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B-TECH in Data Science at Vignana Bharathi Institute of Technology
Medchal-Malkajgiri, Telangana
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About the Specialization
What is Data Science at Vignana Bharathi Institute of Technology Medchal-Malkajgiri?
This Data Science program at Vignana Bharathi Institute of Technology focuses on equipping students with the theoretical and practical knowledge required to extract insights from vast datasets. It is tailored to address the burgeoning demand for data professionals in the Indian and global markets, emphasizing statistical foundations, machine learning algorithms, and big data technologies. The curriculum combines core computer science principles with specialized data science techniques, preparing graduates for cutting-edge roles.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics, statistics, and programming who aspire to build a career in the data-driven industry. It also caters to working professionals seeking to upskill or transition into data science, as well as career changers aiming to leverage analytical skills in various sectors. Prerequisites include a foundational understanding of programming and strong problem-solving abilities.
Why Choose This Course?
Graduates of this program can expect to pursue India-specific career paths such as Data Scientist, Machine Learning Engineer, Data Analyst, Business Intelligence Developer, or Big Data Engineer. Entry-level salaries typically range from INR 4-8 lakhs per annum, with experienced professionals earning upwards of INR 15-30 lakhs, depending on skills and company. The program aligns with industry demand, fostering growth trajectories in analytics, AI, and cloud-based data solutions.
Student Success Practices
Foundation Stage
Master Programming Fundamentals (Python/C)- (Semester 1-2)
Dedicate significant time to thoroughly understand C and Python programming languages, focusing on data structures and object-oriented concepts. Practice coding daily on platforms like HackerRank or LeetCode to build logical thinking and problem-solving skills.
Tools & Resources
GeeksforGeeks, HackerRank, LeetCode, Python.org Documentation, NPTEL courses on Data Structures
Career Connection
A strong coding foundation is indispensable for all data science roles and is a primary filter in campus placements and technical interviews.
Build Strong Mathematical & Statistical Foundations- (Semester 1-3)
Pay close attention to Engineering Mathematics I, II, and Probability & Statistics. Reinforce concepts like Linear Algebra, Calculus, and Statistical Inference through extra practice problems and online tutorials, as these are critical for understanding advanced ML algorithms.
Tools & Resources
Khan Academy, MIT OpenCourseWare (Linear Algebra), StatQuest with Josh Starmer (YouTube), NCERT Maths Books
Career Connection
Understanding the mathematical underpinnings of algorithms differentiates strong candidates and is vital for research or advanced analytics roles.
Engage in Peer Learning and Technical Clubs- (Semester 1-3)
Join VBIT''''s technical clubs related to AI/Data Science. Actively participate in hackathons, coding competitions, and peer study groups. This helps clarify doubts, expose you to different problem-solving approaches, and develop teamwork skills.
Tools & Resources
VBIT Data Science Club, Google Developer Student Clubs (GDSC), Meetup groups for local tech events
Career Connection
Networking and collaborative skills gained here are highly valued by employers, and participation builds a strong co-curricular profile.
Intermediate Stage
Undertake Practical Data Science Projects- (Semester 3-5)
Start working on mini-projects using real-world datasets from platforms like Kaggle. Focus on the entire data science lifecycle: data collection, cleaning, EDA, modeling, and visualization. Document your work thoroughly on GitHub.
Tools & Resources
Kaggle, GitHub, Jupyter Notebook, Python libraries (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn)
Career Connection
A portfolio of well-documented projects is crucial for demonstrating practical skills and significantly boosts your resume for internships and placements.
Explore Industry Tools and Technologies- (Semester 4-6)
Beyond classroom learning, get hands-on experience with industry-standard tools for Big Data (Hadoop, Spark), Cloud Platforms (AWS, Azure, GCP), and specialized ML/DL frameworks (TensorFlow, PyTorch). Pursue relevant certifications.
Tools & Resources
Coursera/Udemy courses (with VBIT-provided free access if available), AWS Educate, Google Cloud Skills Boost, official documentation
Career Connection
Proficiency in these tools makes you job-ready and highly attractive to companies looking for immediate contributions.
Seek Early Internships and Mentorship- (Semester 4-6)
Actively search for internships during summer breaks, even if unpaid initially, to gain exposure to industry practices and corporate culture. Connect with alumni and industry professionals on LinkedIn for mentorship and guidance.
Tools & Resources
LinkedIn, Internshala, VBIT Placement Cell, Alumni Network
Career Connection
Internships provide invaluable experience, often lead to pre-placement offers, and build a professional network vital for future career growth.
