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B-SC in Data Science at Koneru Lakshmaiah Education Foundation (Deemed to be University)
Guntur, Andhra Pradesh
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About the Specialization
What is Data Science at Koneru Lakshmaiah Education Foundation (Deemed to be University) Guntur?
This Data Science program at Koneru Lakshmaiah, Guntur focuses on equipping students with essential skills in data analysis, machine learning, and big data technologies. It is designed to meet the escalating demand for data professionals in the Indian market, fostering innovation and analytical prowess. The curriculum integrates theoretical knowledge with practical application, preparing graduates for real-world challenges.
Who Should Apply?
This program is ideal for fresh graduates from science backgrounds (10+2) aspiring to enter the rapidly growing data industry. It also caters to those keen on analytical problem-solving, possessing a strong foundation in mathematics and statistics. Career changers looking to transition into data-centric roles will also find the comprehensive curriculum beneficial, offering a pathway into the dynamic field of data science.
Why Choose This Course?
Graduates of this program can expect diverse career paths such as Data Analyst, Machine Learning Engineer, Business Intelligence Developer, or Data Scientist in India. Entry-level salaries typically range from INR 4-7 LPA, with experienced professionals earning significantly more. The program’s strong practical focus aids in securing roles in IT services, e-commerce, banking, and healthcare sectors across major Indian cities.
Student Success Practices
Foundation Stage
Master Programming Fundamentals (Python & Data Structures)- (Semester 1-2)
Consistently practice Python programming, focusing on core concepts, data structures (lists, dictionaries, sets), and algorithmic thinking. Solve at least 2-3 coding problems daily to build a strong logical base.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation
Career Connection
Strong programming skills are foundational for all data science roles, crucial for data manipulation, algorithm implementation, and technical interviews.
Build a Solid Statistical and Mathematical Base- (Semester 1-2)
Pay close attention to Calculus, Linear Algebra, Probability, and Statistics courses. Practice numerical problems regularly and understand the underlying mathematical intuition behind data science concepts.
Tools & Resources
Khan Academy, NPTEL courses, Reference textbooks
Career Connection
A robust mathematical foundation is essential for understanding machine learning algorithms, model interpretation, and advanced data analysis techniques.
Engage in Peer Learning and Early Projects- (Semester 1-2)
Form study groups to discuss complex topics, clarify doubts, and collaboratively solve problems. Start working on small, self-initiated data projects using publicly available datasets to apply learned concepts.
Tools & Resources
Kaggle Datasets, Google Colab, GitHub for version control
Career Connection
Develops teamwork, problem-solving skills, and creates an early portfolio for showcasing practical application of knowledge to potential employers.
Intermediate Stage
Deepen Machine Learning and Database Skills- (Semester 3-5)
Go beyond classroom theory by implementing various machine learning algorithms from scratch (e.g., linear regression, k-means). Become proficient in SQL for complex data retrieval and manipulation, critical for data engineering.
Tools & Resources
Scikit-learn documentation, SQLZoo, DataCamp, Jupyter Notebooks, PostgreSQL
Career Connection
These are core competencies for Data Analyst, Machine Learning Engineer, and Data Scientist roles, directly applicable in interviews and real-world projects.
Acquire Industry-Relevant Tool Proficiency- (Semester 3-5)
Dedicate time to master popular data visualization tools like Tableau or Power BI, and explore cloud platforms such as AWS or Azure. Understand their services relevant to data storage, processing, and analytics.
Tools & Resources
Official Tableau/Power BI tutorials, AWS/Azure free tier accounts, Coursera courses on cloud essentials
Career Connection
Demonstrates practical readiness for industry roles, as most companies widely use these tools for data reporting and infrastructure management.
Participate in Hackathons and Competitions- (Semester 3-5)
Actively participate in data science hackathons and coding competitions on platforms like Kaggle. This provides hands-on experience with real-world datasets, exposure to diverse problem statements, and competitive pressure.
Tools & Resources
Kaggle, HackerEarth, University-organized hackathons
Career Connection
Builds a competitive portfolio, enhances problem-solving under pressure, and offers networking opportunities with industry experts and peers.
Advanced Stage
Undertake an Impactful Capstone Project- (Semester 6)
Choose a challenging Capstone Project that addresses a real-world problem, preferably in collaboration with an industry partner or research lab. Focus on end-to-end implementation, rigorous evaluation, and clear documentation.
Tools & Resources
Python libraries (TensorFlow, PyTorch, Pandas, NumPy), Cloud services, Project management tools
Career Connection
A strong Capstone Project acts as a centerpiece for resumes and interviews, showcasing advanced technical skills and problem-solving abilities to potential employers.
Secure a Relevant Industry Internship- (Semester 6)
Actively seek and complete an internship in a data science, machine learning, or data engineering role. Focus on gaining practical exposure to industry workflows, tools, and team dynamics, building professional connections.
