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B-TECH in Cse Data Science at Koneru Lakshmaiah Education Foundation (Deemed to be University)
Guntur, Andhra Pradesh
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About the Specialization
What is CSE - Data Science at Koneru Lakshmaiah Education Foundation (Deemed to be University) Guntur?
This B.Tech CSE Data Science program at K L University focuses on equipping students with advanced skills in data analysis, machine learning, and artificial intelligence. Recognizing India''''s rapidly expanding digital economy and data-driven industries, the curriculum emphasizes practical application and theoretical depth to produce highly competent data professionals. The program differentiates itself by integrating core computer science principles with specialized modules in Big Data, Deep Learning, and Ethical AI, preparing graduates for diverse roles in the analytics domain.
Who Should Apply?
This program is ideal for aspiring computer science engineers with a strong aptitude for mathematics and problem-solving, seeking entry into high-growth data roles. It also caters to graduates keen on understanding complex datasets to derive actionable insights for business or scientific applications. Working professionals in IT, keen to upskill into data science, and career changers transitioning into the analytics industry will find the comprehensive curriculum and practical focus beneficial. A foundational understanding of programming and basic statistics is advantageous.
Why Choose This Course?
Graduates of this program can expect to secure roles as Data Scientists, Machine Learning Engineers, Data Analysts, or Big Data Specialists within Indian and multinational corporations. Entry-level salaries typically range from INR 6-12 LPA, with experienced professionals earning upwards of INR 20-30 LPA, reflecting the high demand for these skills. The program''''s design aligns with industry-recognized certifications like AWS Certified Machine Learning and Azure Data Scientist Associate, fostering strong growth trajectories in India''''s booming tech sector.
Student Success Practices
Foundation Stage
Master Programming & Data Structures- (Semester 1-2)
Dedicate significant effort to mastering C, Java, and fundamental data structures. Consistently practice coding problems on platforms to solidify logical thinking and efficient algorithm design, which are crucial for subsequent data science modules.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, KLU''''s programming labs
Career Connection
Strong programming skills are the bedrock for any CSE role, especially in data science, ensuring eligibility and success in technical screening rounds for internships and placements.
Build a Strong Mathematical Base- (Semester 1-3)
Focus intently on Engineering Mathematics, Discrete Mathematics, Probability, and Statistics. Understand concepts thoroughly, as they form the theoretical backbone for Machine Learning and Artificial Intelligence algorithms.
Tools & Resources
Khan Academy, NPTEL courses, reference textbooks, peer study groups
Career Connection
A solid mathematical foundation is crucial for understanding ML algorithms, developing new models, and excelling in quantitative roles in data science and research.
Engage in Early Technical Project Building- (Semester 2-3)
Start working on small, personal coding projects beyond coursework. This could involve simple GUI applications, basic web tools, or automating small tasks, applying learned programming concepts in practical scenarios.
Tools & Resources
GitHub for version control, VS Code/Eclipse IDEs, online tutorials, KLU''''s coding mentorship programs
Career Connection
Early projects demonstrate initiative and practical application skills, making a resume stand out for early internships and showcasing problem-solving ability to potential employers.
Intermediate Stage
Develop a Data Science Portfolio with Python- (Semester 3-5)
Leverage Python for data manipulation, analysis, and visualization. Actively work on Kaggle datasets or real-world problems to create a portfolio of data science projects using libraries like Pandas, NumPy, Scikit-learn, and Matplotlib.
Tools & Resources
Kaggle, Google Colab, Jupyter Notebook, Scikit-learn, Seaborn, Tableau Public
Career Connection
A strong project portfolio is critical for showcasing practical data science skills and is often a prerequisite for interviews for data scientist and analyst roles in the Indian market.
Seek Internships and Industry Exposure- (Semester 4-6)
Proactively search for summer internships (paid or unpaid) in data science, analytics, or machine learning roles. Attend industry workshops, guest lectures, and hackathons organized by the department or external companies.
Tools & Resources
LinkedIn, Internshala, KLU Placement Cell, departmental industry connect events
Career Connection
Internships provide invaluable real-world experience, build industry networks, and often convert into pre-placement offers, significantly boosting employability within India''''s tech sector.
Specialize in Key ML/DL Areas- (Semester 5-6)
Identify a specific area within Machine Learning or Deep Learning (e.g., NLP, Computer Vision, Reinforcement Learning) that interests you most. Take advanced online courses and build complex projects in this chosen area to gain depth.