Advanced Stage
Specialize and Build a Capstone Project- (Semester 6-8)
Deep dive into a specific area of Data Science (e.g., NLP, Computer Vision, Reinforcement Learning) through professional electives. Develop a comprehensive, innovative capstone project that showcases your specialized skills, potentially collaborating with faculty for research papers.
Tools & Resources
Advanced Python/R libraries, GPU resources (Google Colab Pro), Research papers (arXiv, IEEE Xplore), Academic Mentors
Career Connection
A strong capstone project demonstrates expertise, problem-solving capability, and can be a highlight during technical interviews, particularly for niche roles.
Intensive Placement Preparation- (Semester 7-8)
Engage in rigorous aptitude test practice, mock technical interviews, and HR interview simulations. Refine your resume and LinkedIn profile to highlight projects, skills, and internships. Attend workshops organized by the placement cell focused on interview strategies.
Tools & Resources
VBIT Placement Cell resources, Online aptitude tests, Mock interview platforms, InterviewBit
Career Connection
Thorough preparation is paramount for converting placement opportunities into job offers in competitive Indian campus recruitment drives.
Network Professionally and Stay Updated- (Semester 6-8)
Regularly attend webinars, industry conferences (virtual or local), and tech meetups. Subscribe to data science newsletters and follow thought leaders. This helps stay abreast of emerging trends, tools, and job market demands in India''''s rapidly evolving tech landscape.
Tools & Resources
LinkedIn Learning, Medium blogs (Towards Data Science), YouTube channels (StatQuest, sentdex), Online conferences
Career Connection
Continuous learning and networking are vital for long-term career success, opening doors to new opportunities and fostering professional development.
Program Structure and Curriculum
Eligibility:
- 10+2 with Physics, Chemistry, and Mathematics, and a valid rank in TS-EAMCET/JEE Mains/ECET or equivalent entrance examination.
Duration: 4 years (8 semesters)
Credits: 148 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MA101BS | Engineering Mathematics-I (Calculus and Differential Equations) | Core | 4 | Differential Calculus, Integral Calculus, Multiple Integrals, Vector Calculus, First Order Differential Equations, Higher Order Linear Differential Equations |
| 22AP102BS | Applied Physics | Core | 4 | Quantum Mechanics, Semiconductor Physics, Lasers and Fiber Optics, Electromagnetism, Dielectric Properties, Superconductivity |
| 22CS103PC | Programming for Problem Solving | Core | 3 | Introduction to C Programming, Control Structures, Functions and Arrays, Pointers and Strings, Structures and Unions, File Handling |
| 22ME104ES | Engineering Graphics & Design | Core | 3 | Orthographic Projections, Isometric Projections, Projection of Points and Lines, Projection of Planes and Solids, Sectional Views, Introduction to CAD |
| 22EN105HS | English for Skill Enhancement | Core | 2 | Listening and Speaking Skills, Reading and Writing Skills, Grammar and Vocabulary, Technical Communication, Paragraph and Essay Writing, Report Writing |
| 22AP106BS | Applied Physics Lab | Lab | 1.5 | RC Circuit, LED Characteristics, Photoelectric Effect, LASER Diffraction, Optical Fiber NA, Hall Effect |
| 22CS107PC | Programming for Problem Solving Lab | Lab | 1.5 | Conditional Statements, Looping Constructs, Arrays and Functions, Pointers and Strings, Structures and Files, Basic Algorithm Implementation |
| 22EN108HS | English Language & Communication Skills Lab | Lab | 1 | JAM Sessions, Role Play, Group Discussions, Presentations, Public Speaking, Interview Skills |
| 22ME109ES | Basic Civil & Mechanical Engineering Workshop | Lab | 1.5 | Carpentry, Fitting, Tin Smithy, Welding, Black Smithy, Plumbing |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MA201BS | Engineering Mathematics-II (Linear Algebra and Vector Calculus) | Core | 4 | Matrices and Eigenvalues, Vector Spaces, Linear Transformations, Multivariable Calculus, Partial Differential Equations, Fourier Series and Transforms |
| 22CH202BS | Engineering Chemistry | Core | 4 | Water Technology, Electrochemistry and Corrosion, Polymers and Composites, Spectroscopic Techniques, Energy Sources, Nano Materials |
| 22CS203PC | Data Structures | Core | 3 | Arrays and Pointers, Linked Lists, Stacks and Queues, Trees and Binary Search Trees, Graphs and Graph Traversal, Searching and Sorting Algorithms |
