning- (Semester 7-8)
Understand the ethical implications of AI/Data Science. Stay updated with new technologies and research papers through online courses and conferences.
Tools & Resources
Research papers (arXiv), AI ethics guidelines, deeplearning.ai, Coursera specializations, Industry webinars
Career Connection
Prepares for leadership roles, ensures responsible innovation, and supports long-term career growth in a rapidly evolving and ethically sensitive field.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 or equivalent examination with Physics, Chemistry, and Mathematics, qualifying through entrance exams like TS EAMCET/JEE (Mains).
Duration: 8 semesters / 4 years
Credits: 160 Credits
Assessment: Internal: Varies by course type (e.g., Theory: 30%, Labs: 40%), External: Varies by course type (e.g., Theory: 70%, Labs: 60%)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22EN101HS | English Language & Communication Skills | Core | 3 | Introduction to Communication, Listening Skills, Speaking Skills, Reading Skills, Writing Skills |
| 22MA101BS | Linear Algebra & Calculus | Core | 4 | Matrices and Determinants, Eigenvalues and Eigenvectors, Differential Calculus, Integral Calculus, Vector Calculus |
| 22CS101PC | Programming for Problem Solving | Core | 3 | Programming Fundamentals, Control Structures, Functions, Arrays and Pointers, Structures and Files |
| 22EN102HS | English Language & Communication Skills Lab | Lab | 1 | Listening for Academic and Public Speaking, Articulation and Pronunciation, Interviews and Group Discussions, Presentations and Public Speaking, Professional Communication |
| 22CS102PC | Programming for Problem Solving Lab | Lab | 1 | C Programming Basics, Conditional and Looping Statements, Arrays and Strings, Functions and Pointers, File Operations |
| 22ME101ES | Engineering Workshop | Lab | 1 | Carpentry, Fitting, Tin-Smithy, Foundry, Welding, House Wiring |
| 22CS103ES | Elements of Computer Science and Engineering | Core | 3 | Computer Hardware, Operating Systems, Networking Basics, Databases, Software Engineering, AI/ML Fundamentals |
| 22CH101BS | Engineering Chemistry | Core | 3 | Water Technology, Electrochemistry and Corrosion, Fuels and Combustion, Polymers and Composites, Environmental Chemistry |
| 22CH102BS | Engineering Chemistry Lab | Lab | 1 | Water Quality Analysis, Acid-Base Titrations, Redox Titrations, Conductometric Titration, Potentiometric Titration |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MA201BS | Advanced Calculus & Numerical Methods | Core | 4 | Ordinary Differential Equations, Laplace Transforms, Vector Differentiation, Fourier Series, Numerical Methods for Equations |
| 22PH201BS | Applied Physics | Core | 3 | Wave Optics, Lasers and Fiber Optics, Quantum Mechanics, Semiconductor Physics, Magnetic Materials |
| 22EC201ES | Electronic Devices & Circuits | Core | 3 | Diode Characteristics, Rectifiers and Filters, Transistor Biasing, FET Characteristics, Amplifiers and Oscillators |
| 22EE201ES | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Transformers, DC Machines, AC Machines |
| 22EC202ES | Electronic Devices & Circuits Lab | Lab | 1 | Diode and Zener Diode Characteristics, Half-wave and Full-wave Rectifiers, Transistor CB, CE Characteristics, FET Characteristics, RC Phase Shift Oscillator |
| 22PH202BS | Applied Physics Lab | Lab | 1 | Diffraction Grating, Laser Wavelength Determination, Fiber Optics Numerical Aperture, Photoelectric Effect, Energy Gap of a Semiconductor |
| 22EE202ES | Basic Electrical Engineering Lab | Lab | 1 | Verification of KVL and KCL, Superposition Theorem, Thevenin''''s and Norton''''s Theorem, Frequency Response of RLC Circuit, DC Machine Characteristics |
| 22ME201ES | Computer Aided Engineering Graphics | Lab | 2 | Orthographic Projections, Isometric Projections, Projections of Points and Lines, Projections of Planes and Solids, Introduction to CAD Software |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MA301BS | Probability and Statistics | Core | 3 | Probability Theory, Random Variables and Distributions, Joint Probability Distributions, Sampling Distributions, Statistical Inference |
| 22CS301PC | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Heaps, Graphs, Sorting and Searching Algorithms |
| 22CS302PC | Object Oriented Programming | Core | 3 | OOP Concepts (Encapsulation, Inheritance, Polymorphism), Classes and Objects, Constructors and Destructors, Exception Handling, Templates and Collections |
| 22CS303PC | Digital Logic Design | Core | 3 | Boolean Algebra and Logic Gates, Combinational Circuits, Sequential Circuits, Registers and Counters, Memory and Programmable Logic |