Tools & Resources
LinkedIn, Internshala.com, College placement cell, Direct company applications
Career Connection
Internships are crucial for gaining real-world experience, converting into full-time roles, and understanding corporate culture and expectations for job readiness.
Focus on Interview Preparation and Soft Skills- (Semester 6)
Practice technical interview questions covering algorithms, data structures, SQL, and ML concepts. Simultaneously, refine communication, presentation, and teamwork skills, essential for professional success.
Tools & Resources
LeetCode, InterviewBit, Mock interviews with peers/mentors, Toastmasters or public speaking clubs
Career Connection
Prepares students for the rigorous placement process, increasing their chances of securing desirable job offers in top companies and ensuring career growth.
Program Structure and Curriculum
Eligibility:
- A Pass in 10+2 with a minimum of 50% Marks in aggregate or equivalent grade from a recognized Board/University.
Duration: 3 years (6 semesters)
Credits: 112 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23BS1101 | Calculus and Matrix Algebra | Core | 4 | Sequences and Series, Differential Calculus, Multivariable Calculus, Matrices and Determinants, System of Linear Equations |
| 23BS1102 | Python Programming for Data Science | Core | 4 | Python Language Fundamentals, Data Types and Operators, Control Flow and Functions, Object-Oriented Programming in Python, File Handling and Modules |
| 23BS1103 | Data Fundamentals | Core | 4 | Introduction to Data and Data Science, Data Collection Methods, Data Preprocessing Techniques, Introduction to Data Visualization, Data Ethics and Privacy |
| 23BS1104 | English Communication | Core | 3 | Fundamentals of Communication, Reading Comprehension Skills, Academic and Professional Writing, Presentation Techniques, Group Discussion Strategies |
| 23BS1151 | Python Programming for Data Science Lab | Lab | 1.5 | Python Environment Setup, Basic Python Programming Exercises, Implementing Data Structures, Functions and Modules Practice, File Operations and Error Handling |
| 23BS1152 | Data Fundamentals Lab | Lab | 1.5 | Data Cleaning and Transformation Tools, Basic Data Manipulation Techniques, Introductory Data Visualization Tools, Data Sourcing and Quality Checks, Fundamentals of SQL for Data Access |
| 23BS1161 | Sports/Yoga/NSS/NCC | Mandatory Activity | 0 | Physical Fitness Activities, Basic Yoga Practices, Community Service (NSS), Discipline and Leadership (NCC) |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23BS1201 | Probability and Statistics | Core | 4 | Basic Probability Theory, Random Variables and Distributions, Inferential Statistics, Hypothesis Testing, Correlation and Regression Analysis |
| 23BS1202 | Data Structures and Algorithms | Core | 4 | Arrays, Linked Lists, Stacks, Queues, Trees and Graph Data Structures, Sorting Algorithms (e.g., Bubble, Merge, Quick), Searching Algorithms (e.g., Linear, Binary), Algorithm Analysis (Time and Space Complexity) |
| 23BS1203 | Database Management Systems | Core | 4 | Introduction to DBMS and Data Models, Relational Model and SQL, Database Design (ER Diagrams, Normalization), Query Processing and Optimization, Transaction Management and Concurrency Control |
| 23BS1204 | Indian Constitution | Ability Enhancement | 2 | Historical Background of Indian Constitution, Preamble and Fundamental Rights, Directive Principles of State Policy, Structure and Functions of Union Government, Constitutional Amendments and Emergency Provisions |
| 23BS1251 | Data Structures and Algorithms Lab | Lab | 1.5 | Implementation of Linear Data Structures, Implementation of Non-Linear Data Structures, Practical Applications of Sorting Algorithms, Practical Applications of Searching Algorithms, Algorithm Efficiency Analysis through Coding |
| 23BS1252 | Database Management Systems Lab | Lab | 1.5 | SQL DDL and DML Commands, Advanced SQL Queries (Joins, Subqueries), Database Creation and Manipulation, Stored Procedures and Triggers, Building Simple Database Applications |
| 23BS1261 | Universal Human Values | Mandatory Activity | 0 | Understanding Human Values, Harmony in Individual and Society, Ethics and Morality, Professional Ethics, Holistic Development |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23BS1301 | Object Oriented Programming through Java | Core | 4 | Introduction to OOP Concepts, Classes, Objects, and Methods, Inheritance and Polymorphism, Exception Handling and Multithreading, GUI Programming with AWT/Swing |
| 23BS1302 | Introduction to Machine Learning | Core | 4 | Fundamentals of Machine Learning, Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation and Selection, Introduction to Ensemble Methods |
| 23BS1303 | Operating Systems | Core | 4 | Operating System Concepts, Process Management and Scheduling, Memory Management Techniques, File Systems and I/O Management, Deadlocks and Concurrency |
