Program Objectives
The Program Objectives for the B.Tech CSE (AI & Data Science) program are as follows:
- To provide students with a substantial understanding of Data Science fundamentals todevelop their technical competence to address the real life problems.
- To train the students with the knowledge and skills in the area of Data Science for analysis, design and implementation of real life problems.
- To groom the students overall personality for professional growth.
- To inculcate values and ethics among the students and making them aware about their social commitments.

4 Years
Duration

₹ 1,95,000
Fees

PCET
Centralized Placement Cell
Program Highlights
Structured Program with Dedicated Support
Dedicated Career Assistance
Hands on Learning
Job Readiness Program
Curriculum designed to cater recent trends
Quality placements.
Student participation in global competitions.
Exposure of In-house Incubation Cell nurturing various Start ups.
Preamble
Data science has become important due to recent technology disruptions. There is an increasing demand of capturing, analyzing, and synthesizing this large amount of data sets in a number of application domains to better understand various phenomena and to convert the available information in the data into actionable strategies such as new scientific discoveries, business applications, policy making, and healthcare etc.
Data science is the area where applications of various tools and techniques from the disciplines of applied statistics, mathematics and computer science are used to get greater insight and to make better and informed decisions for various purposes by analyzing a large amount of data.
Given the mounting importance of the data science paradigm, Pimpri Chinchwad University offers a 4 years bachelor program B Tech in Computer Science & Engineering with specialization in Artificial Intelligence &Data Science.
The curriculum of the B Tech CSE (AI & Data Science) program focuses on exposing to the students with the essentials of applied statistics, applied mathematics, and computer science required in the context of data science and its applications with strong emphasis on having hands-on experience with the help of theory, labs and experience of dealing with real-world problems.
Vision and Mission
Vision
To offer value-based quality education that promotes academic excellence through encouraging research, innovation and entrepreneurship.
Mission
- To accomplish a competitive advantage in Data Science by continual learning and exploring new technologies.
- To encourage and carry out comprehensive research and development.
- To foster an environment that encourages the design and development of applications with focus on societal needs.
- To imbibe principles of morality and ethics in students.
Course Curriculum
Semester I
Semester – I | Teaching Scheme | ||||||
Sr. No. | Category | Course Name | L | T | P | H | Cr |
1 | MAJ | Linear Algebra & Univariate Calculus | 3 | 1 | – | 4 | 4 |
2 | MIN | Engineering Physics | 3 | – | – | 3 | 3 |
3 | MAJ | Basic Electrical & Electronics
Engineering |
3 | – | – | 3 | 3 |
4 | SEC | Computer Programming – I | 1 | – | – | 1 | 1 |
5 | OE | Open Elective – I | 3 | – | – | 3 | 3 |
6 | MIN | Engineering Physics Lab | – | – | 1 | 2 | 1 |
7 | MAJ | Basic Electrical & Electronics
Engineering Lab |
– | – | 1 | 2 | 1 |
8 | SEC | Computer Programming Lab I | – | – | 2 | 4 | 2 |
9 | AEC | HSMC – I | – | – | 1 | 2 | 1 |
10 | VAC | Life Skill-I | – | – | 1 | 2 | 1 |
Total | 13 | 01 | 6 | 26 | 20 |
Semester II
Semester – II | Teaching Scheme | ||||||
Sr. No. | Category | Course Name | L | T | P | H | Cr |
1 | MAJ | Multivariate Calculus | 3 | 1 | – | 4 | 4 |
2 | MAJ | Engineering Chemistry | 3 | – | – | 3 | 3 |
3 | MIN | Engineering Graphics | 2 | – | – | 2 | 2 |
4 | SEC | Computer Programming – I | 1 | – | – | 1 | 1 |
5 | MAJ | Engineering Chemistry Laboratory | – | – | 1 | 2 | 1 |
6 | OE | Open Elective – II | 3 | – | – | 3 | 3 |
7 | MIN | Engineering Graphics Laboratory | – | – | 2 | 4 | 2 |
8 | AEC | HSMC – II | – | – | 1 | 2 | 1 |
9 | SEC | Computer Programming Lab I | – | – | 2 | 4 | 2 |
