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B-E in Data Science at PES Institute of Technology and Management
Shivamogga, Karnataka
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About the Specialization
What is Data Science at PES Institute of Technology and Management Shivamogga?
This Data Science program at PES Institute of Technology and Management, Shivamogga focuses on equipping students with expertise in data analytics, machine learning, and artificial intelligence. Situated in a rapidly evolving technological landscape, the curriculum emphasizes practical application and theoretical foundations crucial for India''''s burgeoning data-driven economy. This program distinguishes itself by integrating core computer science principles with advanced statistical methods, preparing graduates for diverse roles in sectors like IT, finance, healthcare, and e-commerce across India.
Who Should Apply?
This program is ideal for aspiring data scientists, machine learning engineers, and data analysts who possess a strong analytical aptitude and a keen interest in problem-solving with data. Fresh graduates from 10+2 science backgrounds with a passion for mathematics and computer science will find this program a robust starting point. It also caters to individuals seeking to transition into the fast-growing data industry, providing a comprehensive skill set for entry-level to mid-level positions.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding career paths such as Data Scientist, ML Engineer, Business Intelligence Analyst, or Data Engineer within prominent Indian and multinational corporations. Entry-level salaries typically range from INR 4-8 lakhs per annum, with experienced professionals commanding significantly higher packages. The program fosters critical thinking and problem-solving skills, aligning with industry demand for professionals capable of extracting actionable insights from complex datasets to drive business decisions and innovation in India.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Develop a strong command over C/C++ and Python programming languages, focusing on data structures and algorithms. Regularly practice coding problems on platforms to solidify logical thinking.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation, C++ tutorials
Career Connection
Essential for cracking coding interviews and building foundational logic required for advanced data science algorithms and development roles.
Build a Strong Mathematical Base- (Semester 1-2)
Focus on understanding core concepts in Engineering Mathematics, Linear Algebra, Probability, and Statistics. These are the bedrock of data science. Utilize online courses and textbooks for deeper understanding.
Tools & Resources
NPTEL courses, Khan Academy, specific textbooks for Calculus, Linear Algebra, Probability
Career Connection
Crucial for understanding the theoretical underpinnings of machine learning algorithms, enabling effective model selection and interpretation.
Engage in Peer Learning and Collaborative Projects- (Semester 1-2)
Form study groups with peers to discuss complex topics, share insights, and collaborate on small academic projects. Participate in campus coding contests or hackathons to apply knowledge.
Tools & Resources
GitHub for code collaboration, campus clubs, online forums like Stack Overflow
Career Connection
Enhances teamwork, communication, and problem-solving skills, highly valued in corporate environments for project delivery.
Intermediate Stage
Practical Application with Real-world Data- (Semester 3-5)
Apply learned concepts in Database Management Systems, Data Structures and Algorithms, and introductory Machine Learning to solve real-world problems using publicly available datasets.
Tools & Resources
Kaggle, UCI Machine Learning Repository, Google Dataset Search, Jupyter Notebook, SQL databases
Career Connection
Develops practical data handling and analysis skills, building a portfolio of projects that are highly attractive to potential employers for data analyst and junior data scientist roles.
Seek Early Industry Exposure through Internships- (Semester 4-5)
Actively search for short-term internships or virtual internships during semester breaks. Focus on roles that involve data collection, cleaning, or basic model implementation to gain hands-on experience.
Tools & Resources
Internshala, LinkedIn, college placement cell, networking events
Career Connection
Provides invaluable exposure to industry practices, helps in networking, and often leads to pre-placement offers or full-time opportunities.
Specialize through Electives and Online Certifications- (Semester 5)
Choose professional and open electives strategically to align with career interests (e.g., Big Data, Cloud Computing). Supplement classroom learning with industry-recognized certifications in specific tools or domains.
Tools & Resources
Coursera, edX, Udemy for certifications, AWS Cloud Practitioner, Google Data Analytics Professional Certificate
Career Connection
Demonstrates initiative and focused skill development, making candidates more competitive for specialized roles in the Indian tech market.