| 22CS304DS | Introduction to Data Science | Core - Data Science | 3 | Data Science Lifecycle, Data Collection and Preprocessing, Exploratory Data Analysis, Data Visualization Techniques, Introduction to Machine Learning |
| 22CS305PC | Data Structures Lab | Lab | 1 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Practice |
| 22CS306PC | Object Oriented Programming Lab | Lab | 1 | Classes and Objects in Java/C++, Inheritance and Polymorphism, Abstract Classes and Interfaces, File I/O and Exception Handling, Collection Frameworks |
| 22CS307PC | Digital Logic Design Lab | Lab | 1 | Basic Logic Gates Implementation, Combinational Circuit Design, Flip-Flops and Latches, Registers and Counters, Memory Unit Design |
| 22CS308DS | Data Science Lab | Lab - Data Science | 1 | Python for Data Science (NumPy, Pandas), Data Loading and Cleaning, Basic Data Visualization (Matplotlib, Seaborn), Introduction to Scikit-learn, Simple Regression and Classification Models |
| 22CS309PC | Skill Oriented Course – I (Web Technologies) | Skill Oriented Course | 2 | HTML5 and CSS3, JavaScript Fundamentals, Responsive Web Design, Front-end Frameworks (e.g., Bootstrap), Web Development Tools |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CS401PC | Database Management Systems | Core | 3 | Introduction to DBMS, Relational Model and SQL, Database Design (ER, Normalization), Transaction Management, Concurrency Control and Recovery |
| 22CS402PC | Operating Systems | Core | 3 | OS Structure and Functions, Process Management and Scheduling, Memory Management, File Systems, Deadlocks and Concurrency |
| 22CS403PC | Computer Organization and Architecture | Core | 3 | Basic Computer Organization, CPU Design and Instruction Set, Memory Hierarchy, Input/Output Organization, Pipelining and Parallel Processing |
| 22CS404DS | Data Visualization | Core - Data Science | 3 | Principles of Data Visualization, Visualization Tools (e.g., Tableau, Power BI), Static and Interactive Visualizations, Storytelling with Data, Dashboard Design and Best Practices |
| 22CS405PC | Database Management Systems Lab | Lab | 1 | SQL DDL, DML, DCL Commands, Advanced SQL Queries (Joins, Subqueries), Views and Stored Procedures, Triggers and Cursors, Database Connectivity (e.g., JDBC) |
| 22CS406PC | Operating Systems Lab | Lab | 1 | Shell Scripting, Process Management, Inter-process Communication, CPU Scheduling Algorithms, Memory Management Techniques |
| 22CS407PC | Skill Oriented Course – II (App Development) | Skill Oriented Course | 2 | Mobile Application Development Basics, Android/iOS Platform Overview, UI/UX Design for Mobile, Data Storage and Retrieval, App Deployment |
| 22EN401HS | Environmental Science | Mandatory Non-Credit Course | 0 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources, Sustainable Development, Environmental Protection Acts |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CS501PC | Design & Analysis of Algorithms | Core | 3 | Algorithm Analysis Techniques, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms and NP-Completeness |
| 22CS502PC | Computer Networks | Core | 3 | Network Topologies and Models (OSI/TCP-IP), Data Link Layer, Network Layer (IP, Routing), Transport Layer (TCP, UDP), Application Layer Protocols (HTTP, DNS) |
| 22CS503DS | Machine Learning | Core - Data Science | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation and Selection, Ensemble Methods, Feature Engineering |
| 22CS504DS | Big Data Analytics | Core - Data Science | 3 | Big Data Ecosystem (Hadoop, Spark), HDFS and MapReduce, NoSQL Databases, Data Ingestion and Processing, Stream Processing |
| 22CS511PE | Professional Elective – I (Information Retrieval Systems) | Professional Elective | 3 | Information Retrieval Models, Text Processing and Indexing, Query Processing, Evaluation of IR Systems, Web Search and Link Analysis |
| 22CS505DS | Machine Learning Lab | Lab - Data Science | 1 | Implementing Regression Models, Classification Algorithms, Clustering Techniques, Model Evaluation Metrics, Cross-Validation and Hyperparameter Tuning |
| 22CS506DS | Big Data Analytics Lab | Lab - Data Science | 1 | Hadoop HDFS Operations, MapReduce Programming, Apache Spark for Data Processing, NoSQL Database (e.g., MongoDB) Operations, Data Ingestion Tools |
| 22CS507PC | Computer Networks Lab | Lab | 1 | Socket Programming (TCP/UDP), Network Configuration Commands, Packet Sniffing and Analysis, Routing Protocols Implementation, Client-Server Application Development |
