Comprehensive Course Structure
The Data Science curriculum at Aditya University Kakinada spans four years and is divided into eight semesters. Each semester includes core courses, departmental electives, science electives, and laboratory components designed to build a strong foundation in data science principles and practices.
| Year | Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|---|
| I | 1 | DS101 | Introduction to Data Science | 3-0-2-4 | - |
| DS102 | Calculus I | 3-0-2-4 | - | ||
| DS103 | Linear Algebra | 3-0-2-4 | - | ||
| DS104 | Programming Fundamentals | 2-0-2-3 | - | ||
| DS105 | Statistics I | 3-0-2-4 | - | ||
| I | 2 | DS201 | Data Structures and Algorithms | 3-0-2-4 | DS104 |
| DS202 | Calculus II | 3-0-2-4 | DS102 | ||
| DS203 | Probability Theory | 3-0-2-4 | DS105 | ||
| DS204 | Database Systems | 3-0-2-4 | - | ||
| DS205 | Statistics II | 3-0-2-4 | DS105 | ||
| II | 3 | DS301 | Machine Learning I | 3-0-2-4 | DS201, DS203 |
| DS302 | Data Mining | 3-0-2-4 | DS204 | ||
| DS303 | Statistical Inference | 3-0-2-4 | DS205 | ||
| DS304 | Python for Data Science | 2-0-2-3 | DS104 | ||
| DS305 | Data Visualization | 3-0-2-4 | - | ||
| II | 4 | DS401 | Machine Learning II | 3-0-2-4 | DS301 |
| DS402 | Deep Learning | 3-0-2-4 | DS301 | ||
| DS403 | Time Series Analysis | 3-0-2-4 | DS303 | ||
| DS404 | R Programming | 2-0-2-3 | DS104 | ||
| DS405 | Research Methodology | 3-0-2-4 | - | ||
| III | 5 | DS501 | Advanced Machine Learning | 3-0-2-4 | DS401, DS402 |
| DS502 | Computer Vision | 3-0-2-4 | DS401 | ||
| DS503 | Natural Language Processing | 3-0-2-4 | DS401 | ||
| DS504 | Reinforcement Learning | 3-0-2-4 | DS401 | ||
| DS505 | Special Topics in Data Science | 3-0-2-4 | - | ||
| III | 6 | DS601 | Big Data Analytics | 3-0-2-4 | DS204, DS302 |
| DS602 | Privacy and Security in Data Science | 3-0-2-4 | DS105 | ||
| DS603 | Ethics in Data Science | 3-0-2-4 | - | ||
| DS604 | Advanced Visualization Techniques | 3-0-2-4 | DS305 | ||
| DS605 | Capstone Project I | 2-0-0-2 | - | ||
| IV | 7 | DS701 | Capstone Project II | 2-0-0-2 | DS605 |
| DS702 | Industry Internship | 0-0-4-4 | - | ||
| DS703 | Advanced Topics in AI | 3-0-2-4 | DS501 | ||
| DS704 | Data Science for Business | 3-0-2-4 | DS301 | ||
| DS705 | Professional Development | 2-0-0-2 | - | ||
| IV | 8 | DS801 | Final Year Thesis | 4-0-0-6 | DS701 |
| DS802 | Entrepreneurship in Data Science | 3-0-2-4 | - | ||
| DS803 | Advanced Ethics and Governance | 3-0-2-4 | DS603 | ||
| DS804 | Industry Collaboration Project | 4-0-0-6 | - | ||
| DS805 | Graduation Seminar | 2-0-0-2 | - |
Detailed Departmental Elective Courses
Departmental electives provide students with opportunities to specialize in specific areas of interest within data science. These courses are designed to enhance technical skills and foster deeper understanding of advanced concepts.
- Advanced Machine Learning (DS501): This course delves into the theoretical underpinnings of machine learning, focusing on ensemble methods, kernel machines, and model selection techniques. Students will implement complex algorithms using Python and explore their applications in real-world scenarios.
- Computer Vision (DS502): Covering topics such as image processing, object detection, facial recognition, and deep learning architectures for visual data, this course prepares students to develop intelligent systems capable of interpreting visual inputs from cameras and sensors.
- Natural Language Processing (DS503): Students learn about text preprocessing, sentiment analysis, language modeling, and neural network applications in linguistics. The course includes hands-on labs using libraries like NLTK, spaCy, and Hugging Face Transformers.
- Reinforcement Learning (DS504): This elective explores how agents can learn optimal behaviors through interaction with environments, covering Markov decision processes, Q-learning, policy gradients, and actor-critic methods. Applications in robotics and game AI are emphasized.
- Special Topics in Data Science (DS505): A flexible course that allows students to explore emerging areas in data science such as quantum computing, explainable AI, or federated learning. Topics vary annually based on faculty expertise and industry trends.
- Big Data Analytics (DS601): Focused on scalable data processing frameworks like Hadoop and Spark, this course teaches students how to manage large datasets using distributed computing environments. Students will work with real-world datasets from social media platforms and e-commerce sites.
- Privacy and Security in Data Science (DS602): This course addresses ethical considerations in data handling, including GDPR compliance, differential privacy, and secure multi-party computation. Students learn to design systems that protect individual identities while preserving utility.
- Ethics in Data Science (DS603): Examines the moral implications of data usage, algorithmic bias, fairness, and transparency in AI systems. Through case studies, students develop frameworks for responsible decision-making in data-intensive environments.
- Advanced Visualization Techniques (DS604): Utilizing tools like D3.js, Plotly, and Tableau, this course teaches advanced visualization techniques that help communicate complex findings to diverse stakeholders effectively.
- Capstone Project I (DS605): Students begin their capstone journey by selecting a project topic aligned with industry needs or academic interests. They work closely with faculty mentors to define research questions, gather data, and outline methodology for their final thesis.
Project-Based Learning Philosophy
At Aditya University Kakinada, we believe that practical experience is essential for mastering data science concepts. Our project-based learning approach integrates theory with application across all levels of the curriculum. Mini-projects are assigned in the second and third years to reinforce fundamental skills and encourage experimentation.
The mini-projects typically last two months and involve small teams working on real datasets provided by industry partners or generated through simulated environments. Evaluation criteria include technical execution, creativity, clarity of presentation, and adherence to deadlines.
The final-year thesis/capstone project is a significant component of the program. Students select an area of interest within data science and collaborate with faculty mentors to conduct original research or develop innovative solutions. The process involves literature review, hypothesis formulation, data collection, model building, validation, and documentation.
Students are encouraged to propose their own ideas but may also choose from suggested topics provided by faculty members or industry partners. The selection process ensures that each student's project aligns with their strengths and career goals while contributing to the broader field of data science.