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B-SC in Data Science at V. P. & R. P. T. P. Science College, Vallabh Vidyanagar
Anand, Gujarat
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About the Specialization
What is Data Science at V. P. & R. P. T. P. Science College, Vallabh Vidyanagar Anand?
This B.Sc. Data Science program at V. P. & R. P. T. P. Science College, Anand focuses on equipping students with a robust foundation in data analysis, machine learning, and statistical modeling. Reflecting India''''s burgeoning data-driven economy, the curriculum emphasizes practical skills and theoretical knowledge essential for navigating complex datasets. It uniquely integrates mathematical principles with programming expertise, preparing graduates for diverse roles in the rapidly evolving Indian tech landscape.
Who Should Apply?
This program is ideal for high school graduates with a strong aptitude for mathematics and an interest in technology and problem-solving. It caters to freshers aspiring for entry-level positions in data analytics, business intelligence, or machine learning engineering. Furthermore, individuals looking to transition into the data science domain or gain a formal academic qualification in this highly demanded field will find this course beneficial, especially those with a science background.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding careers in India, including Data Analyst, Junior Data Scientist, Business Intelligence Developer, or Machine Learning Engineer. Entry-level salaries typically range from INR 3-6 LPA, with significant growth potential as experience accrues in leading Indian IT firms and startups. The curriculum aligns with industry-recognized certifications, enhancing employability and fostering a strong foundation for advanced studies.
Student Success Practices
Foundation Stage
Master Programming Fundamentals and Logic- (Semester 1-2)
Dedicate consistent time to practice Python programming, focusing on core concepts, data structures, and basic algorithms. Utilize online coding platforms to solve problems regularly and enhance problem-solving skills.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation, NPTEL courses on programming
Career Connection
Strong programming skills are non-negotiable for all data science roles, forming the bedrock for data manipulation, analysis, and model development in Indian tech companies.
Build a Solid Mathematical & Statistical Base- (Semester 1-2)
Actively engage with the applied mathematics and statistics courses. Practice problem-solving from textbooks and online resources, ensuring a deep understanding of concepts like linear algebra, calculus, probability, and inferential statistics.
Tools & Resources
Khan Academy, NPTEL lectures, Specific textbooks for probability and statistics, Online calculators for statistical tests
Career Connection
A robust quantitative foundation is crucial for understanding and developing complex data science models, highly valued by analytics firms in India, enabling deeper insights.
Participate in Peer Learning and Study Groups- (Semester 1-2)
Form or join study groups to discuss concepts, clarify doubts, and collaboratively solve programming assignments and mathematical problems. Teaching concepts to peers solidifies your own understanding and improves communication skills.
Tools & Resources
College library, WhatsApp groups for academic discussion, Google Meet for virtual study sessions, Whiteboards for collaborative problem solving
Career Connection
Enhances communication and teamwork skills, essential for collaborative projects in the Indian IT industry, and helps in understanding diverse perspectives and problem-solving approaches.
Intermediate Stage
Engage in Practical Data Science Projects- (Semester 3-5)
Beyond coursework, undertake personal data analysis and machine learning projects using publicly available datasets. Focus on end-to-end project development, from data cleaning and exploration to model building and evaluation.
Tools & Resources
Kaggle, UCI Machine Learning Repository, Google Colab, Jupyter Notebooks, GitHub for version control
Career Connection
Practical projects build a portfolio, demonstrating problem-solving abilities and hands-on experience, significantly boosting employability for internships and entry-level jobs in India.
Explore Industry-Relevant Tools and Technologies- (Semester 3-5)
Proactively learn and gain proficiency in industry-standard tools not explicitly covered in depth by the curriculum, such as Tableau/Power BI for visualization, SQL for advanced querying, and basic cloud platforms (AWS/Azure/GCP).
Tools & Resources
Coursera, Udemy, DataCamp, Official documentation of tools, Free tiers of cloud platforms
Career Connection
Familiarity with diverse tools makes graduates more adaptable and attractive to Indian companies that use a variety of technologies, reducing training time and increasing immediate value.