| 22CS508DS | Skill Oriented Course – III (Data Visualization tools) | Skill Oriented Course | 2 | Tableau/Power BI Fundamentals, Creating Interactive Dashboards, Data Storytelling, Advanced Chart Types, Data Cleaning for Visualization |
| 22CS509DS | Mini Project – I (DS) | Project | 2 | Problem Identification and Scoping, Literature Review, System Design and Architecture, Implementation and Testing, Report Writing and Presentation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CS601PC | Compiler Design | Core | 3 | Phases of a Compiler, Lexical Analysis, Syntax Analysis, Semantic Analysis, Code Generation and Optimization |
| 22CS602PC | Software Engineering | Core | 3 | Software Development Life Cycle Models, Requirements Engineering, Software Design Principles, Software Testing Techniques, Software Project Management |
| 22CS603DS | Deep Learning | Core - Data Science | 3 | Neural Network Fundamentals, Activation Functions and Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Frameworks (TensorFlow, PyTorch) |
| 22CS604DS | Cloud Computing | Core - Data Science | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, Virtualization and Containerization, Cloud Security, Major Cloud Providers (AWS, Azure, GCP) |
| 22CS612PE | Professional Elective – II (Natural Language Processing) | Professional Elective | 3 | Text Preprocessing and Tokenization, N-grams and Word Embeddings, Part-of-Speech Tagging, Named Entity Recognition, Sentiment Analysis and Machine Translation |
| 22CS605DS | Deep Learning Lab | Lab - Data Science | 1 | Implementing Neural Networks with Keras/PyTorch, Image Classification using CNNs, Sequence Prediction with RNNs/LSTMs, Transfer Learning Applications, Model Optimization Techniques |
| 22CS606DS | Cloud Computing Lab | Lab - Data Science | 1 | Deploying Instances on Cloud Platforms, Configuring Storage Services (S3, Blob), Serverless Computing (AWS Lambda), Container Orchestration (Docker, Kubernetes basics), Cloud Monitoring and Security |
| 22CS607PC | Compiler Design Lab | Lab | 1 | Lexical Analyzer using LEX, Parser using YACC, Symbol Table Management, Intermediate Code Generation, Code Optimization Techniques |
| 22CS608HS | Professional Ethics & Human Values | Mandatory Non-Credit Course | 0 | Professional Ethics Theories, Human Values and Morality, Engineering Ethics, Corporate Social Responsibility, Environmental Ethics |
| 22CS609MC | Mini Project – II (DS) | Project | 2 | Advanced Problem Solving, Project Planning and Execution, Teamwork and Collaboration, Technical Documentation, Project Presentation and Demonstration |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CS713PE | Professional Elective – III (Reinforcement Learning) | Professional Elective | 3 | Markov Decision Processes, Dynamic Programming (Value/Policy Iteration), Monte Carlo Methods, Temporal Difference Learning (Q-Learning, SARSA), Deep Reinforcement Learning |
| 22CS714PE | Professional Elective – IV (Graph Neural Networks) | Professional Elective | 3 | Graph Theory Fundamentals, Graph Embeddings, Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Applications of GNNs (e.g., node classification) |
| 22CS701OE | Open Elective – I (Generic) | Open Elective | 3 | Fundamentals of Management, Principles of Marketing, Financial Management Basics, Entrepreneurship Concepts, Human Resource Management |
| 22CS701PC | Major Project – Part A | Project | 4 | Problem Definition and Scope, Detailed System Design, Feasibility Study, Initial Implementation and Prototype, Technical Report and Presentation |
| 22CS702DS | Internship/Industrial Training | Internship | 2 | Real-world Industry Experience, Application of Theoretical Knowledge, Professional Skill Development, Industry Best Practices, Internship Report and Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CS815PE | Professional Elective – V (Generative AI) | Professional Elective | 3 | Introduction to Generative Models, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, Large Language Models (LLMs) and Prompt Engineering |
| 22CS802OE | Open Elective – II (Generic) | Open Elective | 3 | Business Analytics, Supply Chain Management, Digital Marketing, Intellectual Property Rights, Cyber Security Basics |
| 22CS801PC | Major Project – Part B | Project | 8 | Full System Implementation, Extensive Testing and Debugging, Performance Evaluation, Comprehensive Project Report, Final Project Defense |
| 22CS803MC | Mandatory Non Credit Course - Entrepreneurship & Start-up Essentials | Mandatory Non-Credit Course | 0 | Startup Ecosystem in India, Idea Generation and Validation, Business Model Canvas, Funding Sources and Investor Pitches, Legal and Regulatory Aspects for Startups |
