B. Sc.- (Computer Science ) in Data Analytics

DETAILS

This Bachelor's degree program in Computer Science is designed that gives extensive knowledge into the methods of forecasting, predictive modelling and the mathematical background for the basics of statistics. It gives the students exposure to the different tools of the trade like NoSQL, Data warehousing, PyTorch, Tableau, R, and Splunk. They also get extensive knowledge in the field of statistics and mathematical simulations, which enables the scholar to be able to get a good grasp on the market demand for Predictive analytics and simulation work which is a primary contributor to the designing of the Meta Verse-enabled professionals. 

Duration

Duration of Program

3 years (6 Semesters)

Seats

Seats

*

DESIGN YOUR OWN DEGREE


upes

UPES’ curriculum framework is holistic in its overall structure and yet focuses on the individual need of the student to discover, experience, explore and challenge. Along with the core subject, students have the option to choose from subject-focused specialisations. They are also allowed to choose minor/exploratory subject from other schools at UPES that are: School of Engineering, School of Computer Science, School of Law, School of Business, School of Health Sciences, School of Design, School of Modern Media, and School of Liberal Studies.

Further, based on the multifaceted needs of the global workplace and evolving lifestyles, the curriculum offers Signature and Life-Skills courses through School for Life. To round off this learning experience, students are required to do mandatory internships in the social sector, government/public sector, and industry. The combinations available for students to pick and choose from are endless, ensuring both depth and width of knowledge.

Details

PEO1

Graduates will demonstrate technical competency and leadership to become professional scientists leading to a successful careers.

PEO2

The graduate will have the leadership qualities to handle all kinds of circumstances in diversities by providing an interdisciplinary and multidisciplinary learning environment.

PEO3

Graduates will continuously learn and adopt new skills and techniques to overcome problems related to new technologies.

PEO4

The graduate will be able to formulate, investigate and analyze scientifically real-life problems along with an ethical attitude, which works in a multidisciplinary team.

PSO1

Perform system and application programming using computer system concepts, concepts of Data Structures, algorithm development, problem-solving and optimizing techniques.

PSO2

Apply computational knowledge to assess, design, propose and develop analytical systems to solve real-world problems.

The B. Sc. Computer Science Specialization in Data Analytics prepares the students to be suitable for various roles in the industry as follows

  • Data Analyst.
  • Software Programmer.
  • Business Analyst
  • Data Architect
  • Statistician
  • Automation Engineer.
  • Data Engineer.
  • Simulation architect.

 

The curricula for the program have been framed in consultation with industry, academia, alumni, and parents to make the students industry ready for graduation.

First year:  Understanding computer systems, coding languages, data structures, algorithms and databases and studying problem-solving and troubleshooting.

Second year: Understanding of administering Windows and Linux OS, fundamentals of data integration and machine learning along with specialized tools to help develop knowledge of different aspects of Artificial Intelligence and data processing.

Final Year: the main domain skills in ANN and Deep Learning with the associated tools are taught as well as the students are given hands-on practice in their major and minor projects.

The domains covered in these years will influence and support the final year project module and engage with the industry to work on a real-life project.

 

SEMESTER I

 

 

SEMESTER II   

 

 

Subject Code

Subject

Credits

Subject Code

Subject

Credits

 

Mathematical Sciences

3

 

 Discrete Structures & Theory of Logic

3

 

Digital Electronics and Logic Design

3

 

Formal Languages and Automata Theory

2

 

Introduction to OS (with Linux) L(2+2)

4

 

Data Structures and Algorithms L

4

 

Probability and Statistical Inference

3

 

Introduction to C Programming L

4

 

Fundamentals of Computer Engineering

3

 

Introduction to Database L

4

 

Environmental Science

2

 

Leadership and Teamwork

2

SLLS 0101

Living Conversations

2

 

Critical Thinking and Writing

3

SLLS 0102

Learning how to learn

2

 

 

 

 

TOTAL

22

 

TOTAL

22

SEMESTER III

 

 

SEMESTER IV   

 

 

Subject Code

Subject

Credits

Subject Code

Subject

Credits

 

Linear Algebra & Combinatorics

3

 

Introduction to SWEng

3

 

Introduction to Python L

4

 

AI & Machine Learning

3

 

Advanced-Data Structure and Algorithms Lab

3

 

Data Mining and Predictive Analysis

3

 

