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B-C-A in Data Science at Kalinga University
Raipur, Chhattisgarh
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About the Specialization
What is Data Science at Kalinga University Raipur?
This Data Science program at Kalinga University focuses on equipping students with essential skills for extracting insights and knowledge from complex data. In the rapidly evolving Indian industry, data science is crucial for informed decision-making across sectors like e-commerce, healthcare, and finance. The program integrates theoretical foundations with practical applications, preparing graduates for real-world challenges in data analysis and machine learning.
Who Should Apply?
This program is ideal for fresh graduates with a background in 10+2 Mathematics or Computer Science, seeking entry into the high-demand data science field. It also caters to individuals looking to upskill in areas like machine learning, big data, and artificial intelligence. Career changers transitioning into analytical roles will find the structured curriculum beneficial, providing a solid foundation for a data-driven career path in India.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding career paths such as Data Analyst, Machine Learning Engineer, Business Intelligence Developer, or AI Specialist within Indian companies and MNCs operating in India. Entry-level salaries typically range from 3-6 LPA, with experienced professionals earning significantly more. The curriculum aligns with industry demands, fostering skills for certifications in tools like Python, R, SQL, and popular cloud platforms, ensuring strong growth trajectories.
Student Success Practices
Foundation Stage
Master Programming Fundamentals (C, C++, Python)- (Semester 1-2)
Dedicate consistent time to practice core programming concepts in C, C++, and Python. Utilize online coding platforms like HackerRank, CodeChef, and GeeksforGeeks for daily problem-solving, focusing on data structures and algorithms, which are foundational for data science.
Tools & Resources
HackerRank, CodeChef, GeeksforGeeks, Jupyter Notebook, Online C++ compilers
Career Connection
Strong programming skills are non-negotiable for data science roles, impacting interview performance and the ability to implement complex algorithms. This foundation directly leads to higher chances of securing technical internships and entry-level positions.
Build a Solid Mathematical & Statistical Base- (Semester 1-2)
Regularly revise concepts of linear algebra, calculus, probability, and statistics. Use resources like Khan Academy, NPTEL lectures, and textbooks to deepen understanding. Actively participate in ''''Mathematics for Data Science'''' lab sessions to apply theoretical knowledge.
Tools & Resources
Khan Academy, NPTEL, MIT OpenCourseware (Mathematics), Python''''s NumPy/SciPy
Career Connection
A strong grasp of mathematics and statistics is critical for understanding the mechanics of machine learning algorithms and interpreting model results, which are highly valued by analytics firms and research divisions.
Engage in Peer Learning and Discussion Groups- (Semester 1-2)
Form small study groups with peers to discuss complex topics, share insights, and collaboratively solve problems. Explain concepts to each other to solidify understanding and develop communication skills essential for data science teams.
Tools & Resources
WhatsApp groups, Discord channels, University library study rooms
Career Connection
Collaboration and communication are key in real-world data science projects. Practicing these skills early on enhances teamwork abilities, crucial for project-based roles and contributes to a supportive learning environment.
Intermediate Stage
Undertake Mini-Projects and Kaggle Competitions- (Semester 3-5)
Apply learned concepts in DBMS, Python, and Machine Learning by working on small-scale personal projects. Participate in introductory Kaggle competitions or similar data challenges to gain practical experience with real datasets and different problem types.
Tools & Resources
Kaggle.com, GitHub, Google Colab, Scikit-learn
Career Connection
Building a portfolio of projects is vital for showcasing practical skills to potential employers. Experience in competitions demonstrates problem-solving abilities and resilience, significantly boosting internship and placement prospects.
Develop Database and SQL Proficiency- (Semester 3-5)
Practice SQL extensively using online tutorials and real-world datasets. Focus on complex queries, joins, and database design principles learned in DBMS. Proficiency in SQL is a fundamental requirement for almost all data-related roles.
Tools & Resources
MySQL Workbench, PostgreSQL, SQLZoo.net, LeetCode SQL
Career Connection
Database skills are the backbone of data extraction and management. Mastery of SQL is frequently tested in interviews for Data Analyst, BI Developer, and Data Engineer roles, ensuring efficient data manipulation for any project.
Explore Data Science Tools and Libraries- (Semester 3-5)
Beyond classroom labs, explore popular Python libraries like Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn. Work through online courses (Coursera, Udemy) or documentation to understand their functionalities and practical applications.
Tools & Resources
Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn documentation, Coursera/Udemy Data Science courses
Career Connection
Familiarity with industry-standard tools and libraries makes you job-ready. Employers look for candidates who can immediately contribute using these established platforms, accelerating your learning curve in a professional setting.
