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Fees
₹30,00,000
Placement
93.0%
Avg Package
₹5,20,000
Highest Package
₹8,50,000
Fees
₹30,00,000
Placement
93.0%
Avg Package
₹5,20,000
Highest Package
₹8,50,000
Seats
100
Students
1,200
Seats
100
Students
1,200
The Data Science curriculum at Plaksha University Mohali is meticulously designed to provide a balanced blend of foundational knowledge and advanced specialization. The program spans eight semesters, with each semester structured around core courses, departmental electives, science electives, and laboratory sessions.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | DS101 | Introduction to Programming | 3-1-0-4 | - |
| 1 | DS102 | Calculus I | 3-1-0-4 | - |
| 1 | DS103 | Statistics for Data Science | 3-1-0-4 | - |
| 1 | DS104 | Data Structures and Algorithms | 3-1-0-4 | DS101 |
| 2 | DS201 | Linear Algebra and Probability Theory | 3-1-0-4 | DS102 |
| 2 | DS202 | Database Systems | 3-1-0-4 | DS101 |
| 2 | DS203 | Operating Systems | 3-1-0-4 | DS101 |
| 2 | DS204 | Computer Architecture | 3-1-0-4 | DS101 |
| 3 | DS301 | Machine Learning Fundamentals | 3-1-0-4 | DS201 |
| 3 | DS302 | Data Mining Techniques | 3-1-0-4 | DS202 |
| 3 | DS303 | Statistical Inference and Modeling | 3-1-0-4 | DS103 |
| 3 | DS304 | Deep Learning | 3-1-0-4 | DS301 |
| 4 | DS401 | Advanced Machine Learning | 3-1-0-4 | DS301 |
| 4 | DS402 | Natural Language Processing | 3-1-0-4 | DS301 |
| 4 | DS403 | Computer Vision | 3-1-0-4 | DS301 |
| 4 | DS404 | Time Series Analysis | 3-1-0-4 | DS303 |
| 5 | DS501 | Reinforcement Learning | 3-1-0-4 | DS401 |
| 5 | DS502 | Big Data Analytics | 3-1-0-4 | DS202 |
| 5 | DS503 | Data Visualization and Storytelling | 3-1-0-4 | DS103 |
| 5 | DS504 | Privacy and Security in Data Science | 3-1-0-4 | DS202 |
| 6 | DS601 | Quantitative Finance | 3-1-0-4 | DS303 |
| 6 | DS602 | Healthcare Analytics | 3-1-0-4 | DS503 |
| 6 | DS603 | Sustainability Analytics | 3-1-0-4 | DS502 |
| 6 | DS604 | Entrepreneurship in Data Science | 3-1-0-4 | - |
| 7 | DS701 | Capstone Project I | 3-1-0-4 | DS501 |
| 7 | DS702 | Capstone Project II | 3-1-0-4 | DS701 |
| 8 | DS801 | Research Internship | 3-1-0-4 | - |
The department offers a range of advanced elective courses that allow students to specialize in emerging areas within data science. These courses are taught by leading faculty members and reflect the latest developments in the field.
This course delves into neural network architectures, convolutional networks, recurrent networks, and transformer models using TensorFlow. Students learn how to implement complex deep learning pipelines from scratch and optimize performance on GPUs.
Students explore text processing techniques, sentiment analysis, language modeling, and machine translation. The course includes hands-on labs with tools like spaCy, NLTK, and Hugging Face Transformers.
This advanced course covers image classification, object detection, segmentation, and generative adversarial networks (GANs). Students gain practical experience working with datasets like ImageNet and COCO using PyTorch and OpenCV.
Focusing on forecasting methods for temporal data, this course explores ARIMA models, exponential smoothing, and state-space models. Students apply these techniques to real-world financial and environmental datasets.
This course introduces Markov decision processes, Q-learning, policy gradients, and actor-critic methods. Practical applications include robotics control, game AI, and autonomous vehicle navigation.
Students learn to process large-scale datasets using Hadoop ecosystem, Spark, Kafka, and Databricks. The course emphasizes distributed computing principles and scalable analytics solutions.
This course teaches students how to create compelling visualizations using Tableau, Power BI, and D3.js. Emphasis is placed on effective communication of insights to diverse audiences through interactive dashboards and reports.
Students examine data anonymization techniques, differential privacy, secure multi-party computation, and ethical considerations in AI development. Case studies from healthcare and finance sectors illustrate real-world challenges.
This course applies mathematical modeling to financial markets, covering derivatives pricing, portfolio optimization, risk management, and algorithmic trading strategies. Students use Python libraries like QuantLib and Bloomberg terminals.
Students study applications of data science in clinical research, drug discovery, electronic health records analysis, and patient outcome prediction. Collaborations with hospitals provide real-world context for learning.
This interdisciplinary course combines environmental science with data analytics to tackle sustainability challenges such as carbon footprint tracking, renewable energy forecasting, and climate impact modeling.
The department believes that project-based learning is essential for developing practical skills and deepening conceptual understanding. Students begin working on individual projects from their second year, building upon foundational knowledge gained in core courses.
Mini-projects are assigned at the end of each semester, focusing on specific aspects of data science methodologies such as exploratory data analysis, model selection, and evaluation metrics. These projects are assessed through peer review processes and faculty feedback.
The final-year capstone project represents the culmination of a student's academic journey. Working in teams or individually, students select real-world problems from industry partners or self-initiated research questions. Projects are supervised by faculty mentors who guide students through data collection, modeling, implementation, and presentation stages.
Each project must demonstrate proficiency in statistical reasoning, computational methods, domain-specific knowledge, and ethical considerations. Students present their findings to an external panel comprising industry experts and academic faculty, ensuring alignment with professional standards.