Advanced Stage
Develop a Capstone Project and Portfolio- (Semester 6-8)
Undertake a significant capstone project (Mini/Major Project) that solves a complex data science problem, from data acquisition to model deployment. Document extensively and create a strong online portfolio.
Tools & Resources
GitHub, Personal Website/Blog, Tableau/Power BI for visualizations, deployment platforms like Heroku/Streamlit
Career Connection
The capstone project serves as a showcase of comprehensive skills, crucial for demonstrating competence during placement interviews and securing high-value roles.
Focus on Interview Preparation and Networking- (Semester 7-8)
Actively practice interview questions (technical, behavioral, case studies) for data science roles. Attend webinars, industry conferences, and network with professionals on platforms like LinkedIn.
Tools & Resources
Glassdoor, LeetCode for interview prep, LinkedIn, industry meetups
Career Connection
Direct preparation for placement drives, enabling students to articulate their skills and experience effectively, leading to successful job offers.
Continuous Learning and Research- (Semester 6-8)
Stay updated with the latest advancements in data science, AI, and related fields by reading research papers, blogs, and participating in advanced workshops. Explore opportunities for publishing research.
Tools & Resources
arXiv, Towards Data Science, Medium, university research groups
Career Connection
Fosters lifelong learning, critical for adapting to rapidly changing technologies, and can open doors to research roles or higher studies like M.Tech/Ph.D.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 or equivalent examination with English as one of the languages and obtained a minimum of 45% marks in aggregate in Physics and Mathematics as compulsory subjects along with one of the Chemistry/Biology/Biotechnology/Computer Science/Electronics. (40% for SC/ST/OBC category candidates). Qualification in KCET or COMEDK UGET is also required.
Duration: 4 years / 8 semesters
Credits: 173 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MA11 | Engineering Mathematics-I | Core | 4 | Differential Calculus, Integral Calculus, Multivariable Calculus, Vector Calculus, Differential Equations |
| 22EGS12 | Communicative English | Core | 2 | Technical Communication, Reading Comprehension, Writing Skills, Presentation Skills, Group Discussions, Verbal Aptitude |
| 22EGDL13 | Engineering Graphics & Design (Practical) | Lab | 2 | Orthographic Projections, Isometric Views, Sectional Views, AutoCAD Fundamentals, Assembly Drawings |
| 22DS14 | Programming for Problem Solving | Core | 4 | C Programming Basics, Data Types and Operators, Control Flow Statements, Functions and Pointers, Arrays and Structures, File Handling |
| 22PH15 | Physics for Data Science | Core | 4 | Quantum Mechanics, Solid State Physics, Lasers and Fiber Optics, Semiconductor Devices, Nanoscience and Technology |
| 22DS16 | DS Workshop | Lab | 1 | Basic Computer Hardware, Operating System Fundamentals, Software Installation, Network Configuration, Troubleshooting Techniques |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MA21 | Engineering Mathematics-II | Core | 4 | Linear Algebra, Laplace Transforms, Fourier Series, Partial Differential Equations, Numerical Methods |
| 22EGS22 | Communicative English | Core | 2 | Advanced Technical Writing, Report and Proposal Writing, Professional Correspondence, Public Speaking Skills, Interview Preparation |
| 22DSDL23 | Data Analytics Lab using Python | Lab | 2 | Python Programming Basics, Data Structures in Python, NumPy for Numerical Operations, Pandas for Data Manipulation, Matplotlib and Seaborn for Visualization |
| 22DS24 | Data Structures using C++ | Core | 4 | Introduction to C++, Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting and Searching Algorithms |
| 22CH25 | Chemistry for Data Science | Core | 4 | Engineering Materials, Electrochemistry, Corrosion and its Control, Water Treatment Technology, Fuel Cells and Batteries |