10 | VAC | Life Skill-II | – | – | 1 | 2 | 1 |
Total | 12 | 01 | 7 | 27 | 20 |
Semester III
Semester – III | Teaching Scheme | ||||||
Sr. No. | Category | Course Name | L | T | P | H | Cr |
1 | MAJ | Mathematical Foundation for Data
Science |
3 | – | – | 3 | 3 |
2 | MIN | Discrete Mathematics | 3 | 1 | – | 4 | 4 |
3 | MAJ | Fundamentals of Data Structures | 3 | – | – | 3 | 3 |
4 | OE | Open Elective – III | 3 | – | – | 3 | 3 |
5 | SEC | Proficiency Foundation Course – I | 1 | – | – | 1 | 1 |
6 | MAJ | Mathematical Foundation for Data
Science Lab |
– | – | 1 | 2 | 1 |
7 | MAJ | Fundamentals of Data Structures –Lab | – | – | 1 | 2 | 1 |
8 | AEC | HSMC – III | – | – | 1 | 2 | 1 |
9 | SEC | Proficiency Foundation Course – I Lab | – | – | 2 | 4 | 2 |
10 | VAC | Life Skill-III | – | – | 1 | 2 | 1 |
Total | 13 | 01 | 6 | 26 | 20 |
Semester IV
Semester – IV | Teaching Scheme | ||||||
Sr. No. | Category | Course Name | L | T | P | H | Cr |
1 | MAJ | Artificial Intelligence | 3 | – | – | 3 | 3 |
2 | MAJ | Object Oriented Programming with
JAVA |
3 | – | – | 3 | 3 |
3 | MIN | Operating System | 2 | – | – | 2 | 2 |
4 | MIN | Database Management System | 2 | – | – | 2 | 2 |
5 | MAJ | Artificial Intelligence Lab –Lab | – | – | 2 | 4 | 2 |
6 | MAJ | Object Oriented Programming-Lab | – | – | 2 | 4 | 2 |
7 | MIN | Operating System Lab | – | – | 1 | 2 | 1 |
8 | MIN | Database Management System Lab | – | – | 1 | 2 | 1 |
9 | AEC | HSMC – IV | – | – | 1 | 2 | 1 |
10 | SEC | Proficiency Foundation Course – II
Lab |
– | – | 2 | 4 | 2 |
11 | VAC | Life Skill-IV | – | – | 1 | 2 | 1 |
Total | 10 | – | 10 | 30 | 20 |
Semester V
Semester – V | Teaching Scheme | ||||||
Sr. No. | Category | Course Name | L | T | P | H | Cr |
1 | MAJ | Big Data Analytics | 3 | – | – | 3 | 3 |
2 | MAJ | Data Handling and Visualization | 3 | – | – | 3 | 3 |
3 | MIN | Data Warehousing and Mining | 2 | – | – | 2 | 2 |
4 | MIN | Elective – I | 2 | – | – | 2 | 2 |
5 | MAJ | Big Data Analytics Lab | – | – | 2 | 4 | 2 |
6 | MAJ | Data Handling and Visualization Lab | – | – | 2 | 4 | 2 |
7 | MIN | Data Warehousing and Mining Lab | – | – | 1 | 2 | 1 |
8 | MIN | Elective – I Lab | – | – | 1 | 2 | 1 |
9 | AEC | HSMC – V | – | – | 1 | 2 | 1 |
10 | SEC | Proficiency Foundation Course – III
Lab |
– | – | 2 | 4 | 2 |
11 | VAC | Life Skill-V | – | – | 1 | 2 | 1 |
Total | 10 | 00 | 10 | 30 | 20 |
Semester VI
Semester – VI | Teaching Scheme | ||||||
Sr. No. | Category | Course Name | L | T | P | H | Cr |
1 | MAJ | Machine Learning | 3 | – | – | 3 | 3 |
2 | MAJ | Data Analysis and Data Wrangling | 3 | – | – | 3 | 3 |
3 | MIN | Predictive Modelling and Analytics | 2 | – | – | 2 | 2 |
4 | MIN | Elective – II | 2 | – | – | 2 | 2 |
5 | MAJ | Machine Learning Lab | – | – | 2 | 4 | 2 |
6 | MAJ | Data Analysis and Data Wrangling
Lab |
– | – | 2 | 4 | 2 |
7 | MIN | Predictive Modelling and Analytics
Lab |
– | – | 1 | 2 | 1 |
8 | MIN | Elective – II Lab | – | – | 1 | 2 | 1 |
9 | AEC | HSMC – VI | – | – | 1 | 2 | 1 |
10 | SEC | Proficiency Foundation Course – IV
Lab |
– | – | 2 | 4 | 2 |
11 | VAC | Life Skill-VI | – | – | 1 | 2 | 1 |
Total | 10 | 00 | 10 | 30 | 20 |
Semester VII
Semester – VII | Teaching Scheme | ||||||
Sr. No. | Category | Course Name | L | T | P | H | Cr |
1 | MAJ | Deep Learning | 3 | – | – | 3 | 3 |
2 | MAJ | Recommender System | 3 | – | – | 3 | 3 |
3 | MIN | Elective – III | 2 | – | – | 2 | 2 |
4 | MIN | Elective – IV | 2 | – | – | 2 | 2 |
5 | MAJ | Deep Learning Lab | – | – | 2 | 4 | 2 |
6 | MAJ | Recommender System Lab | – | – | 2 | 4 | 2 |
7 | MIN | Elective – III Lab | – | – | 1 | 2 | 1 |
8 | MIN | Elective – IV Lab | – | – | 1 | 2 | 1 |
9 | INTR | Internship | – | – | 4 | 8 | 4 |
Total | 10 | – | 10 | 30 | 20 |
Semester VIII
Semester – VIII | Teaching Scheme | |||||||
Sr. No. | Category | Course Name | L | T | P | H | Cr | |
1 | MAJ | Time series analysis and Forecasting | 3 | – | – | 3 | 3 | |
2 | MIN | Elective – V | 2 | – | – | 2 | 2 | |
3 | MAJ | Time series analysis and Forecasting Lab | – | – | 2 | 4 | 2 | |
4 | MIN | Elective – V Lab | – | – | 1 | 2 | 1 | |
5 | PROJ | Project | – | – | 12 | 24 | 12 | |
