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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

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.