| 22CIV26 | Basic Civil & Mechanical Engineering (Lab) | Lab | 1 | Building Materials, Surveying Basics, Workshop Practices, Lathe Machine Operations, Welding Techniques |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS31 | Advanced Engineering Mathematics | Core | 4 | Complex Analysis, Probability Theory, Random Variables and Distributions, Stochastic Processes, Queueing Theory |
| 22DS32 | Analog and Digital Electronics | Core | 4 | Diode Circuits, Transistor Amplifiers, Operational Amplifiers, Logic Gates and Boolean Algebra, Combinational and Sequential Circuits |
| 22DS33 | Database Management Systems | Core | 4 | DBMS Architecture, ER Model, Relational Model and Algebra, SQL Queries, Normalization, Transaction Management |
| 22DS34 | Data Structures and Algorithms | Core | 4 | Advanced Trees, Heaps and Hashing, Graph Algorithms, Divide and Conquer Strategy, Dynamic Programming |
| 22DS35 | Object Oriented Programming with Java | Core | 4 | Java Fundamentals, Classes and Objects, Inheritance and Polymorphism, Exception Handling, Collections Framework, Multithreading |
| 22DSL36 | Database Management Systems Lab | Lab | 2 | SQL Commands, Database Creation, PL/SQL Programming, Query Optimization, Transaction Control |
| 22DSL37 | Object Oriented Programming with Java Lab | Lab | 2 | Java Program Implementation, Class and Object Manipulation, Inheritance and Interface Usage, Exception Handling in Java, File I/O Operations |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS41 | Design and Analysis of Algorithms | Core | 4 | Asymptotic Notations, Recurrence Relations, Graph Traversals, Shortest Path Algorithms, Spanning Trees, Network Flow |
| 22DS42 | Operating Systems | Core | 4 | OS Structures, Process Management, CPU Scheduling, Deadlocks, Memory Management, File Systems |
| 22DS43 | Probability and Statistics for Data Science | Core | 4 | Probability Distributions, Hypothesis Testing, Regression Analysis, Correlation and Covariance, ANOVA, Sampling Distributions |
| 22DS44 | Artificial Intelligence | Core | 4 | Intelligent Agents, Search Algorithms, Game Playing, Knowledge Representation, Logical Reasoning, Machine Learning Introduction |
| 22DS45 | Web Technologies | Core | 4 | HTML5 and CSS3, JavaScript and DOM, XML and AJAX, PHP and MySQL, Web Security Fundamentals |
| 22DSL46 | Design and Analysis of Algorithms Lab | Lab | 2 | Implementation of Sorting Algorithms, Graph Traversal Algorithms, Dynamic Programming Problems, Greedy Algorithm Solutions, Backtracking Algorithms |
| 22DSL47 | Operating Systems Lab | Lab | 2 | Linux Commands and Shell Scripting, Process Management, CPU Scheduling Algorithms, Deadlock Detection, Memory Allocation Algorithms |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS51 | Machine Learning | Core | 4 | Supervised and Unsupervised Learning, Linear and Logistic Regression, Decision Trees and Random Forests, Support Vector Machines, Clustering Algorithms, Model Evaluation |
| 22DS52 | Data Warehousing and Data Mining | Core | 4 | Data Warehouse Architecture, OLAP Operations, Data Preprocessing, Association Rule Mining, Classification and Prediction, Cluster Analysis |
| 22DS53 | Cloud Computing | Core | 4 | Cloud Architecture, Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization Technology, Cloud Security, AWS and Azure Basics |
| 22DS541 | Professional Elective - I (Big Data Analytics) | Elective | 3 | Hadoop Ecosystem, MapReduce Framework, HDFS Architecture, Apache Spark, NoSQL Databases, Stream Processing |
| 22DS551 | Open Elective - I (Introduction to Cyber Security) | Open Elective | 3 | Cyber Threats and Attacks, Network Security, Cryptography Basics, Firewalls and IDS/IPS, Digital Forensics |
| 22DSL56 | Machine Learning Lab | Lab | 2 | Python for Machine Learning, Scikit-learn Library, Data Preprocessing Techniques, Implementation of Regression Models, Classification and Clustering Algorithms |
| 22DSL57 | Data Warehousing and Data Mining Lab | Lab | 2 | SQL for Data Warehousing, OLAP Operations, Data Cleaning and Transformation, Association Rule Mining Tools, Classification Algorithms Application |