Advanced Stage
Focus on Specialization (Deep Learning, NLP, Big Data)- (Semester 6)
Deep dive into your chosen specialization areas like Deep Learning and Natural Language Processing. Engage with advanced topics, research papers, and build complex models. Explore frameworks like TensorFlow/PyTorch through dedicated online courses and projects.
Tools & Resources
TensorFlow, Keras, PyTorch, Hugging Face, NLTK, Spark, Hadoop
Career Connection
Specialized knowledge in advanced fields like Deep Learning or NLP sets you apart, opening doors to roles as AI/ML Engineer, NLP Scientist, or Big Data Specialist, often with higher growth potential and innovative work.
Secure and Excel in Internships/Projects- (Semester 6)
Actively seek and participate in relevant internships or major data science projects. Aim to contribute significantly to real-world problems. Document your contributions, challenges, and solutions meticulously for your resume and interview discussions.
Tools & Resources
LinkedIn, Internshala, Company career pages, Project management tools
Career Connection
Internships provide invaluable industry exposure and often lead to pre-placement offers. Demonstrating successful project completion on your resume proves practical application of skills, making you a highly desirable candidate for placements.
Prepare for Placements and Professional Networking- (Semester 6)
Refine your resume and portfolio, focusing on your data science projects and skills. Practice mock interviews, including technical and behavioral rounds. Network with alumni and industry professionals through LinkedIn and college career fairs for insights and opportunities.
Tools & Resources
LinkedIn, Mock interview platforms, Kalinga University Alumni Network, Career services
Career Connection
Effective placement preparation and networking are crucial for securing desired jobs. A polished professional presence and strong interview skills directly lead to successful placements in top companies.
Program Structure and Curriculum
Eligibility:
- 10+2 with Mathematics/Computer Science/Information Practice/Statistics/Business Mathematics/Equivalent recognized by Board/University with 45% (40% for SC/ST/OBC).
Duration: 6 semesters / 3 years
Credits: 146 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCA-101 | Computer Fundamentals | Core | 4 | Computer Generations and Classification, Hardware and Software Concepts, Input and Output Devices, Memory Organization (Primary and Secondary), Operating System Introduction, Number Systems |
| BCA-102 | Programming in C | Core | 4 | Fundamentals of C Programming, Operators and Expressions, Control Structures (Conditional, Looping), Arrays and Strings, Functions and Pointers, Structures, Unions, and File Handling |
| BCA-103 | Digital Electronics | Core | 4 | Number Systems and Conversions, Logic Gates and Boolean Algebra, K-Maps and Combinational Circuits, Flip-Flops and Sequential Circuits, Registers and Counters, Analog-to-Digital Converters |
| BCA-104 | Business Communication | Core | 4 | Process and Types of Communication, Verbal and Non-Verbal Communication, Listening Skills and Feedback, Business Letters and Memos, Report Writing and Presentations, Interview and Group Discussion Skills |
| BCA-105 | Computer Fundamentals Lab | Lab | 2 | Basic Computer Operations, Windows and Linux File Management, MS Office Applications (Word, Excel, PowerPoint), Internet Browsing and Email, Peripheral Device Handling |
| BCA-106 | Programming in C Lab | Lab | 2 | C Program Structure, Conditional and Looping Constructs, Array and String Manipulation, Function Implementation, Pointer Arithmetic, File Operations |
| BCA-107 | Digital Electronics Lab | Lab | 2 | Logic Gate Verification, Boolean Function Implementation, Adder and Subtractor Circuits, Multiplexer and Demultiplexer, Flip-Flop Circuits, Counter and Register Design |
| BCA-108 | Communication Lab | Lab | 2 | Self-Introduction and Public Speaking, Presentation Techniques, Group Discussion Practice, Interview Role-Plays, Email Etiquette, Body Language and Confidence Building |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCA-201 | Data Structures | Core | 4 | Introduction to Data Structures, Arrays and Linked Lists, Stacks and Queues, Trees and Binary Search Trees, Graphs and Graph Traversal, Searching and Sorting Algorithms |
| BCA-202 | Object Oriented Programming with C++ | Core | 4 | OOP Concepts (Encapsulation, Inheritance), Classes and Objects, Constructors and Destructors, Polymorphism (Function/Operator Overloading), Virtual Functions and Abstract Classes, Exception Handling and File I/O |
