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B-E in Data Science 60 Seats at Alva's Institute of Engineering and Technology
Dakshina Kannada, Karnataka
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About the Specialization
What is Data Science (60 seats) at Alva's Institute of Engineering and Technology Dakshina Kannada?
This Data Science program at Alva''''s Institute of Engineering and Technology focuses on equipping students with advanced analytical and computational skills. It addresses the burgeoning demand for data professionals in the Indian market, covering core concepts from statistics, machine learning, and big data technologies. The program emphasizes practical application and problem-solving relevant to various industry sectors.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics and programming seeking entry into the rapidly expanding data science and analytics field. It also caters to working professionals aiming to upskill in cutting-edge data technologies or career changers transitioning into data-driven roles across IT, finance, healthcare, and e-commerce sectors in India.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding India-specific career paths as Data Scientists, Machine Learning Engineers, Data Analysts, or Big Data Specialists. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly more. The curriculum aligns with requirements for certifications from platforms like IBM, Google, and AWS, enhancing growth trajectories in Indian and multinational companies.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Develop a strong foundation in C/Python programming, data structures, and algorithms. Actively solve coding problems online and participate in hackathons to reinforce logical thinking and problem-solving abilities crucial for data science.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, CodeChef, NPTEL courses on DSA
Career Connection
Essential for cracking technical interviews, building efficient data processing scripts, and understanding the computational backbone of data science algorithms.
Build a Strong Math & Statistics Base- (Semester 1-2)
Focus deeply on linear algebra, calculus, probability, and descriptive statistics. These mathematical pillars are fundamental to understanding how machine learning algorithms work and interpreting their results.
Tools & Resources
Khan Academy, NPTEL courses, Essence of Linear Algebra (3Blue1Brown), Textbooks like Sheldon Ross (Probability)
Career Connection
Crucial for comprehending model biases, statistical significance, and for advanced research roles in data science and machine learning.
Engage in Peer Learning & Group Projects- (Semester 1-2)
Form study groups with peers to discuss complex topics, share insights, and collaborate on small projects. Participating in academic competitions or building mini-projects as a team enhances practical skills and teamwork.
Tools & Resources
GitHub for collaborative coding, Discord/Slack for communication, Kaggle for starter datasets
Career Connection
Develops collaboration skills, crucial in team-oriented data science roles, and provides early exposure to project management.
Intermediate Stage
Hands-on Data Science Project Portfolio- (Semester 3-5)
Start building a portfolio of practical data science projects using Python (NumPy, Pandas, Matplotlib) and common ML libraries (Scikit-learn). Focus on end-to-end projects from data acquisition to model deployment.
Tools & Resources
Kaggle datasets, UCI Machine Learning Repository, Google Colab, Jupyter Notebooks
Career Connection
A strong project portfolio is vital for demonstrating practical skills to recruiters and securing internships/placements in data science roles.
Explore Electives and Specializations- (Semester 5-6)
Strategically choose professional and open electives like NLP, Business Intelligence, or IoT based on career interests. Deep dive into these areas to gain specialized knowledge and differentiate your skill set.
Tools & Resources
Online courses (Coursera, Udemy) for elective topics, Research papers, Industry blogs
Career Connection
Helps in specializing for specific data science domains (e.g., NLP Engineer, Computer Vision Engineer) which are high-demand in India.
Seek Industry Internships & Workshops- (Semester 4-5 breaks, or semester 5)
Actively pursue summer internships with Indian startups or established companies to gain real-world industry exposure. Attend workshops and seminars on emerging data science trends and tools.
Tools & Resources
LinkedIn, Internshala, College placement cell, Industry specific hackathons
Career Connection
Converts theoretical knowledge into practical skills, builds professional networks, and often leads to pre-placement offers.
Advanced Stage
Advanced Project & Research Contributions- (Semester 7-8)
Focus on your major project (Phase I & II) to solve complex, real-world problems. Consider contributing to open-source projects or writing a research paper in your area of specialization.
Tools & Resources
GitHub, Research databases (IEEE Xplore, ACM Digital Library), Mentorship from faculty
Career Connection
Demonstrates advanced problem-solving, research aptitude, and innovation, highly valued for senior roles or further academic pursuits.
Comprehensive Placement Preparation- (Semester 7-8)
Start early with rigorous interview preparation, focusing on data structures, algorithms, SQL, machine learning concepts, and soft skills. Practice mock interviews and aptitude tests.
Tools & Resources
Glassdoor, InterviewBit, Pramp (mock interviews), Company-specific prep materials
Career Connection
Maximizes chances of securing placements in top-tier companies, including MNCs with a strong presence in India and leading Indian tech firms.
Build a Professional Network & Brand- (Semester 6-8)
Attend industry conferences, connect with professionals on LinkedIn, and actively participate in data science communities. Share your project work and insights to build your professional brand.