| 22EE204ES | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Transformers, DC Machines, AC Machines, Electrical Safety |
| 22CH205BS | Engineering Chemistry Lab | Lab | 1.5 | Water Analysis, Titrations, Conductivity Meter, Viscosity Measurement, pH Meter, Synthesis of Polymers |
| 22CS206PC | Data Structures Lab | Lab | 1.5 | Linked List Operations, Stack and Queue Implementation, Tree Traversals, Graph Algorithms, Sorting Algorithms, Hashing Techniques |
| 22EE207ES | Basic Electrical Engineering Lab | Lab | 1 | Ohm''''s Law Verification, Kirchhoff''''s Laws, Thevenin''''s Theorem, Norton''''s Theorem, Single Phase AC Circuits, Three Phase Circuits |
| 22CS208PC | Computer Engineering Workshop | Lab | 1.5 | Linux Commands, Shell Scripting, Version Control (Git), Latex for Documentation, Basic Networking Concepts, Introduction to Web Technologies |
| 22ME209ES | Elements of Mechanical Engineering | Core | 2 | Thermodynamics Basics, Heat Transfer Modes, Fluid Mechanics, Power Plants, IC Engines, Refrigeration & Air Conditioning |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MA301BS | Engineering Mathematics-III (Probability and Statistics) | Core | 4 | Random Variables, Probability Distributions, Joint Probability, Sampling Distributions, Estimation Theory, Hypothesis Testing |
| 22CS302PC | Discrete Mathematics | Core | 3 | Set Theory, Mathematical Logic, Relations and Functions, Counting Principles, Graph Theory, Algebraic Structures |
| 22CS303PC | Database Management Systems | Core | 3 | DBMS Introduction, ER Model, Relational Model, SQL Queries, Normalization, Transaction Management |
| 22DS304PC | Object Oriented Programming using Python | Core | 3 | Python Fundamentals, OOP Concepts, Classes and Objects, Inheritance and Polymorphism, Exception Handling, File I/O |
| 22DS305PC | Data Communication and Computer Networks | Core | 3 | Network Models (OSI, TCP/IP), Physical Layer Concepts, Data Link Layer Protocols, Network Layer (IP, Routing), Transport Layer (TCP, UDP), Application Layer Protocols |
| 22CS306PC | Database Management Systems Lab | Lab | 1 | DDL Commands, DML Commands, Join Operations, Views and Sequences, Stored Procedures, Triggers |
| 22DS307PC | Object Oriented Programming using Python Lab | Lab | 1 | OOP Implementations, Class and Object Creation, Inheritance Example, Polymorphism Demonstration, File Operations, Database Connectivity |
| 22CS308PC | IT Workshop | Lab | 1 | MS Office Tools, Web Search Techniques, Email Etiquette, Operating System Customization, Presentation Skills, Device Connectivity |
| 22AC309MC | Environmental Science | Mandatory Non-Credit | 0 | Ecosystems, Biodiversity, Pollution Control, Natural Resources, Sustainable Development, Environmental Policies |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22SM401HS | Business Economics & Financial Analysis | Core | 3 | Introduction to Economics, Demand and Supply Analysis, Production and Cost Analysis, Market Structures, Capital Budgeting, Financial Statement Analysis |
| 22CS402PC | Design and Analysis of Algorithms | Core | 3 | Algorithm Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms, Backtracking and Branch & Bound |
| 22DS403PC | Operating Systems | Core | 3 | OS Concepts, Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems |
| 22DS404PC | Artificial Intelligence | Core | 3 | Introduction to AI, Problem Solving Agents, Search Algorithms, Knowledge Representation, Machine Learning Basics, Expert Systems |
| 22DS405PC | Introduction to Data Science | Core | 3 | Data Science Lifecycle, Data Collection and Cleaning, Exploratory Data Analysis, Data Visualization, Statistical Methods, Introduction to R/Python |
| 22CS406PC | Operating Systems Lab | Lab | 1 | Shell Scripting, Process Creation, CPU Scheduling Algorithms, Deadlock Avoidance, Memory Allocation, File System Operations |
| 22DS407PC | Artificial Intelligence Lab | Lab | 1 | BFS and DFS, Heuristic Search, Constraint Satisfaction, Game Playing, Knowledge Representation, Prolog/LISP Basics |
| 22DS408PC | Data Science with Python Lab | Lab | 1 | NumPy and Pandas, Matplotlib and Seaborn, Data Preprocessing, Feature Engineering, Basic Regression Models, Classification Models |
| 22AC409MC | Constitution of India | Mandatory Non-Credit | 0 | Preamble and Fundamental Rights, Directive Principles of State Policy, Union and State Governments, Judiciary and Emergency Provisions, Constitutional Amendments, Local Self-Government |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS501PC | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Regression Algorithms, Classification Algorithms, Clustering Techniques |