| BCA-203 | Computer Architecture and Organization | Core | 4 | Basic Computer Organization, CPU Organization and Instruction Set, Memory Hierarchy (Cache, Main Memory), Input/Output Organization, Pipelining and Parallel Processing, Control Unit Design |
| BCA-204 | Mathematics for Data Science | Core | 4 | Linear Algebra (Matrices, Vectors, Eigenvalues), Calculus (Differentiation, Integration, Optimization), Probability Theory (Random Variables, Distributions), Statistics (Descriptive, Inferential, Hypothesis Testing), Set Theory and Combinatorics, Regression Analysis Fundamentals |
| BCA-205 | Data Structure Lab | Lab | 2 | Array and Linked List Implementation, Stack and Queue Operations, Binary Search Tree Traversal, Graph Representation and Algorithms, Bubble Sort, Quick Sort Implementation, Linear and Binary Search |
| BCA-206 | Object Oriented Programming with C++ Lab | Lab | 2 | Class and Object Definition, Inheritance Implementation, Polymorphism Examples, Constructor and Destructor Usage, File Handling in C++, Template Programming |
| BCA-207 | Computer Architecture & Organization Lab | Lab | 2 | Assembly Language Programming Basics, Data Transfer Operations, Arithmetic and Logic Operations, Memory Addressing Modes, I/O Device Control, Introduction to Simulator Tools |
| BCA-208 | Mathematics for Data Science Lab | Lab | 2 | Matrix Operations using Libraries, Vector Operations and Dot Product, Probability Calculation Simulations, Descriptive Statistics using Python/R, Basic Hypothesis Testing, Optimization Techniques |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCA-301 | Operating System | Core | 4 | Operating System Overview, Process Management and Scheduling, Inter-process Communication, Deadlocks and Prevention, Memory Management Techniques, File Systems and I/O Management |
| BCA-302 | Database Management System | Core | 4 | DBMS Architecture and Data Models, Entity-Relationship (ER) Model, Relational Model and Algebra, Structured Query Language (SQL), Normalization and Dependencies, Transaction Management and Concurrency Control |
| BCA-303 | Data Communication and Computer Network | Core | 4 | Network Topologies and Types, OSI and TCP/IP Reference Models, Transmission Media, Data Link Layer Protocols, Network Layer (IP Addressing, Routing), Transport Layer (TCP, UDP) |
| BCA-304 | Core Java | Core | 4 | Java Language Fundamentals, Classes, Objects, and Methods, Inheritance, Interfaces, and Packages, Exception Handling, Multithreading in Java, Applets and GUI Programming (AWT/Swing) |
| BCA-305 | Operating System Lab | Lab | 2 | Linux Commands and Shell Scripting, Process Creation and Management, CPU Scheduling Algorithms, Deadlock Detection and Prevention, Memory Allocation Algorithms, File System Operations |
| BCA-306 | Database Management System Lab | Lab | 2 | SQL DDL and DML Commands, Advanced SQL Queries (Joins, Subqueries), PL/SQL Programming Basics, Database Schema Design, Trigger and Cursor Implementation, Normalization Practical Exercises |
| BCA-307 | Data Communication and Computer Network Lab | Lab | 2 | Network Cable Crimping, IP Addressing and Subnetting, Network Configuration Commands (ping, tracert), Socket Programming, Packet Sniffing Tools (Wireshark), Basic Router and Switch Configuration |
| BCA-308 | Core Java Lab | Lab | 2 | Java Program Development, Object-Oriented Programming Implementations, Exception Handling Programs, Multithreading Applications, GUI Development with AWT/Swing, File I/O Operations |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCA-401 | Python Programming | Core | 4 | Python Basics and Data Types, Control Flow and Functions, Data Structures (Lists, Tuples, Dictionaries), Modules and Packages, Object-Oriented Programming in Python, File I/O and Exception Handling |
| BCA-402 | Web Technology | Core | 4 | HTML Fundamentals and Structure, CSS Styling and Layout, JavaScript for Client-side Scripting, DOM Manipulation and Events, XML Basics, Introduction to Web Servers and Hosting |
| BCA-403 | Software Engineering | Core | 4 | Software Development Life Cycle Models, Requirements Engineering and Analysis, Software Design Principles, Software Testing Techniques, Software Maintenance and Configuration Management, Software Project Management |
| BCA-404 | Elective - I (General Pool) | Elective | 4 | |