| 22DS58 | Internship/Project Work Phase-1 | Project | 2 | Problem Definition, Literature Survey, Project Planning, Report Writing, Presentation Skills, Team Collaboration |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS61 | Deep Learning | Core | 4 | Neural Network Architectures, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders and GANs, Deep Learning Frameworks (TensorFlow/PyTorch) |
| 22DS62 | Business Intelligence | Core | 4 | BI Architecture, Data Modeling for BI, ETL Processes, Reporting and Dashboards, Data Visualization Tools, Predictive Analytics in BI |
| 22DS63 | Natural Language Processing | Core | 4 | NLP Fundamentals, Text Preprocessing, Tokenization and POS Tagging, Named Entity Recognition, Sentiment Analysis, Language Models |
| 22DS641 | Professional Elective - II (Reinforcement Learning) | Elective | 3 | Markov Decision Processes, Value and Policy Iteration, Q-Learning and SARSA, Deep Reinforcement Learning, Exploration-Exploitation Dilemma |
| 22DS651 | Open Elective - II (Organizational Behavior and Human Resource Management) | Open Elective | 3 | Individual Behavior in Organizations, Group Dynamics and Teamwork, Leadership and Motivation, HRM Functions, Recruitment and Selection, Training and Development |
| 22DSL66 | Deep Learning Lab | Lab | 2 | TensorFlow/PyTorch Implementation, CNN Model Development, RNN Model Development, Transfer Learning Applications, Model Deployment Basics |
| 22DSL67 | Business Intelligence Lab | Lab | 2 | ETL Tool Usage, Data Modeling for BI, Report Generation, Dashboard Creation, Data Visualization Practice |
| 22DS68 | Mini Project | Project | 2 | Problem Identification, Design and Implementation, Testing and Debugging, Documentation, Project Presentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS71 | Data Security and Privacy | Core | 4 | Cryptography and Encryption, Access Control Mechanisms, Data Anonymization Techniques, Privacy Preserving Data Mining, GDPR and Data Protection Regulations, Security Analytics |
| 22DS72 | Ethics in AI and Data Science | Core | 4 | Ethical AI Principles, Bias in Algorithms, Fairness and Accountability, Transparency in AI, Privacy Concerns in Data Science, Societal Impact of AI |
| 22DS731 | Professional Elective - III (Time Series Analysis) | Elective | 3 | Time Series Components, ARIMA Models, Exponential Smoothing, Forecasting Techniques, Deep Learning for Time Series |
| 22DS741 | Professional Elective - IV (Computer Vision) | Elective | 3 | Image Processing Fundamentals, Feature Extraction, Object Detection, Image Segmentation, Facial Recognition, Deep Learning for Vision |
| 22DS751 | Open Elective - III (Intellectual Property Rights) | Open Elective | 3 | Patents and Patentability, Copyrights and Related Rights, Trademarks and Geographical Indications, Industrial Designs and Trade Secrets, IP Infringement and Enforcement |
| 22DS76 | Project Work Phase - II | Project | 6 | Advanced Project Implementation, Experimentation and Evaluation, Results Analysis and Interpretation, Technical Report Writing, Demonstration and Presentation |
| 22DS77 | Internship | Internship | 3 | Industry Work Experience, Practical Skill Application, Professional Networking, Internship Report Submission, Performance Evaluation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS81 | Major Project | Project | 10 | Comprehensive Project Planning, System Design and Development, Testing and Validation, Deployment and Documentation, Viva-Voce Examination |
| 22DS821 | Professional Elective - V (IoT for Data Science) | Elective | 3 | IoT Architecture, Sensors and Actuators, IoT Protocols, Data Collection from IoT Devices, Edge Computing, IoT Security |
| 22DS83 | Technical Seminar | Seminar | 1 | Research Topic Selection, Literature Review, Presentation Skills, Technical Report Writing, Question and Answer Handling |
| 22DS84 | Research Methodology and IPR | Core | 3 | Research Problem Formulation, Data Collection Methods, Statistical Analysis, Report Writing, Plagiarism and Ethics, Intellectual Property Rights Overview |