Network and Attend Webinars/Workshops- (Semester 3-5)
Attend industry webinars, workshops, and local meetups organized by data science communities. Connect with professionals on LinkedIn to gain insights into industry trends and potential career opportunities and mentorship.
Tools & Resources
LinkedIn, Eventbrite for local events, College career fair events, Online communities like PyData or Data Science India
Career Connection
Builds a professional network, exposes students to real-world applications, and can lead to mentorships or internship recommendations within the Indian data science ecosystem.
Advanced Stage
Master Model Deployment and MLOps Concepts- (Semester 6)
Focus on understanding the lifecycle of machine learning models from development to deployment and maintenance. Gain hands-on experience with tools like Docker, Flask/FastAPI for building APIs, and cloud services for model serving.
Tools & Resources
Docker tutorials, Kubernetes documentation, AWS SageMaker/Azure ML free tiers, Flask/FastAPI documentation and examples, Online MLOps courses
Career Connection
Proficiency in MLOps and deployment is highly sought after in India, enabling graduates to bridge the gap between model development and production in companies, making them job-ready.
Undertake an Intensive Capstone Project and Internship- (Semester 6)
Dedicate significant effort to a comprehensive capstone project, ideally solving a real-world problem or participating in an industry internship. Focus on documenting the process, results, and learning outcomes thoroughly.
Tools & Resources
Company projects during internship, University research labs, GitHub for project repository, Project management tools like Trello or Jira, Mentors from industry or academia
Career Connection
A strong final project or internship experience is paramount for placements in India, showcasing independent work, problem-solving, and practical application of knowledge to employers.
Prepare for Placements and Professional Interviews- (Semester 6)
Start preparing for technical interviews by practicing coding challenges, revising core data science concepts, and developing strong communication skills for behavioral questions. Participate in mock interviews and group discussions.
Tools & Resources
LeetCode for coding challenges, InterviewBit for interview questions, GeeksforGeeks interview sections, Mock interview platforms, College career services for guidance
Career Connection
Systematic preparation directly translates to successful placements in top Indian tech companies, securing roles as Junior Data Scientists or ML Engineers and ensuring a smooth career start.
Program Structure and Curriculum
Eligibility:
- A candidate seeking admission to B.Sc. Programme must have passed Higher Secondary School Certificate Examination (Std. XII Science Stream) with English, Physics, Chemistry and Mathematics/Biology/Computer Science/Statistics/Geology/Geography as subjects conducted by Gujarat Secondary and Higher Secondary Education Board, Gandhinagar or an Examination recognized as equivalent thereto.
Duration: 3 years / 6 semesters
Credits: 150 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS101 | Introduction to Data Science | Core | 4 | Data Science Concepts, Types of Data, Data Pre-processing, Data Visualization, Introduction to Data Science Tools |
| DS102 | Python Programming for Data Science | Core | 4 | Python Fundamentals, Data Structures in Python, Functions and Modules, File I/O, Introduction to Pandas and NumPy |
| DS103 | Applied Mathematics for Data Science | Core | 4 | Linear Algebra Basics, Calculus Fundamentals, Probability Theory, Descriptive Statistics, Optimization Techniques |
| DS104 | Database Management System | Core | 4 | Relational Model Concepts, SQL Queries, ER Model, Normalization, Transaction Management |
| DS105 | Practical based on DS102 | Practical | 2 | Python Programming Exercises, Data Manipulation using Pandas, NumPy Array Operations, Function Implementation, Module Usage |
| DS106 | Practical based on DS104 | Practical | 2 | SQL Data Definition Language, SQL Data Manipulation Language, Database Design, Query Optimization, Stored Procedures |