Tools & Resources
LinkedIn, GitHub, Kaggle profiles, Local meetups, Tech conferences (e.g., India AI Conference)
Career Connection
Opens doors to referrals, mentorship, and unadvertised job opportunities, vital for long-term career growth in the Indian tech ecosystem.
Program Structure and Curriculum
Eligibility:
- Passed 2nd PUC / 12th standard or equivalent with English, minimum 45% aggregate in Physics and Mathematics (compulsory) along with Chemistry / Bio-Technology / Biology / Electronics / Computer. 40% for SC/ST/OBC. Must qualify in CET / COMEDK / JEE Main.
Duration: 8 semesters / 4 years
Credits: 164 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MATE21 | Advanced Calculus and Numerical Methods | Core | 4 | Partial differentiation, Multiple integrals, Vector integration, Numerical methods for equations, Interpolation |
| 22CHYE22 | Engineering Chemistry | Core | 4 | Electrochemistry, Corrosion, Fuel cells, Water treatment, Polymers, Nanomaterials |
| 22ECL23 | Analog and Digital Electronics | Core | 3 | Diodes, Transistors, OP-AMPs, Logic gates, Flip-flops, Counters |
| 22EEE24 | Basic Electrical Engineering | Core | 3 | DC circuits, AC circuits, Transformers, DC machines, AC machines, Power systems |
| 22CSL25 | Data Structures and Algorithms | Core | 3 | Arrays, Stacks, Queues, Linked lists, Trees, Graphs, Sorting, Searching |
| 22CHYL27 | Engineering Chemistry Lab | Lab | 1 | Water analysis, Potentiometric titrations, pH metry, Viscosity measurement |
| 22EEL28 | Basic Electrical Engineering Lab | Lab | 1 | Verification of Ohm''''s law, KVL/KCL, Thevenin''''s, Norton''''s, Measurement of power |
| 22CSL29 | Data Structures and Algorithms Lab | Lab | 1 | Implementation of stacks, Queues, Linked lists, Binary trees, Sorting algorithms |
| 22MIP20 | Innovation and Design Thinking | Audit | 1 | Design thinking process, Ideation, Prototyping, User-centered design |
| 22NDA21 | Communicative English | Audit | 1 | Speaking skills, Listening skills, Reading comprehension, Written communication, Presentation skills |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BDATS301 | Mathematics for Data Science | Core | 4 | Linear Algebra, Probability Theory, Statistics, Optimization, Random Variables |
| BDATS302 | Data Structures and Applications | Core | 3 | Arrays, Linked Lists, Stacks, Queues, Trees, Hashing, Sorting, Searching |
| BDATS303 | Database Management Systems | Core | 3 | ER Model, Relational Model, SQL, Normalization, Transaction Management, Concurrency Control |
| BDATS304 | Object Oriented Programming with JAVA | Core | 3 | Classes & Objects, Inheritance, Polymorphism, Exception Handling, Multithreading, Collections |
| BDATS305 | Computer Organization and Architecture | Core | 3 | Basic Computer Organization, CPU Design, Memory Hierarchy, I/O Organization, Pipelining |
| BDATS306 | Software Engineering | Core | 3 | Software Process Models, Requirements Engineering, Design Concepts, Software Testing, Project Management |
| BDATSL307 | Data Structures and Applications Lab | Lab | 1 | Implementation of Stacks, Queues, Linked Lists, Trees, Graphs, Hashing |
| BDATSL308 | Database Management Systems Lab | Lab | 1 | SQL Queries, PL/SQL, Triggers, Views, Procedures, Database connectivity |
| BDATSL309 | Object Oriented Programming with JAVA Lab | Lab | 1 | Programs using Classes, Inheritance, Interfaces, Exception Handling, GUI |
| BDATSC310 | Environmental Studies | Audit | 0 | Ecosystems, Biodiversity, Pollution, Renewable Energy, Environmental Legislation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BDATS401 | Design and Analysis of Algorithms | Core | 3 | Asymptotic Notations, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms |
| BDATS402 | Operating Systems | Core | 3 | Process Management, CPU Scheduling, Deadlocks, Memory Management, File Systems, I/O Systems |
| BDATS403 | Probability and Statistics for Data Science | Core | 4 | Probability Distributions, Hypothesis Testing, Regression Analysis, ANOVA, Bayesian Statistics |
| BDATS404 | Python for Data Science | Core | 3 | Python Fundamentals, NumPy, Pandas, Matplotlib, Data Cleaning, Data Manipulation |
| BDATS405 | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Model Evaluation |
| BDATS406 | Data Warehousing and Data Mining | Core | 3 | Data Warehouse Architecture, ETL Process, OLAP, Data Mining Techniques, Association Rule Mining, Classification |
| BDATSL407 | Python for Data Science Lab | Lab | 1 | Data loading, Cleaning, Visualization using Python libraries, Statistical analysis |
