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B-VOC-DATA-SCIENCE in General at Guru Nanak Khalsa Institute of Technology and Management
Yamunanagar, Haryana
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About the Specialization
What is General at Guru Nanak Khalsa Institute of Technology and Management Yamunanagar?
This B.Voc. (Data Science) program at Guru Nanak Khalsa Institute of Technology and Management focuses on equipping students with essential skills in data analysis, machine learning, and big data technologies, highly relevant for the burgeoning Indian digital economy. The program emphasizes hands-on practical exposure to real-world data challenges, preparing graduates for immediate impact in diverse industry sectors across India. It blends theoretical knowledge with application-oriented learning, reflecting the industry''''s demand for job-ready data professionals.
Who Should Apply?
This program is ideal for 10+2 graduates with an aptitude for mathematics and logical reasoning, seeking entry into the dynamic field of data science. It also caters to individuals looking to upskill or career changers from related technical backgrounds who wish to specialize in data-driven roles. Students with a keen interest in problem-solving using data, analytical thinking, and programming will find this curriculum highly beneficial. A basic understanding of computers is helpful but not strictly necessary for admission.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India as Data Analysts, Machine Learning Engineers, Business Intelligence Developers, or Data Scientists. Entry-level salaries typically range from INR 3-6 lakhs per annum, with experienced professionals earning significantly more. The program aligns with industry needs, fostering skills for roles in IT services, e-commerce, finance, and healthcare. Graduates will also be prepared for relevant industry certifications and higher studies in data science.
Student Success Practices
Foundation Stage
Master Programming & Math Fundamentals- (Semester 1-2)
Dedicate significant time to mastering C programming, Data Structures, OOP with C++, and foundational mathematics (Calculus, Algebra, Probability). Utilize online coding platforms to practice problem-solving daily. Form study groups to discuss complex topics and debug code collaboratively.
Tools & Resources
HackerRank, CodeChef, GeeksforGeeks, Khan Academy (for Math), college programming labs
Career Connection
Strong fundamentals are non-negotiable for data science roles; they build the logical thinking and problem-solving abilities critical for advanced topics and technical interviews.
Develop Strong Communication Skills- (Semester 1-2)
Actively participate in communication skills classes, focusing on both verbal and written aspects. Practice presenting technical concepts clearly and concisely. Join debating clubs or public speaking forums within the college to enhance confidence and articulation.
Tools & Resources
College communication labs, Toastmasters (if available), online resources for presentation tips
Career Connection
Data scientists need to explain complex insights to non-technical stakeholders; effective communication is crucial for career progression and leadership roles.
Build a Basic Web Development Portfolio- (Semester 1-2)
Apply Web Designing skills (HTML, CSS, JavaScript) to create simple, personal websites or small projects. This hands-on application solidifies understanding and provides a tangible output for early portfolios. Experiment with responsive design principles and user-friendly interfaces.
Tools & Resources
VS Code, CodePen, free hosting services (e.g., GitHub Pages)
Career Connection
Demonstrating practical application, even basic, shows initiative and technical aptitude, which can be a differentiator in early internships and entry-level jobs, reflecting a foundational understanding of front-end technologies.
Intermediate Stage
Excel in Database and Python Programming- (Semester 3-4)
Deep dive into SQL for Database Management Systems and Python for data manipulation and analysis. Work on mini-projects involving data extraction, transformation, and loading (ETL) using SQL and Python. Contribute to open-source projects or build small data-driven applications.
Tools & Resources
MySQL/PostgreSQL, Jupyter Notebooks, Pandas, NumPy, Kaggle datasets
Career Connection
Proficiency in SQL and Python is the bedrock for most data science roles; mastery here directly translates to higher chances of securing relevant internships and jobs in Indian tech companies.
Engage in Data Visualization & Mining Challenges- (Semester 3-4)
Participate in online data visualization challenges (e.g., MakeoverMonday) and apply data mining techniques to publicly available datasets. Explore tools beyond the curriculum like Tableau Public or Power BI for advanced visualization practice. Document findings and insights thoroughly.
Tools & Resources
Tableau Public, Power BI, Matplotlib, Seaborn, Kaggle competitions
Career Connection
The ability to extract meaningful insights and present them effectively is a highly valued skill, crucial for roles in business intelligence and data analysis within the Indian market, helping companies make data-driven decisions.