| BCA-405 | Python Programming Lab | Lab | 2 | Python Scripting for Basic Tasks, List, Tuple, Dictionary Operations, Function and Module Creation, File Handling in Python, Object-Oriented Python Programming, Exception Handling Practice |
| BCA-406 | Web Technology Lab | Lab | 2 | HTML Page Design, CSS Styling Implementation, JavaScript for Interactive Web Pages, Form Validation using JavaScript, DOM Manipulation Exercises, Basic Web Hosting Concepts |
| BCA-407 | Software Engineering Lab | Lab | 2 | UML Diagrams (Usecase, Class, Sequence), Requirements Gathering Documentation, Software Design Document Creation, Test Case Generation, Version Control System (Git) Basics, Project Planning Tools |
| BCA-408 | Elective - I Lab (General Pool) | Lab | 2 |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCA-501 | Artificial Intelligence | Core | 4 | Introduction to AI and its Applications, Problem-Solving using AI (Search Algorithms), Knowledge Representation Techniques, Logical Reasoning and Expert Systems, Introduction to Machine Learning, Natural Language Processing Fundamentals |
| BCA-502 | Big Data Analytics | Core | 4 | Introduction to Big Data Concepts, Hadoop Ecosystem (HDFS, MapReduce), Data Ingestion with Sqoop and Flume, Data Processing with Hive and Pig, NoSQL Databases (Cassandra, MongoDB), Introduction to Apache Spark |
| BCA-503 | Data Visualization | Core | 4 | Principles of Data Visualization, Types of Charts and Graphs, Dashboard Design Best Practices, Data Visualization Tools (Tableau/Power BI), Interactive Visualizations, Storytelling with Data |
| BCA-504 | Machine Learning | Core | 4 | Introduction to Machine Learning, Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation Metrics, Bias-Variance Tradeoff, Decision Trees, SVM, K-Nearest Neighbors |
| BCA-505 | Artificial Intelligence Lab | Lab | 2 | Python for AI Programming, Search Algorithm Implementation (BFS, DFS), Constraint Satisfaction Problems, Expert System Shells, Basic Machine Learning Model Training, AI Library Usage (NumPy, Pandas) |
| BCA-506 | Big Data Analytics Lab | Lab | 2 | HDFS Commands and Operations, MapReduce Program Development, Hive Query Language (HQL), Pig Scripting, Spark RDD Operations, NoSQL Database Operations |
| BCA-507 | Data Visualization Lab | Lab | 2 | Data Import and Preparation, Chart Creation (Bar, Line, Scatter), Dashboard Development, Interactive Filtering and Sorting, Geospatial Visualization, Story Creation and Sharing |
| BCA-508 | Machine Learning Lab | Lab | 2 | Data Preprocessing Techniques, Implementing Regression Models, Implementing Classification Algorithms, Clustering Algorithm Practice, Model Training and Evaluation, Using Scikit-learn Library |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCA-601 | Deep Learning | Core | 4 | Introduction to Deep Learning, Artificial Neural Networks, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Frameworks (TensorFlow/Keras) |
| BCA-602 | Natural Language Processing | Core | 4 | Introduction to NLP, Text Preprocessing (Tokenization, Stemming), Part-of-Speech Tagging, Named Entity Recognition, Sentiment Analysis, Language Models and Word Embeddings |
| BCA-603 | Data Science Project | Project | 4 | Problem Definition and Scope, Data Collection and Cleaning, Exploratory Data Analysis, Model Selection and Development, Project Evaluation and Reporting, Presentation of Findings |
| BCA-604 | Business Intelligence (Elective – II) | Elective | 4 | Introduction to Business Intelligence, Data Warehousing Concepts, Online Analytical Processing (OLAP), BI Dashboards and Reporting, Data Mining for Business Insights, Predictive Analytics in BI |
| BCA-605 | Deep Learning Lab | Lab | 2 | TensorFlow/Keras Environment Setup, Implementing Feedforward Networks, Training CNNs for Image Classification, Building RNNs for Sequence Data, Hyperparameter Tuning, Transfer Learning Applications |
| BCA-606 | Natural Language Processing Lab | Lab | 2 | NLTK Library Usage, Text Preprocessing Implementations, POS Tagging Algorithms, NER Model Building, Sentiment Analysis on Text Data, Word Embeddings Generation |
| BCA-607 | Project / Internship | Project/Internship | 4 | Industry-Specific Problem Solving, Application of Theoretical Knowledge, Professional Report Writing, Presentation and Communication Skills, Teamwork and Collaboration, Ethical Considerations in Projects |
| BCA-608 | Elective – II Lab (Data Science) | Lab | 2 | BI Tool Proficiency (e.g., Power BI/Tableau), Data Extraction and Transformation (ETL), Dashboard Creation, OLAP Cube Navigation, Reporting and Analytics, SQL for BI Queries |