| DS107 | Communication Skills | Ability Enhancement Compulsory Course (AEC) | 2 | Verbal Communication, Non-Verbal Communication, Public Speaking, Presentation Skills, Interpersonal Skills |
| DS108 | Digital Literacy | Value Added Course (VAC) | 2 | Computer Basics, Internet Fundamentals, Office Software Applications, Digital Security and Ethics, E-governance |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS201 | Statistics for Data Science | Core | 4 | Descriptive Statistics, Inferential Statistics, Hypothesis Testing, Correlation and Regression, ANOVA |
| DS202 | Data Structures and Algorithms | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| DS203 | Machine Learning Fundamentals | Core | 4 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Model Evaluation Metrics |
| DS204 | Web Technology for Data Science | Core | 4 | HTML and CSS, JavaScript Basics, Web Servers and APIs, Flask/Django Framework Basics, Data Scraping Fundamentals |
| DS205 | Practical based on DS202 | Practical | 2 | Implementing Data Structures, Algorithm Analysis, Sorting and Searching Practice, Linked List Operations, Tree Traversal |
| DS206 | Practical based on DS203 | Practical | 2 | Implementing Regression Models, Implementing Classification Algorithms, Clustering Techniques, Model Hyperparameter Tuning, Data Splitting and Cross-validation |
| DS207 | Environmental Studies | Ability Enhancement Compulsory Course (AEC) | 2 | Ecology and Ecosystems, Biodiversity and Conservation, Environmental Pollution, Climate Change Impacts, Sustainable Development |
| DS208 | Indian Constitution | Value Added Course (VAC) | 2 | Preamble and Basic Structure, Fundamental Rights, Directive Principles of State Policy, Union and State Government, Constitutional Amendments |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS301 | Advanced Statistical Methods for Data Science | Core | 4 | Multivariate Analysis, Time Series Analysis, Sampling Techniques, Non-parametric Statistical Tests, Factor Analysis |
| DS302 | Advanced Machine Learning | Core | 4 | Deep Learning Basics, Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transfer Learning |
| DS303 | Big Data Technologies | Core | 4 | Hadoop Ecosystem, HDFS and MapReduce, Apache Spark, Hive and Pig, NoSQL Databases |
| DS304 | Data Visualization and Storytelling | Core | 4 | Principles of Data Visualization, Data Visualization Tools (Tableau/Power BI), Interactive Dashboards, Infographics, Effective Data Storytelling |
| DS305 | Practical based on DS302 | Practical | 2 | Implementing Neural Networks with Keras/TensorFlow, CNN for Image Classification, RNN for Sequence Data, Transfer Learning Applications, Deep Learning Model Tuning |
| DS306 | Practical based on DS303 | Practical | 2 | Hadoop HDFS Operations, MapReduce Programming, Spark Data Processing, Hive Query Language, NoSQL Database Operations |
| DS307 | Open Source Technologies | Skill Enhancement Course (SEC) | 2 | Introduction to Linux, Version Control with Git, Open Source Software Development, Open Source Licensing, Contributing to Open Source Projects |
| DS308 | Entrepreneurship Development | Value Added Course (VAC) | 2 | Entrepreneurial Mindset, Business Idea Generation, Business Plan Development, Startup Funding, Marketing and Sales for Startups |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS401 | Cloud Computing for Data Science | Core | 4 | Cloud Service Models (IaaS, PaaS, SaaS), Major Cloud Providers (AWS, Azure, GCP), Cloud Storage Solutions, Cloud Data Processing Services, Cloud Security Fundamentals |
| DS402 | Natural Language Processing | Core | 4 | Text Pre-processing Techniques, Tokenization and Stemming, Word Embeddings (Word2Vec, GloVe), Sentiment Analysis, Text Generation Models |
| DS403 | Business Intelligence and Data Warehousing | Core | 4 | Data Warehousing Concepts, ETL Process, OLAP and Data Cubes, Data Marts, Business Reporting Tools |
| DS404 | Internet of Things (IoT) for Data Science | Core | 4 | IoT Architecture, Sensors and Actuators, Data Acquisition from IoT Devices, IoT Data Analytics, Edge Computing Basics |
| DS405 | Practical based on DS401 | Practical | 2 | Cloud Storage Services (S3, Blob Storage), Virtual Machine Deployment, Serverless Computing (Lambda, Azure Functions), Cloud Networking Basics, Database Services in Cloud |
