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BCA 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 University focuses on equipping students with essential skills in data analysis, machine learning, and visualization. It prepares students to extract actionable insights from complex datasets, a critical need in India''''s rapidly expanding digital economy. The curriculum emphasizes practical applications, integrating theoretical knowledge with hands-on projects to foster industry-ready professionals.
Who Should Apply?
This program is ideal for 10+2 graduates with a strong aptitude for mathematics, statistics, or computer science, seeking entry into the lucrative data science field. It also caters to individuals looking for a foundational degree to pursue advanced studies or jumpstart their careers in data analytics, machine learning, or business intelligence roles within Indian tech firms and startups.
Why Choose This Course?
Graduates of this program can expect to pursue dynamic career paths such as Data Analyst, Machine Learning Engineer, Business Intelligence Developer, or Data Scientist in India. Entry-level salaries typically range from INR 3-6 LPA, with significant growth potential up to INR 10-15 LPA for experienced professionals. The program aligns with certifications from prominent platforms, enhancing employability in the competitive Indian job market.
Student Success Practices
Foundation Stage
Master Foundational Programming & Logic- (Semester 1-2)
Dedicate significant time to thoroughly understand Python, C, and Java programming concepts, alongside discrete mathematics and statistical fundamentals. Practice coding regularly using online platforms to build strong problem-solving skills, which are crucial for data science algorithms.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation, JavaTpoint, Khan Academy for Math
Career Connection
Strong programming and logical reasoning are prerequisites for any data science role, forming the base for understanding complex algorithms and data manipulation.
Build a Strong Statistical & Data Visualization Base- (Semester 1-2)
Focus on internalizing statistical methods for data science and mastering data visualization tools like Tableau or PowerBI. Work on small projects to visualize public datasets, practicing data cleaning, exploration, and effective presentation of insights.
Tools & Resources
Kaggle datasets, Tableau Public, PowerBI Desktop, R/Python for basic statistical analysis
Career Connection
Essential for any Data Analyst or Junior Data Scientist role, allowing effective interpretation and communication of data insights.
Engage in Peer Learning & Academic Support- (Semester 1-2)
Form study groups with peers to discuss challenging concepts, review code, and prepare for exams. Actively participate in academic support sessions offered by the university and seek guidance from faculty for difficult topics. This fosters a collaborative learning environment and strengthens understanding.
Tools & Resources
University academic support centers, Discord/WhatsApp study groups, Faculty office hours
Career Connection
Develops teamwork, communication, and critical thinking skills, vital for collaborative project environments in the industry.
Intermediate Stage
Deep Dive into Core Data Science Algorithms & Tools- (Semester 3-4)
Go beyond theoretical understanding of Machine Learning, Data Mining, and Big Data by implementing algorithms from scratch and using industry-standard libraries (Scikit-learn, Pandas, NumPy, Spark). Explore advanced SQL for database management and gain proficiency in R for statistical computing.
Tools & Resources
Jupyter Notebooks, Google Colab, Scikit-learn, Pandas, Apache Spark, MySQL/PostgreSQL, RStudio
Career Connection
Directly translates to skills required for Machine Learning Engineer, Data Engineer, and Data Scientist roles, enabling complex data analysis and model building.
Pursue Relevant Internships & Industry Projects- (Semester 3-5)
Actively seek out internships during summer breaks or part-time industry projects that align with data science. Apply classroom knowledge to real-world problems, build a portfolio of work, and gain exposure to professional work environments and industry practices.
Tools & Resources
LinkedIn, Internshala, Company career pages, University placement cell, Faculty network
Career Connection
Crucial for gaining practical experience, making industry contacts, and improving resume for full-time placements. Many internships lead to Pre-Placement Offers (PPOs).
Participate in Data Science Competitions & Workshops- (Semester 3-5)
Engage in online data science competitions (e.g., Kaggle, Analytics Vidhya) and attend workshops or webinars on emerging data science technologies. This helps in continuous learning, applying skills to diverse datasets, and staying updated with industry trends.
Tools & Resources
Kaggle, Analytics Vidhya, GitHub, Industry conferences, Specialized online courses (Coursera, edX)
Career Connection
Enhances problem-solving abilities, provides unique projects for portfolio, and demonstrates initiative to potential employers.
Advanced Stage
Develop a Capstone Project with Real-World Impact- (Semester 6)
Undertake a significant final year project, ideally with an industry mentor or addressing a community problem. Focus on end-to-end implementation, from data collection and model deployment to performance evaluation and user interface design. Document the project meticulously.
Tools & Resources
Cloud platforms (AWS, Azure, GCP), Docker, Git, Relevant programming languages and libraries
Career Connection
Showcases comprehensive skills to recruiters, demonstrates ability to deliver complete solutions, and often forms the core of interview discussions.