| BDATSL408 | Machine Learning Lab | Lab | 1 | Implementation of ML algorithms like Linear Regression, SVM, Decision Trees, K-Means |
| BDATSL409 | Data Warehousing and Data Mining Lab | Lab | 1 | OLAP operations, Data cube, Data mining using tools like Weka, Data preprocessing |
| BDATSC410 | Constitution of India and Professional Ethics | Audit | 0 | Indian Constitution, Fundamental Rights, Professional Ethics, Cyber laws, Corporate Governance |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BDATS501 | Deep Learning | Core | 4 | Neural Networks, CNN, RNN, LSTM, Autoencoders, Generative Models |
| BDATS502 | Big Data Analytics | Core | 3 | Hadoop Ecosystem, HDFS, MapReduce, Spark, Hive, Pig, NoSQL Databases |
| BDATS503 | Internet of Things | Core | 3 | IoT Architecture, Sensors, Actuators, Communication Protocols, Cloud Platforms, Security in IoT |
| BDATS504A | Natural Language Processing | Elective | 3 | Text Preprocessing, NLP Tasks, Word Embeddings, POS Tagging, Named Entity Recognition, Sentiment Analysis |
| BDATS505X (Assumed) | Web Technologies | Elective | 3 | HTML, CSS, JavaScript, Web Servers, Database Connectivity, Web Security |
| BDATSL506 | Deep Learning Lab | Lab | 1 | Implementation of CNN, RNN, Autoencoders using TensorFlow/Keras |
| BDATSL507 | Big Data Analytics Lab | Lab | 1 | Hadoop installation, MapReduce programming, Spark applications, Hive queries |
| BDATSI508 | Internship-I | Internship | 2 | Industry exposure, Project development, Report writing, Presentation skills |
| BDATSC509 | Professional Ethics and Cyber Law | Audit | 0 | Ethical theories, Cybercrime, Intellectual property rights, Data privacy, IT Act |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BDATS601 | Cloud Computing | Core | 4 | Cloud Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization, Cloud Security, AWS/Azure services |
| BDATS602 | Data Visualization | Core | 3 | Data Storytelling, Visualization Principles, Chart Types, Dashboard Design, Tools like Tableau/PowerBI |
| BDATS603 | Computer Networks | Core | 3 | OSI Model, TCP/IP, Network Devices, Routing Protocols, Network Security, Wireless Networks |
| BDATS604A | Business Intelligence | Elective | 3 | BI Architecture, Data Modeling, Data Marts, Reporting, Dashboards, Data Governance |
| BDATS605X (Assumed) | Mobile Application Development | Elective | 3 | Android/iOS architecture, UI/UX design, Activity lifecycle, Data storage, API integration |
| BDATSL606 | Cloud Computing Lab | Lab | 1 | Virtual machine setup, Cloud storage, EC2 instances, S3 buckets, AWS/Azure services |
| BDATSL607 | Data Visualization Lab | Lab | 1 | Creating interactive dashboards, Reports using Tableau/PowerBI, Python visualization libraries |
| BDATSP608 | Mini Project | Project | 2 | Problem formulation, Literature survey, Design, Implementation, Testing, Report writing |
| BDATSC609 | Research Methodology and IPR | Audit | 0 | Research design, Data collection, Statistical analysis, Patenting, Copyrights, Trademarks |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BDATS701 | Artificial Intelligence | Core | 4 | Intelligent Agents, Problem Solving, Search Algorithms, Knowledge Representation, Machine Learning, Expert Systems |
| BDATS702 | Data Security and Privacy | Core | 3 | Cryptography, Access Control, Data Encryption, Privacy-preserving techniques, GDPR, Data Governance |
| BDATS703A | Text Analytics | Elective | 3 | Text mining, Information retrieval, Document classification, Topic modeling, Sentiment analysis |
| BDATS704A | Ethical Hacking for Data Security | Elective | 3 | Penetration testing, Vulnerability assessment, Footprinting, Scanning, Malware analysis |
| BDATSI705 | Internship-II | Internship | 5 | In-depth industry experience, Real-world project implementation, Professional skill development |
| BDATSP706 | Project Work Phase - I | Project | 3 | Project proposal, Literature review, System design, Module development, Preliminary testing |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BDATS801 | Industrial Management and Economics | Core | 3 | Management functions, Production management, Financial management, Marketing, Engineering economics, Project evaluation |
| BDATS802A | Computer Vision for Data Science | Elective | 3 | Image processing fundamentals, Feature extraction, Object recognition, Image segmentation, Deep learning for vision |
| BDATSP803 | Project Work Phase - II | Project | 12 | Advanced development, Integration, Comprehensive testing, Performance evaluation, Technical report, Presentation |