Seek First Internship/Live Project- (Semester 4-5)
Proactively search for internships or live projects, even unpaid, in local startups or NGOs. Focus on applying learned concepts in a real-world setting. Network with faculty and seniors for leads. Build a professional resume and LinkedIn profile highlighting practical skills and project work.
Tools & Resources
LinkedIn, Internshala, college placement cell, industry mentorship programs
Career Connection
Practical industry experience during the intermediate stage provides invaluable exposure, builds a professional network, and significantly boosts resume strength for future placements, offering a competitive edge in India''''s job market.
Advanced Stage
Build an Advanced Data Science Portfolio with Capstone Projects- (Semester 5-6)
Undertake comprehensive capstone projects (Project Work I & II) that integrate Machine Learning, Deep Learning, Big Data, and Cloud Computing concepts. Focus on end-to-end solutions, from data acquisition and preprocessing to model deployment. Document projects thoroughly on GitHub with clear explanations.
Tools & Resources
GitHub, Google Colab, AWS/Azure free tier, TensorFlow, Keras, PyTorch, Apache Spark
Career Connection
A robust portfolio showcasing complex projects is essential for demonstrating expertise to potential employers and standing out in competitive job markets for roles like ML Engineer or Data Scientist in India.
Specialize and Network Proactively- (Semester 5-6)
Identify a niche within data science (e.g., NLP, Computer Vision, Big Data Engineering) and pursue advanced online courses or certifications. Attend industry webinars, workshops, and career fairs (both online and offline) to network with professionals and understand current industry trends and requirements in India.
Tools & Resources
Coursera, edX, NPTEL, industry conferences (e.g., Data Science Congress), LinkedIn networking
Career Connection
Specialization makes you a more attractive candidate for specific roles, while networking opens doors to opportunities and mentorship that are often crucial for career advancement in the rapidly evolving Indian tech landscape.
Master Interview Skills and Placement Preparation- (Semester 6)
Practice technical interview questions related to data structures, algorithms, SQL, Python, machine learning concepts, and behavioral questions. Participate in mock interviews with peers and faculty. Research potential employers and tailor your resume and cover letter to specific job descriptions.
Tools & Resources
LeetCode, InterviewBit, GeeksforGeeks, mock interview platforms, college placement cell workshops
Career Connection
Excellent interview skills are paramount for converting academic knowledge and project experience into successful job offers, securing roles in leading tech companies and startups across India, ensuring a strong start to your career.
Program Structure and Curriculum
Eligibility:
- 10+2 with at least 50% marks in aggregate from Board of School Education Haryana or any other examination recognized by Kurukshetra University as equivalent thereto.
Duration: 6 semesters / 3 years
Credits: 146 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BVDAS-101 | Computer Fundamentals and Programming in C | Core | 6 | Computer Basics, Input/Output Devices, Memory Organization, Operating System Concepts, C Language Fundamentals, Data Types and Operators, Control Structures, Arrays and Strings, Functions |
| BVDAS-102 | Basic Mathematics | Core | 4 | Matrices and Determinants, Differential Calculus, Integral Calculus, Set Theory, Permutation & Combination, Probability |
| BVDAS-103 | Communication Skills | Core | 4 | Introduction to Communication, Types of Communication, Barriers to Communication, Verbal and Non-Verbal Communication, Listening Skills, Writing Skills, Presentation Skills |
| BVDAS-104 | Web Designing | Core | 6 | HTML Fundamentals, HTML Elements and Attributes, Cascading Style Sheets (CSS), JavaScript Introduction, DOM Manipulation, Event Handling, Form Validation |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BVDAS-201 | Data Structures | Core | 6 | Introduction to Data Structures, Arrays and Linked Lists, Stacks and Queues, Trees (Binary, AVL, B-trees), Graphs (Traversal, Shortest Path), Sorting Algorithms, Searching Algorithms |
| BVDAS-202 | Object-Oriented Programming using C++ | Core | 6 | OOP Concepts (Encapsulation, Inheritance, Polymorphism), Classes and Objects, Constructors and Destructors, Operator Overloading, Virtual Functions, Templates, Exception Handling |