Master Interview Preparation & Networking- (Semester 6)
Begin intensive preparation for technical interviews by practicing common data structures, algorithms, SQL queries, and machine learning concepts. Focus on case studies and behavioral questions. Network with alumni and industry professionals through career fairs and LinkedIn.
Tools & Resources
InterviewBit, LeetCode, Glassdoor, LinkedIn, University career services
Career Connection
Maximizes chances of securing top placements in leading companies. Networking can open doors to opportunities not advertised publicly.
Specialise in Niche Data Science Domains- (Semester 6)
Based on interest and career goals, deep dive into a specific area like Deep Learning, Natural Language Processing, or Big Data Engineering. Pursue advanced certifications or online courses in these niche fields to differentiate yourself in the job market.
Tools & Resources
DeepLearning.AI, NVIDIA DLI, Specialized MOOCs, Industry certifications (e.g., AWS Certified Machine Learning Specialty)
Career Connection
Positions graduates for specialized roles with higher earning potential and faster career growth in cutting-edge domains of data science.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 or its equivalent examination with 60% and above. Students with Mathematics/Statistics/Computer Science/Information Technology as one of the subjects are eligible to apply.
Duration: 3 years / 6 semesters
Credits: 135 (as stated in official document, sum of courses is 121) Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20CA1110 | Problem Solving & Programming using Python | Core Theory | 3 | Python Fundamentals, Data Types & Structures, Control Flow, Functions & Modules, File Handling, Object-Oriented Programming Basics |
| 20CA1111 | Computer Organization & Architecture | Core Theory | 3 | Digital Logic Circuits, Data Representation, CPU Organization, Memory System Hierarchy, Input/Output Organization, Pipelining |
| 20CA1112 | Mathematical Foundations for Data Science | Core Theory | 3 | Set Theory & Logic, Relations & Functions, Graph Theory, Combinatorics, Number Theory, Algebraic Structures |
| 20HS1101 | English for Communication | Humanities | 2 | Grammar & Vocabulary, Listening & Speaking Skills, Reading Comprehension, Writing Paragraphs & Essays, Presentation Skills, Non-verbal Communication |
| 20CA1113 | Data Visualization | Core Theory | 3 | Introduction to Data Visualization, Types of Data, Visualization Techniques, Dashboard Design, Storytelling with Data, Tools like Tableau/PowerBI |
| 20CA1180 | Python Programming Lab | Core Lab | 1.5 | Python Environment Setup, Basic Syntax & Operations, Control Structures, Functions & Data Structures, File Operations, Simple OOP Implementations |
| 20CA1181 | Computer Organization & Architecture Lab | Core Lab | 1.5 | Logic Gates & Boolean Algebra, Combinational Circuits, Sequential Circuits, Registers & Counters, Memory Unit Simulation, Basic CPU Design Concepts |
| 20CA1182 | Data Visualization Lab | Core Lab | 1.5 | Data Import & Cleaning, Creating Various Chart Types, Interactive Dashboards, Advanced Visualizations, Reporting with Visualization Tools, Data Storytelling Exercises |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20CA1210 | Programming in C | Core Theory | 3 | C Language Fundamentals, Control Statements, Functions & Arrays, Pointers & Strings, Structures & Unions, File I/O |
| 20CA1211 | Operating Systems | Core Theory | 3 | OS Concepts & Structures, Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems |
| 20CA1212 | Object Oriented Programming using Java | Core Theory | 3 | OOP Principles, Classes & Objects, Inheritance & Polymorphism, Interfaces & Packages, Exception Handling, Multithreading |
| 20CA1213 | Statistical Methods for Data Science | Core Theory | 3 | Probability Theory, Random Variables & Distributions, Sampling & Estimation, Hypothesis Testing, Regression Analysis, ANOVA |
| 20CA1214 | Data Structures & Algorithms | Core Theory | 3 | Arrays & Linked Lists, Stacks & Queues, Trees & Graphs, Searching Algorithms, Sorting Algorithms, Hashing |
| 20CA1280 | Programming in C Lab | Core Lab | 1.5 | Implementing C Programs, Using Control Structures, Functions & Pointers, Arrays & Strings Manipulation, Structures & File I/O, Debugging C Code |
| 20CA1281 | Object Oriented Programming using Java Lab | Core Lab | 1.5 | Java Class & Object Creation, Inheritance & Polymorphism Exercises, Interface & Package Implementation, Exception Handling Practices, Basic GUI Programming, Multithreading Applications |