| 23BS13E1 | Data Visualization Tools | Generic Elective - I | 3 | Principles of Data Visualization, Introduction to Tableau/Power BI, Creating Various Chart Types, Designing Interactive Dashboards, Storytelling with Data |
| 23BS1351 | Object Oriented Programming Lab | Lab | 1.5 | Java Program Development, Implementation of OOP Principles, Developing Applications with Inheritance, Handling Exceptions in Java, Building Simple GUI Applications |
| 23BS1352 | Machine Learning Lab | Lab | 1.5 | Scikit-learn Library Basics, Implementing Regression Models, Implementing Classification Models, Practical Clustering Algorithms, Model Evaluation and Hyperparameter Tuning |
| 23BS13J1 | Mini Project - I | Project | 1 | Problem Identification and Scoping, Literature Survey and Data Collection, Project Design and Planning, Implementation of a Small-Scale Project, Report Writing and Presentation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23BS1401 | Big Data Technologies | Core | 4 | Introduction to Big Data Ecosystem, Hadoop Distributed File System (HDFS), MapReduce Programming Model, Apache Spark Framework, NoSQL Databases (e.g., Cassandra, MongoDB) |
| 23BS1402 | Deep Learning | Core | 4 | Fundamentals of Neural Networks, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Frameworks (TensorFlow/PyTorch) |
| 23BS1403 | Computer Networks | Core | 4 | Network Models (OSI, TCP/IP), Data Link Layer Protocols, Network Layer (IP addressing, Routing), Transport Layer (TCP, UDP), Application Layer Protocols (HTTP, DNS) |
| 23BS14E1 | Cloud Computing | Generic Elective - II | 3 | Introduction to Cloud Computing, Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, Virtualization Technology, Cloud Security and Management |
| 23BS1451 | Big Data Technologies Lab | Lab | 1.5 | Hadoop Ecosystem Setup and Commands, Developing MapReduce Programs, Apache Spark RDD Operations, Data Processing with Hive and Pig, Introduction to NoSQL Database Operations |
| 23BS1452 | Deep Learning Lab | Lab | 1.5 | Implementing Neural Networks with TensorFlow/Keras, Image Classification with CNNs, Text Classification with RNNs, Transfer Learning Techniques, Model Training and Evaluation |
| 23BS14J1 | Mini Project - II | Project | 1 | Advanced Problem Formulation, Data Collection and Preprocessing for Complex Data, Applying Machine Learning Models, Results Analysis and Interpretation, Project Documentation and Presentation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23BS1501 | Data Governance and Ethics | Core | 4 | Data Privacy Regulations (e.g., GDPR, India''''s DPDP Bill), Data Security and Compliance, Ethical Considerations in AI and Data Science, Data Ownership and Accountability, Fairness, Transparency, and Explainability in AI |
| 23BS1502 | Natural Language Processing | Core | 4 | Introduction to NLP and Text Preprocessing, Lexical and Syntactic Analysis, Word Embeddings and Vector Representations, Recurrent Neural Networks for NLP, Transformer Models and Attention Mechanisms |
| 23BS1503 | Data Engineering | Core | 4 | Data Architecture and Pipelines, ETL/ELT Processes, Data Warehousing Concepts, Data Lake Design and Management, Stream Processing with Kafka/Spark Streaming |
| 23BS15E1 | Computer Vision | Generic Elective - III | 3 | Image Processing Fundamentals, Feature Extraction and Description, Object Detection Techniques, Image Segmentation, Deep Learning for Computer Vision (CNNs) |
| 23BS1551 | Natural Language Processing Lab | Lab | 1.5 | NLTK and SpaCy Library Usage, Text Preprocessing and Tokenization, Sentiment Analysis Implementation, Building Simple Chatbots, Introduction to Machine Translation |
| 23BS1552 | Data Engineering Lab | Lab | 1.5 | Implementing ETL Pipelines, Building a Data Lake Prototype, Working with Apache Kafka, Real-time Data Processing with Spark Streaming, Workflow Orchestration with Apache Airflow |
| 23BS15J1 | Mini Project - III | Project | 1 | Complex Problem Analysis and Solution Design, Advanced Data Analysis and Modeling, Application Development and Deployment, Performance Evaluation and Optimization, Detailed Project Report and Presentation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23BS1601 | Capstone Project | Project | 12 | End-to-End Project Planning and Management, Advanced Data Acquisition and Cleaning, System Design and Architecture, Implementation and Testing, Comprehensive Report Writing and Final Presentation |
| 23BS1602 | Professional Ethics and Intellectual Property Rights | Core | 2 | Ethical Theories and Professional Conduct, Data Ethics and Responsible AI, Introduction to Intellectual Property Rights, Copyright, Patent, Trademark Laws in India, Cyber Law and Digital Rights |
| 23BS1603 | Internship / Industrial Training | Internship | 6 | Practical Application of Data Science Skills, Exposure to Industry Workflows and Tools, Professional Skill Development (Communication, Teamwork), Problem Solving in a Real-World Setting, Internship Report and Presentation |