Total | 5 | 00 | 15 | 35 | 20 |
List of Tentative Electives:
Sr. No. | Course Name |
1 | Healthcare Data Analytics |
2 | Social Media Analytics |
3 | Bayesian Data Analysis |
4 | Time series analysis and Forecasting |
5 | Business Intelligence and Analytics |
6 | Cognitive Systems |
7 | Data Modelling and Simulation |
8 | Decision Support systems and Intelligent systems |
9 | Intelligent Database System |
10 | Information Extraction and Retrieval |
11 | Knowledge Representation and Reasoning |
12 | Nature Inspired computing for Data Science |
13 | Computer Vision and Natural Language Processing |
14 | Design Thinking |
15 | Image Processing |
List of Tentative Open Electives:
Sr. No. | Course Name |
1 | Data Science for Engineers |
2 | Introduction of Data Science |
3 | Data Analytics using Python |
4 | Python for Data Science |
5 | Neural Network and fuzzy logic Control |
List of Tentative Life Skill Courses:
Sr. No. | Course Name |
1 | Practicing Meditation |
2 | Sports |
3 | Yoga |
4 |
Performing Arts:
Music, Singing, Poetry, Indian Conventional Dancing, Photography, Short Movie Making, Painting/ Sketching/ Drawing, Theatre Arts, Anchoring, Calligraphy etc. |
5 | Social welfare and Cultural Awareness |
6 |
Caring and service
Hospital Caring, Personal Safety, First Aid, Disaster Management Gardening, Organic farming, Cooking etc. |
Abbreviations: Course Abbreviation; L – Lecture; T – Tutorial; P – Practical; H – Hours; CR – Credits, HSMC – Humanities/ Social Sciences/ Management Courses
Programme
Programme Educational Objectives (PEOs)
- To provide students with a substantial understanding of Data Science fundamentals to develop their technical competence to address the real life problems.
- To train the students with the knowledge and skills in the area of Data Science for analysis, design and implementation of real life problems.
- To groom the students overall personality for professional growth.
- To inculcate values and ethics among the students and making them aware about their social commitments.
Programme Outcomes (POs)
- Engineering Knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
- Problem Analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
- Design/Development of Solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
- Conduct Investigations of Complex Problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
- Modern Tool Usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations.
- The Engineer and Society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
- Environment and Sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
- Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
- Individual and Team Work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
- Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
- Project Management and Finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
- Life-long Learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.
Programme Specific Outcomes (PSOs)
- Apply computing theory, languages and algorithms, mathematical and statistical model and the principles of optimization to appropriately formulate and use data analysis.
- Invent and use appropriate models of data analysis, access the quality of input, derive insights from results and investigate potential issues.
Career Opportunities
There are many exciting career opportunities in Data Science such as
Eligibility
Passed 10+2 examination with Physics & Mathematics AND one of the subject from the following:
Chemistry/ Computer Science/ Electronics/ Information Technology/ Biology / Informatics Practices/ Biotechnology/ Technical Vocational subject/ Agriculture/ Engineering Graphics/ Business Studies/ Entrepreneurship.
Obtained at least 45% marks (40marks in case of candidate belonging to reserved category) in the above subjects taken together.
In addition to this, the applicant must have qualified at least one engineering entrance examination like MHT-CET 2023/JEE 2023 /Other State or National Level Engineering Entrance Exam of 2023 / PERA 2023 / CUET 2023 or Entrance Test Conducted by PCU
Candidates are selected based on entrance test score and on merit.