| 20CA1282 | Data Structures & Algorithms Lab | Core Lab | 1.5 | Implementation of Linked Lists, Stack & Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting & Searching Implementations, Efficiency Analysis |
| 20ES1201 | Environmental Science | Basic Science | 2 | Ecosystems & Biodiversity, Natural Resources, Environmental Pollution, Social Issues & the Environment, Environmental Ethics, Sustainable Development |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20CA2110 | Database Management System | Core Theory | 3 | DBMS Concepts, ER Model, Relational Model, SQL Queries, Normalization, Transaction Management |
| 20CA2111 | Computer Networks | Core Theory | 3 | Network Topologies, OSI & TCP/IP Models, Data Link Layer, Network Layer, Transport Layer, Application Layer Protocols |
| 20CA2112 | Machine Learning | Core Theory | 3 | Introduction to ML, Supervised Learning, Unsupervised Learning, Model Evaluation Metrics, Regression Techniques, Classification Algorithms |
| 20CA2113 | Web Designing | Core Theory | 3 | HTML5 Structure, CSS Styling, JavaScript Interactivity, Responsive Web Design, UI/UX Principles, Web Hosting Basics |
| 20CA2114 | Cloud Computing Fundamentals | Core Theory | 3 | Cloud Computing Concepts, Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization, Cloud Security Challenges, Cloud Ecosystem Providers |
| 20CA2180 | Database Management System Lab | Core Lab | 1.5 | SQL DDL & DML Commands, Advanced SQL Queries, Joins & Subqueries, Stored Procedures & Functions, Triggers & Cursors, Database Design Exercises |
| 20CA2181 | Machine Learning Lab | Core Lab | 1.5 | Data Preprocessing & Cleaning, Implementing Regression Models, Implementing Classification Models, Clustering Techniques, Model Evaluation & Tuning, Using Scikit-learn |
| 20CA2182 | Web Designing Lab | Core Lab | 1.5 | Creating HTML Layouts, CSS Styling & Responsive Design, JavaScript DOM Manipulation, Form Validation, Integrating Multimedia, Building Interactive Web Pages |
| 20CA2120 | Design Thinking | Skill Oriented | 2 | Introduction to Design Thinking, Empathize Stage, Define Stage, Ideate Stage, Prototype Stage, Test Stage |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20CA2210 | Data Warehousing & Data Mining | Core Theory | 3 | Data Warehousing Concepts, OLAP & ETL, Data Mining Techniques, Association Rule Mining, Classification Algorithms, Clustering Algorithms |
| 20CA2211 | Artificial Intelligence | Core Theory | 3 | AI Fundamentals, Intelligent Agents, Search Algorithms, Knowledge Representation, Machine Learning Basics, Expert Systems |
| 20CA2212 | Big Data Analytics | Core Theory | 3 | Big Data Concepts, Hadoop Ecosystem, HDFS, MapReduce, Apache Spark, NoSQL Databases |
| 20CA2213 | Neural Networks & Deep Learning | Core Theory | 3 | Neural Network Architecture, Activation Functions, Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Frameworks (TensorFlow/Keras) |
| 20CA2214 | R Programming for Data Science | Core Theory | 3 | R Basics & Data Types, Data Structures in R, Data Manipulation with dplyr, Statistical Graphics with ggplot2, R Packages for Data Science, Functions & Control Flow in R |
| 20CA2280 | Data Warehousing & Data Mining Lab | Core Lab | 1.5 | ETL Processes, OLAP Operations, Implementing Association Rules, Classification & Clustering using Tools, Data Preprocessing Techniques, Using Weka/Pentaho |
| 20CA2281 | Big Data Analytics Lab | Core Lab | 1.5 | Hadoop Setup & HDFS Commands, MapReduce Programming, Spark RDD & DataFrames, Hive & Pig Scripting, NoSQL Database Operations, Big Data Tools Exploration |
| 20CA2282 | Neural Networks & Deep Learning Lab | Core Lab | 1.5 | Implementing Feedforward Networks, Building CNNs for Image Tasks, Building RNNs for Sequence Tasks, Transfer Learning, Hyperparameter Tuning, Using TensorFlow/Keras |
| 20SS2201 | Constitution of India | Skill Oriented | 2 | Preamble & Fundamental Rights, Directive Principles of State Policy, Union & State Government, Indian Judiciary System, Amendment Procedures, Citizenship |
Semester 5
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20CA3280 | Major Project | Project | 6 | Advanced Project Planning, System Analysis & Design, Implementation of Complex Systems, Quality Assurance & Testing, Deployment & Maintenance, Technical Report & Viva-Voce |
| 20CA3281 | Internship | Internship | 9 | Industry Exposure & Practices, Real-world Problem Solving, Professional Skill Development, Teamwork & Communication, Project Implementation in Industry, Internship Report & Presentation |
