Course Structure Overview

The Data Science program at Get Group Of Institution Faculty Of Technology is structured over eight semesters, with a balanced blend of core courses, departmental electives, science electives, and laboratory sessions. Each semester carries a specific focus that builds upon previous learnings to achieve comprehensive mastery in the field.

Semester-wise Course Breakdown

Semester	Course Code	Course Title	Credit Structure (L-T-P-C)	Prerequisites
Semester I	DS101	Introduction to Data Science	3-1-0-4	None
	MA101	Calculus I	3-1-0-4	None
	CS101	Programming Fundamentals	3-0-2-5	None
	PH101	Physics I	3-1-0-4	None
	CH101	Chemistry I	3-1-0-4	None
	ME101	Introduction to Engineering	2-1-0-3	None
	ES101	English Communication Skills	2-0-0-2	None
	PH102	Physics Lab I	0-0-2-2	PH101
Semester II	DS201	Linear Algebra & Probability	3-1-0-4	MA101
	MA201	Calculus II	3-1-0-4	MA101
	CS201	Data Structures & Algorithms	3-1-2-6	CS101
	PH201	Physics II	3-1-0-4	PH101
	CH201	Chemistry II	3-1-0-4	CH101
	EE201	Electrical Engineering Fundamentals	3-1-0-4	PH101
	ES201	Technical Writing & Presentation Skills	2-0-0-2	ES101
	CS202	Data Structures Lab	0-0-2-2	CS101, CS201
Semester III	DS301	Database Systems	3-1-0-4	CS201
	MA301	Statistics I	3-1-0-4	MA201
	CS301	Machine Learning Fundamentals	3-1-0-4	DS201, CS201
	PH301	Thermodynamics & Statistical Physics	3-1-0-4	PH201
	CH301	Organic Chemistry	3-1-0-4	CH201
	ME301	Mechanics & Materials	3-1-0-4	PH201, ME201
	ES301	Social Sciences & Ethics in Engineering	2-0-0-2	None
	DS302	Database Systems Lab	0-0-2-2	DS301
Semester IV	DS401	Advanced Statistical Methods	3-1-0-4	MA301
	CS401	Deep Learning	3-1-0-4	CS301
	MA401	Probability & Stochastic Processes	3-1-0-4	MA301
	PH401	Quantum Physics I	3-1-0-4	PH301
	CH401	Inorganic Chemistry	3-1-0-4	CH301
	ME401	Fluid Mechanics & Heat Transfer	3-1-0-4	ME301
	ES401	Environmental Studies	2-0-0-2	None
	CS402	Deep Learning Lab	0-0-2-2	CS401
Semester V	DS501	Big Data Technologies	3-1-0-4	DS301, CS301
	CS501	Natural Language Processing	3-1-0-4	CS401
	MA501	Time Series Analysis	3-1-0-4	MA401
	PH501	Quantum Physics II	3-1-0-4	PH401
	CH501	Physical Chemistry	3-1-0-4	CH401
	ME501	Thermodynamics & Control Systems	3-1-0-4	ME401
	ES501	Business Analytics	2-0-0-2	DS401, MA301
	DS502	Big Data Analytics Lab	0-0-2-2	DS501
Semester VI	DS601	Computer Vision	3-1-0-4	CS401, DS501
	CS601	Reinforcement Learning	3-1-0-4	CS401, MA401
	MA601	Bayesian Inference	3-1-0-4	MA501
	PH601	Quantum Computing Concepts	3-1-0-4	PH501
	CH601	Chemical Engineering Fundamentals	3-1-0-4	CH501
	ME601	Applied Mechanics	3-1-0-4	ME501
	ES601	Project Management	2-0-0-2	None
	DS602	Computer Vision Lab	0-0-2-2	DS601
Semester VII	DS701	Data Ethics & Governance	3-1-0-4	ES501, DS601
	CS701	Advanced Topics in Machine Learning	3-1-0-4	CS601
	MA701	Mathematical Modeling	3-1-0-4	MA601
	PH701	Quantum Information Theory	3-1-0-4	PH601
	CH701	Materials Science	3-1-0-4	CH601
	ME701	Engineering Design & Optimization	3-1-0-4	ME601
	ES701	Leadership & Team Dynamics	2-0-0-2	None
	DS702	Capstone Project I	0-0-4-6	DS501, DS601
Semester VIII	DS801	Capstone Project II	0-0-4-6	DS702
	CS801	Research Methodology	3-1-0-4	MA701, DS701
	MA801	Advanced Probability & Measure Theory	3-1-0-4	MA701
	PH801	Quantum Field Theory	3-1-0-4	PH701
	CH801	Industrial Chemistry	3-1-0-4	CH701
	ME801	Advanced Control Systems	3-1-0-4	ME701
	ES801	Entrepreneurship & Innovation	2-0-0-2	None
	DS802	Internship/Research Thesis	0-0-6-10	DS702, CS701