Tools & Resources
Coursera, edX, fast.ai, TensorFlow, PyTorch, specialized research papers
Career Connection
Specialization makes you a desirable candidate for niche roles and advanced research positions, demonstrating deep expertise in a high-demand sub-field of AI/DS in the Indian job market.
Advanced Stage
Focus on Real-World Capstone Projects- (Semester 7-8)
Dedicate significant effort to the major project, choosing a complex, real-world data science problem. Aim for novel solutions, publishable research, or a robust deployable system that showcases your accumulated skills.
Tools & Resources
Cloud platforms (AWS, Azure, GCP), Docker, MLOps tools, academic advisors, industry mentors
Career Connection
A high-quality capstone project is the strongest evidence of a candidate''''s ability to solve complex problems, crucial for senior data science roles and highly competitive positions in Indian companies.
Prepare for Placements with Mock Interviews- (Semester 7-8)
Actively participate in mock interview sessions, both technical and HR. Practice coding, algorithm design, data structures, and machine learning concepts. Prepare a strong resume and LinkedIn profile tailored to industry requirements.
Tools & Resources
KLU Placement Cell, InterviewBit, Glassdoor, personal network for mock interviews
Career Connection
Thorough preparation is key to converting interviews into job offers. This stage directly impacts the quality and number of placement opportunities secured in India''''s competitive job market.
Cultivate Professional Networking and Mentorship- (Semester 6-8)
Attend professional conferences, connect with industry experts on LinkedIn, and seek mentors who can guide career development. Participate in alumni networking events to expand your professional circle.
Tools & Resources
LinkedIn, industry meetups, professional bodies like IEEE/ACM (student chapters), KLU Alumni Association
Career Connection
Networking opens doors to opportunities not advertised, provides insights into industry trends, and establishes long-term career support, crucial for career advancement in the Indian tech landscape.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 examination with Physics, Mathematics, and Chemistry/Biotechnology/Biology/Technical Vocational subject/Computer Science/Information Technology/Informatics Practices/Agriculture/Engineering Graphics/Business Studies as compulsory subjects with at least 45% marks (40% for reserved categories) in the above subjects taken together.
Duration: 4 years / 8 semesters
Credits: 160 Credits
Assessment: Internal: 40% (for Theory courses), External: 60% (for Theory courses)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22HS1001 | Communicative English | Humanities | 2 | Basic English Grammar, Vocabulary Building, Public Speaking, Presentation Skills, Technical Report Writing |
| 22MA1001 | Engineering Mathematics-I | Mathematics | 4 | Differential Equations, Sequences and Series, Multivariable Calculus, Laplace Transforms, Vector Calculus |
| 22PH1001 | Engineering Physics | Engineering Science | 4 | Wave Optics, Quantum Mechanics, Solid State Physics, Lasers and Fiber Optics, Electromagnetism |
| 22CS1001 | Programming for Problem Solving using C | Core | 3 | C Language Fundamentals, Control Flow Statements, Functions, Arrays and Pointers, Structures and Unions |
| 22CS1002 | Data Structures | Core | 3 | Arrays and Lists, Stacks and Queues, Linked Lists, Trees and Graphs, Searching and Sorting Algorithms |
| 22HS1002 | Communicative English Lab | Humanities | 1 | Listening and Speaking Practice, Pronunciation Drills, Role Plays, Group Discussions, Public Presentations |
| 22PH1002 | Engineering Physics Lab | Engineering Science | 1 | Interference and Diffraction Experiments, Laser Characteristics, Photoelectric Effect, Semiconductor Device Characteristics, Magnetic Field Measurements |
| 22CS1003 | Programming for Problem Solving using C Lab | Lab | 1 | C Program Implementation, Debugging Techniques, Array and String Operations, Function Calls, File Input/Output |
| 22CS1004 | Data Structures Lab | Lab | 1 | Stack and Queue Implementation, Linked List Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Implementations |
| 22BT1001 | Biology for Engineers | Engineering Science | 2 | Cell Biology, Biomolecules, Genetics Basics, Microbiology, Bio-Sensors and their Applications |
| 22ES1001 | Computer Aided Engineering Graphics | Engineering Science | 2 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, Introduction to CAD Software |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22HS1003 | Professional Ethics & Human Values | Humanities | 2 | Human Values and Morality, Engineering Ethics, Professionalism in Engineering, Corporate Social Responsibility, Environmental Ethics |