| DS406 | Practical based on DS402 | Practical | 2 | NLP Libraries (NLTK, SpaCy), Text Classification, Named Entity Recognition, Topic Modeling, Chatbot Development |
| DS407 | Data Ethics and Privacy | Skill Enhancement Course (SEC) | 2 | Ethical AI Principles, Data Governance, Privacy Regulations (GDPR, DPDP Bill), Bias in AI Systems, Data Security Practices |
| DS408 | Research Methodology | Value Added Course (VAC) | 2 | Research Design, Data Collection Methods, Quantitative and Qualitative Analysis, Report Writing and Presentation, Research Ethics |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS501 | Data Mining Techniques | Core | 4 | Association Rule Mining, Classification Algorithms, Clustering Techniques, Anomaly Detection, Data Mining Applications |
| DS502 | Computer Vision | Core | 4 | Image Processing Fundamentals, Feature Extraction, Object Detection, Image Segmentation, Convolutional Neural Networks for Vision |
| DS503 | Optimization Techniques for Data Science | Core | 4 | Linear Programming, Non-linear Programming, Heuristic Algorithms, Metaheuristic Optimization, Gradient Descent Variations |
| DS504A | Elective I: Time Series Analysis | Discipline Specific Elective (DSE) | 4 | Time Series Components, ARIMA Models, SARIMA Models, Time Series Forecasting, GARCH Models |
| DS504B | Elective I: Reinforcement Learning | Discipline Specific Elective (DSE) | 4 | Markov Decision Process, Q-Learning, Policy Gradients, Deep Reinforcement Learning, Exploration vs. Exploitation |
| DS505 | Practical based on DS501 | Practical | 2 | Implementing Data Mining Algorithms, Using WEKA/Scikit-learn for Data Mining, Market Basket Analysis, Clustering for Customer Segmentation, Fraud Detection |
| DS506 | Practical based on DS502 | Practical | 2 | Image Filtering and Enhancement, Object Detection using YOLO/Faster R-CNN, Image Segmentation with U-Net, Facial Recognition, OpenCV Library Usage |
| DS507 | Elective Practical based on DS504A/B | Practical | 2 | Time Series Forecasting Implementation, Reinforcement Learning Environments, Hyperparameter Tuning for RL Agents, ARIMA Model Building, Q-Learning Implementation |
| DS508 | Project-I | Core | 4 | Project Design and Planning, Literature Review, Data Collection and Analysis, Model Development and Evaluation, Technical Report Writing |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS601 | Generative AI | Core | 4 | Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, Large Language Models (LLMs), Transformer Architecture |
| DS602 | Deployment of ML Models | Core | 4 | MLOps Principles, Model Serving Frameworks, Containerization with Docker, Orchestration with Kubernetes, Building APIs for ML Models |
| DS603 | Ethical AI and Responsible Data Science | Core | 4 | Bias and Fairness in AI, Transparency and Explainability (XAI), Accountability in AI Systems, Data Privacy and Security in AI, Ethical Frameworks for AI Development |
| DS604A | Elective II: Geospatial Data Analysis | Discipline Specific Elective (DSE) | 4 | Geographic Information Systems (GIS), Satellite Imagery Analysis, Spatial Statistics, Geospatial Data Visualization, QGIS/ArcGIS Basics |
| DS604B | Elective II: Financial Analytics | Discipline Specific Elective (DSE) | 4 | Financial Market Data Analysis, Risk Modeling, Algorithmic Trading Strategies, Portfolio Optimization, Econometric Models |
| DS605 | Practical based on DS601 | Practical | 2 | Implementing GANs for Image Generation, Building Simple LLMs, Fine-tuning Pre-trained Transformers, VAE for Data Generation, Exploring Diffusion Models |
| DS606 | Practical based on DS602 | Practical | 2 | Deploying ML Models with Flask/FastAPI, Containerizing Applications with Docker, Model Monitoring and Logging, Version Control for ML Models, Setting up CI/CD for ML |
| DS607 | Elective Practical based on DS604A/B | Practical | 2 | Geospatial Data Manipulation in Python, Financial Time Series Analysis, Building Risk Models, Creating Thematic Maps, Simulating Trading Strategies |
| DS608 | Project-II | Core | 6 | Advanced Project Implementation, Problem Identification and Scope Definition, Solution Design and Development, Testing and Validation, Final Project Presentation and Documentation |