Detailed Departmental Elective Courses

The department offers a rich selection of advanced elective courses designed to deepen students' understanding and practical application of data science concepts. Below are descriptions of key electives:

• Advanced Machine Learning: This course explores modern machine learning paradigms including ensemble methods, boosting algorithms, and unsupervised learning techniques. Students engage in hands-on projects involving real-world datasets, enhancing their ability to design and evaluate complex ML models.
• Natural Language Processing (NLP): Focused on extracting semantic meaning from text data, this course covers tokenization, sentiment analysis, named entity recognition, and transformer-based architectures. Students build applications like chatbots, summarizers, and language translators using state-of-the-art libraries.
• Computer Vision: This elective delves into image processing, object detection, and neural network-based solutions for visual recognition tasks. Through practical sessions, students learn to implement convolutional neural networks (CNNs) and apply them to real-world problems like autonomous driving and medical imaging.
• Time Series Forecasting: Students study advanced forecasting techniques using ARIMA models, seasonal decomposition, and deep learning approaches for temporal data prediction. Emphasis is placed on building robust models for stock price forecasting, weather prediction, and demand planning.
• Bayesian Inference: This course introduces probabilistic reasoning and Bayesian modeling frameworks. Students learn to construct prior distributions, perform posterior inference, and utilize Markov Chain Monte Carlo (MCMC) methods in computational applications.
• Reinforcement Learning: Designed for students interested in autonomous agents and decision-making systems, this course covers Q-learning, policy gradients, and actor-critic methods. Practical assignments involve training robotic arms and game-playing AI systems.
• Data Visualization & Communication: Focused on presenting data insights effectively, this course teaches tools like Tableau, Power BI, D3.js, and matplotlib. Students learn to create interactive dashboards, informative charts, and compelling narratives around data findings.
• Big Data Technologies: This course explores distributed computing frameworks such as Apache Spark, Hadoop, and Kafka. Students gain experience in processing massive datasets using cluster computing environments and implementing scalable solutions for data engineering tasks.
• Quantitative Finance: Tailored for students interested in financial modeling, this elective covers stochastic calculus, option pricing models, risk management techniques, and algorithmic trading strategies. Real-world applications include portfolio optimization and derivative valuation.
• Healthcare Informatics: This course bridges healthcare domains with data science, focusing on EHR systems, clinical decision support, genomic data analysis, and public health analytics. Students work with anonymized medical datasets to solve real clinical challenges.

Project-Based Learning Philosophy

Our department strongly advocates for project-based learning as a means of integrating theoretical knowledge with practical skills. Projects are assigned at multiple levels throughout the program, from small lab exercises to major capstone initiatives.

Mini-Projects

Mini-projects are undertaken in the second and third years, allowing students to apply newly acquired concepts in controlled environments. These projects typically last one semester and are supervised by faculty members or senior researchers. Assessment criteria include:

• Technical Implementation
• Problem-Solving Approach
• Documentation Quality
• Presentation Skills
• Team Collaboration

Final-Year Thesis/Capstone Project

The final-year capstone project represents the culmination of the student’s academic journey. It is a substantial, independent research endeavor that addresses a relevant problem in data science. Students may choose to work individually or form teams, with guidance from faculty mentors.

Key aspects of the capstone process include:

• Proposal Submission
• Regular Progress Reports
• Midterm Evaluation
• Final Presentation and Defense
• Documentation and Publication Potential

The selection of projects is influenced by:

• Industry Partnerships
• Faculty Research Interests
• Student Interests and Career Goals
• Availability of Resources and Datasets

Mentorship Structure

Each student is paired with a faculty mentor based on mutual interest areas and availability. Mentors provide ongoing support, guidance on methodology, and feedback on progress throughout the project lifecycle.