| 22MA1002 | Engineering Mathematics-II | Mathematics | 4 | Matrices and Eigenvalues, Vector Spaces, Numerical Methods, Complex Analysis, Integral Transforms |
| 22CH1001 | Engineering Chemistry | Engineering Science | 4 | Electrochemistry, Corrosion and its Control, Water Treatment, Polymer Chemistry, Spectroscopic Techniques |
| 22CS1005 | Object Oriented Programming through Java | Core | 3 | OOP Concepts, Java Basics and Syntax, Inheritance and Polymorphism, Exception Handling, Collections Framework |
| 22CS1006 | Digital Logic Design | Core | 3 | Boolean Algebra, Logic Gates, Combinational Circuits, Sequential Circuits, Registers and Counters |
| 22CS1007 | Operating Systems | Core | 3 | OS Concepts, Process Management, Memory Management, File Systems, Input/Output Systems |
| 22CH1002 | Engineering Chemistry Lab | Engineering Science | 1 | Water Quality Analysis, Titrations and pH Metry, Conductometry Experiments, Viscosity Measurements, Instrumental Methods |
| 22CS1008 | Object Oriented Programming through Java Lab | Lab | 1 | Java Program Implementation, Class and Object Design, Inheritance and Interface Usage, Exception Handling Practices, GUI Programming Basics |
| 22CS1009 | Digital Logic Design Lab | Lab | 1 | Logic Gate Implementation, Combinational Circuit Design, Sequential Circuit Design, FPGA/CPLD Programming, Verilog HDL Simulation |
| 22CS1010 | Operating Systems Lab | Lab | 1 | Shell Scripting, Process Management Commands, Inter-process Communication, CPU Scheduling Algorithms, File System Operations |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MA2001 | Discrete Mathematics | Mathematics | 3 | Set Theory and Logic, Relations and Functions, Graph Theory, Combinatorics, Recurrence Relations |
| 22CS2101 | Computer Networks | Core | 3 | Network Topologies, OSI and TCP/IP Models, Data Link Layer, Network Layer Protocols, Transport and Application Layers |
| 22CS2102 | Database Management Systems | Core | 3 | ER Model, Relational Algebra, SQL Queries, Normalization, Transaction Management |
| 22CS2103 | Formal Languages and Automata Theory | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines |
| 22DS2001 | Foundations of Data Science | Core | 3 | Data Science Lifecycle, Data Collection and Cleaning, Exploratory Data Analysis, Data Visualization Techniques, Introduction to Machine Learning |
| 22DS2002 | Foundations of Data Science Lab | Lab | 1 | Python for Data Science, Data Manipulation with Pandas, Data Visualization with Matplotlib/Seaborn, Basic Statistical Analysis, Data Preprocessing Techniques |
| 22CS2104 | Database Management Systems Lab | Lab | 1 | Advanced SQL Queries, Database Design Implementation, Stored Procedures and Triggers, Database Connectivity (JDBC/ODBC), Data Manipulation Language |
| 22CS2105 | Computer Networks Lab | Lab | 1 | Network Command Line Tools, Socket Programming, Packet Tracing with Wireshark, Network Configuration Exercises, Client-Server Communication |
| 22HS2001 | Soft Skills | Humanities | 2 | Communication Skills, Teamwork and Collaboration, Leadership Qualities, Time Management, Interview Skills |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MA2002 | Probability and Statistics | Mathematics | 3 | Probability Theory, Random Variables and Distributions, Sampling Theory, Hypothesis Testing, Regression Analysis |
| 22CS2106 | Artificial Intelligence | Core | 3 | AI Agents and Search, Knowledge Representation, Machine Learning Principles, Natural Language Processing, Introduction to Robotics |
| 22CS2107 | Design and Analysis of Algorithms | Core | 3 | Algorithm Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms, NP-Completeness |
| 22DS2003 | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Regression and Classification, Clustering Algorithms, Model Evaluation and Ensemble Methods |
| 22DS2004 | Machine Learning Lab | Lab | 1 | Implementing ML Algorithms, Scikit-learn Usage, Model Training and Testing, Hyperparameter Tuning, Data Preprocessing for ML |
| 22CS2108 | Artificial Intelligence Lab | Lab | 1 | Prolog Programming, Search Algorithm Implementation, Expert System Development, NLP Task Implementation, Logic Programming Exercises |
| 22CS2109 | Advanced Java & Web Technologies | Core | 3 | Servlets and JSP, JDBC Connectivity, Introduction to Spring Framework, HTML, CSS, JavaScript, Web Security Basics |
| 22CS2110 | Advanced Java & Web Technologies Lab | Lab | 1 | Web Application Development, Database Integration, Front-end Development, Back-end API Creation, Deployment to Web Servers |