| BVDAS-203 | Operating System | Core | 4 | OS Introduction and Types, Process Management, CPU Scheduling, Deadlocks, Memory Management, Virtual Memory, File Systems, I/O Management |
| BVDAS-204 | Digital Electronics | Core | 4 | Number Systems, Boolean Algebra and Logic Gates, Combinational Circuits, Sequential Circuits, Registers and Counters, ADC and DAC Converters |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BVDAS-301 | Database Management System | Core | 6 | DBMS Concepts, Data Models (ER, Relational), Relational Algebra, SQL Queries, Normalization, Transaction Management, Concurrency Control |
| BVDAS-302 | Python Programming | Core | 6 | Python Basics and Data Types, Control Flow and Loops, Functions and Modules, File I/O, Object-Oriented Programming in Python, Data Structures in Python (Lists, Tuples, Dictionaries), Introduction to Libraries (NumPy, Pandas) |
| BVDAS-303 | Computer Networks | Core | 4 | Network Models (OSI, TCP/IP), Physical Layer, Data Link Layer, Network Layer (IP, Routing), Transport Layer (TCP, UDP), Application Layer Protocols, Network Security Basics |
| BVDAS-304 | Data Visualization | Core | 6 | Introduction to Data Visualization, Data Types for Visualization, Visual Encoding Techniques, Common Chart Types, Data Visualization Tools (Matplotlib, Seaborn), Storytelling with Data |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BVDAS-401 | Data Warehousing and Data Mining | Core | 4 | Data Warehousing Concepts, OLAP Operations, Data Preprocessing, Data Mining Techniques, Association Rule Mining, Classification, Clustering, Prediction Models |
| BVDAS-402 | Probability and Statistics | Core | 4 | Probability Theory, Random Variables, Probability Distributions (Binomial, Poisson, Normal), Sampling Techniques, Hypothesis Testing, Correlation Analysis, Regression Analysis |
| BVDAS-403 | Machine Learning | Core | 6 | Introduction to Machine Learning, Supervised Learning, Unsupervised Learning, Regression Algorithms, Classification Algorithms (SVM, Decision Trees), Clustering Algorithms (K-Means), Model Evaluation Metrics |
| BVDAS-404 | Artificial Intelligence | Core | 4 | AI Introduction and History, Intelligent Agents, Problem Solving by Searching, Knowledge Representation, Expert Systems, Natural Language Processing Basics, Machine Learning Overview |
| BVDAS-405 | Industrial Training-I | Practical | 4 | Practical Application of Concepts, Industry Exposure, Problem Solving, Project Implementation, Report Writing |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BVDAS-501 | Cloud Computing | Core | 4 | Cloud Computing Concepts, Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization Technology, Cloud Security, Introduction to Cloud Platforms (AWS, Azure) |
| BVDAS-502 | Big Data Technologies | Core | 6 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Apache Spark, NoSQL Databases (MongoDB, Cassandra), Data Stream Processing, Big Data Analytics |
| BVDAS-503 | IoT | Core | 6 | IoT Architecture, Sensors and Actuators, Communication Protocols (MQTT, CoAP), IoT Platforms, Data Analytics in IoT, IoT Security Challenges |
| BVDAS-504 | Mobile Application Development | Core | 6 | Mobile OS (Android/iOS) Overview, Development Environment Setup, UI/UX Design Principles, Activity Lifecycle, Layouts and Widgets, Data Storage Options, API Integration |
| BVDAS-505 | Project Work – I | Project | 6 | Problem Identification, System Design, Implementation, Testing and Debugging, Documentation and Presentation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BVDAS-601 | Ethical Hacking and Cyber Security | Core | 6 | Cyber Security Fundamentals, Threats and Vulnerabilities, Ethical Hacking Concepts, Network Security, Web Application Security, Cryptography Basics, Digital Forensics |
| BVDAS-602 | Deep Learning | Core | 6 | Introduction to Deep Learning, Artificial Neural Networks, Backpropagation Algorithm, Activation Functions, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transfer Learning |
| BVDAS-603 | Entrepreneurship Development | Core | 4 | Entrepreneurship Concepts, Business Plan Development, Market Research, Sources of Funding, Legal and Ethical Aspects, Marketing Strategies, Innovation Management |
| BVDAS-604 | Block Chain Technologies | Core | 4 | Blockchain Fundamentals, Cryptography in Blockchain, Distributed Ledger Technology, Consensus Mechanisms, Smart Contracts, Use Cases of Blockchain, Bitcoin and Ethereum Overview |
| BVDAS-605 | Project Work – II | Project | 6 | Advanced Problem Solving, Full-stack Development, Deployment Strategies, Performance Optimization, Research and Innovation, Final Documentation and Viva |