| 22DS502PC | Big Data Analytics | Core | 3 | Introduction to Big Data, Hadoop Ecosystem, HDFS and MapReduce, Pig and Hive, Apache Spark, Data Streaming |
| 22DS503PE | Professional Elective-I | Elective | 3 | Web Technologies, Software Engineering, Information Retrieval |
| 22DS504OE | Open Elective-I | Elective | 3 | Economics for Engineers, Operations Research, Cyber Security Essentials |
| 22DS505PC | Machine Learning Lab | Lab | 1.5 | Linear Regression, Logistic Regression, Decision Trees, SVM Implementation, K-Means Clustering, Model Evaluation Metrics |
| 22DS506PC | Big Data Analytics Lab | Lab | 1.5 | HDFS Commands, MapReduce Programming, Pig Scripting, Hive Query Language, Spark RDD Operations, Data Stream Processing |
| 22DS507PW | Data Science Project-I | Project | 2 | Problem Identification, Data Collection and Preprocessing, Exploratory Data Analysis, Model Development, Results Interpretation, Project Reporting |
| 22DS508PW | Advanced English Communication Skills Lab (AECSL) | Lab | 1 | Advanced Presentations, Technical Report Writing, Group Discussions, Interview Skills, Negotiation Techniques, Cross-cultural Communication |
| 22DS509HS | Professional Practice, Law & Ethics | Core | 2 | Professional Ethics, Cyber Law, Intellectual Property Rights, IT Act 2000, Corporate Governance, Sustainability and CSR |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS601PC | Deep Learning | Core | 3 | Neural Network Architecture, Backpropagation, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), LSTMs and GRUs, Deep Learning Frameworks |
| 22DS602PC | Data Warehousing and Mining | Core | 3 | Data Warehousing Concepts, OLAP Operations, ETL Processes, Data Preprocessing, Classification Algorithms, Association Rule Mining |
| 22DS603PE | Professional Elective-II | Elective | 3 | Computer Graphics, Compiler Design, Cloud Computing |
| 22DS604OE | Open Elective-II | Elective | 3 | Digital Marketing, Entrepreneurship Development, Renewable Energy Sources |
| 22DS605PC | Deep Learning Lab | Lab | 1.5 | Implementing Feedforward Networks, CNN for Image Classification, RNN for Sequence Data, Transfer Learning, Hyperparameter Tuning, TensorFlow/PyTorch Basics |
| 22DS606PC | Data Warehousing and Mining Lab | Lab | 1.5 | OLAP Operations, Data Cleaning, Data Transformation, Decision Tree Mining, Clustering with K-Means, Association Rule Generation |
| 22DS607PW | Industrial Oriented Mini Project / Internship | Project | 2 | Industry Problem Solving, Project Planning and Execution, Team Collaboration, Technical Report Writing, Presentation Skills, Real-world Application |
| 22DS608PW | Skill Development Course | Skill Based | 1 | Advanced Python/R, Cloud Platform Certification, DevOps Fundamentals |
| 22DS609MC | Essence of Indian Traditional Knowledge | Mandatory Non-Credit | 0 | Indian Philosophy, Yoga and Ayurveda, Indian Art and Architecture, Indian Scientific Heritage, Traditional Knowledge Systems, Relevance in Modern India |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS701PC | Reinforcement Learning | Core | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning, Q-Learning and SARSA, Policy Gradient Methods |
| 22DS702PE | Professional Elective-III | Elective | 3 | Natural Language Processing, Image and Video Processing, Exploratory Data Analysis |
| 22DS703PE | Professional Elective-IV | Elective | 3 | Distributed Systems, Pattern Recognition, Social Media Analytics |
| 22DS704OE | Open Elective-III | Elective | 3 | Intellectual Property Rights, Financial Management, Organizational Behavior |
| 22DS705PW | Project Stage-I | Project | 3 | Literature Survey, System Design, Module Implementation, Interim Report Preparation, Research Methodology, Problem Formulation |
| 22DS706PW | Seminar | Seminar | 1 | Technical Topic Selection, Literature Review, Presentation Skills, Question and Answer Session, Report Writing, Public Speaking |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS801PE | Professional Elective-V | Elective | 3 | Computer Vision, Text Analytics, Time Series Analysis |
| 22DS802OE | Open Elective-IV | Elective | 3 | Total Quality Management, Operations Management, E-commerce |
| 22DS803PW | Project Stage-II | Project | 8 | Advanced Implementation, System Integration, Comprehensive Testing, Performance Evaluation, Final Report Submission, Viva-Voce |