| 22EV2001 | Environmental Science | Humanities | 2 | Ecosystems and Biodiversity, Environmental Pollution, Climate Change, Renewable Energy Sources, Environmental Protection and Management |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CS3101 | Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization |
| 22DS3001 | Big Data Analytics | Core | 3 | Big Data Ecosystem, Hadoop and MapReduce, HDFS Architecture, Apache Spark, NoSQL Databases |
| 22DS3002 | Deep Learning | Core | 3 | Neural Network Architectures, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transfer Learning and Fine-tuning |
| 22DS3003 | Deep Learning Lab | Lab | 1 | Implementing Neural Networks, Image Classification with CNNs, Sequence Prediction with RNNs, Generative Adversarial Networks (GANs), Using TensorFlow/PyTorch |
| 22DS3004 | Big Data Analytics Lab | Lab | 1 | Hadoop MapReduce Programs, Apache Spark Applications, Hive and Pig Queries, HBase Operations, Data Streaming with Kafka |
| 22DS3005 | Natural Language Processing (Professional Elective - I) | Elective | 3 | Text Preprocessing, N-grams and Language Models, Word Embeddings, POS Tagging and NER, Sentiment Analysis |
| 22DS3006 | Computer Vision (Professional Elective - II) | Elective | 3 | Image Filtering and Enhancement, Feature Detection and Extraction, Image Segmentation, Object Recognition, Deep Learning for Computer Vision |
| 22CS3106 | Research Project - I | Project | 2 | Problem Identification, Literature Survey, Research Methodology, Project Planning, Preliminary Design and Prototyping |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22SM3001 | Project Management and Entrepreneurship | Humanities | 3 | Project Life Cycle, Project Planning and Scheduling, Risk Management, Entrepreneurial Skills, Business Plan Development |
| 22DS3007 | Data Warehousing and Mining | Core | 3 | Data Warehouse Architecture, OLAP Operations, ETL Process, Data Mining Concepts, Association Rule Mining, Classification and Clustering |
| 22DS3008 | Reinforcement Learning | Core | 3 | Markov Decision Processes, Value and Policy Iteration, Q-Learning, SARSA Algorithm, Deep Reinforcement Learning |
| 22DS3009 | Reinforcement Learning Lab | Lab | 1 | Implementing RL Algorithms, OpenAI Gym Environments, Q-Table Management, Policy Gradient Methods, Agent Training and Evaluation |
| 22DS3010 | Data Warehousing and Mining Lab | Lab | 1 | OLAP Cube Operations, ETL Tool Usage, Data Mining Tools (Weka/RapidMiner), Data Preprocessing for Mining, Clustering and Classification Tasks |
| 22DS3011 | Time Series Analysis (Professional Elective - III) | Elective | 3 | Time Series Components, ARIMA Models, Exponential Smoothing, Forecasting Techniques, Deep Learning for Time Series |
| 22DS3012 | Recommender Systems (Professional Elective - IV) | Elective | 3 | Collaborative Filtering, Content-Based Filtering, Hybrid Recommender Systems, Matrix Factorization, Evaluation Metrics for Recommenders |
| 22CS3111 | Research Project - II | Project | 2 | Project Implementation, Data Analysis and Experimentation, Result Interpretation, Technical Report Writing, Project Presentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22SM4001 | Universal Human Values-2 | Humanities | 3 | Understanding Harmony in Human Beings, Harmony in the Family, Harmony in Society, Harmony in Nature, Professional Ethics and Holistic Living |
| 22DS4001 | Industry Internship / Project Work | Project | 8 | Real-world Problem Solving, Industrial Practices and Tools, Project Implementation and Testing, Documentation and Reporting, Professional Communication |
| OE4XX1 | Open Elective - I | Elective | 3 | Topics vary based on chosen elective, Interdisciplinary concepts, Skill enhancement, Broadening academic horizons, Application-oriented learning |
| OE4XX2 | Open Elective - II | Elective | 3 | Topics vary based on chosen elective, Career-focused modules, Emerging technologies, Personal interest areas, Soft skills development |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CS4001 | Major Project | Project | 12 | Advanced System Design, Development and Implementation, Testing and Validation, Deployment Strategies, Comprehensive Project Report and Defense |
| 22DS4002 | Explainable AI (Professional Elective - V) | Elective | 3 | Interpretability and Transparency, Local and Global Interpretability, SHAP Values, LIME Method, Explainable AI Frameworks |
| 22DS4003 | Ethical AI (Professional Elective - VI) | Elective | 3 | AI Ethics Principles, Bias in AI Systems, Fairness and Accountability, AI Privacy Concerns, Responsible AI Development |