Introduction to Networking L

4

 

Visual Analytics (Explorative data analysis)

3

 

Data Integration and Warehousing L

4

 

Introduction to Statistical ML (Regression, Clustering, Classification)

2

 

Sampling Distributions and Applications

3

 

NoSQL Database L

3

SLLS 0201

Design Thinking

2

SLLS 0202

Working with Data

2

SLLS 2001

Social Internship

0

SLSG 0201

Ethical Leadership in the 21st Century

3

 

TOTAL

23

 

TOTAL

22

SEMESTER V

 

 

SEMESTER VI

 

 

Subject Code

Subject

Credits

Subject Code

Subject

Credits

 

Cloud & DevOps Solutions

3

 

Social Network Analysis

2

 

Introduction to Optimization Techniques (math course)

3

 

Time Series & Forecasting Methods

3

 

Big Data Analytics

3

 

Advanced-Data Analytics Platforms(Big Query, Spark, AWS EMR) L

4

 

Data Analytics application in Industry (case studies)

3

 

Seminar

1

 

Project-I

4

 

Project-II

8

SLLS 0301

Persuasive Presence

2

Signature 5

Choose Anyone

3

SLSG 0306

Environment and Sustainability - Himalaya Fellowship

3

SLSG 0302P

Solving Complex Problems

 

 

 

 

SLSG 0303P

Technologies of the Future

 

 

 

 

SLSG 0304P

Future Casting

 

 

 

 

SLSG 0305P

Managing Relationships and Being Happy

 

 

TOTAL

21

 

TOTAL

21

Total Credits of B.Sc. -CS Cyber Security and Forensics (2023-2026)

 

 

 

131

50% marks in class X and XII with Mathematics/ Computer Science/ Information Technology as one of the major Subject in Class XII.

Personal Interview

We at the department believe in experiential teaching and learning and strive to imbibe problem-solving skills and critical thinking in the students. The following practices are in place to improve the quality of Teaching-Learning and student experience:

  • Design and Review of individual Course Plans at the beginning of the session
  • Course Completion Report (CCR)
  • Academic Planning & Monitoring
  • Real-world/Project based assignments
  • Encouraging Advanced Learner
  • Slow Learners Support
  • ICT-enabled Classroom (sound system, mic and projector)
  • Guest lectures from industry experts
  • Certification Courses
  • Professional Software Training (PST) and Certification
  • NPTEL lectures
  • Participation in competitive events (In-house/National/International).
  • Participation in Conferences/Seminars/Workshops (National/International).

Details

PROGRAM EDUCATIONAL OBJECTIVES (PEOs)

PEO1

Graduates will demonstrate technical competency and leadership to become professional scientists leading to a successful careers.

PEO2

The graduate will have the leadership qualities to handle all kinds of circumstances in diversities by providing an interdisciplinary and multidisciplinary learning environment.

PEO3

Graduates will continuously learn and adopt new skills and techniques to overcome problems related to new technologies.

PEO4

The graduate will be able to formulate, investigate and analyze scientifically real-life problems along with an ethical attitude, which works in a multidisciplinary team.

PROGRAM SPECIFIC OUTCOMES (PSOs)

PSO1

Perform system and application programming using computer system concepts, concepts of Data Structures, algorithm development, problem-solving and optimizing techniques.

PSO2

Apply computational knowledge to assess, design, propose and develop analytical systems to solve real-world problems.

Career Prospects

The B. Sc. Computer Science Specialization in Data Analytics prepares the students to be suitable for various roles in the industry as follows

  • Data Analyst.
  • Software Programmer.
  • Business Analyst
  • Data Architect
  • Statistician
  • Automation Engineer.
  • Data Engineer.
  • Simulation architect.

 

The curricula for the program have been framed in consultation with industry, academia, alumni, and parents to make the students industry ready for graduation.

Curriculum

First year:  Understanding computer systems, coding languages, data structures, algorithms and databases and studying problem-solving and troubleshooting.

Second year: Understanding of administering Windows and Linux OS, fundamentals of data integration and machine learning along with specialized tools to help develop knowledge of different aspects of Artificial Intelligence and data processing.

Final Year: the main domain skills in ANN and Deep Learning with the associated tools are taught as well as the students are given hands-on practice in their major and minor projects.

The domains covered in these years will influence and support the final year project module and engage with the industry to work on a real-life project.

 

SEMESTER I

 

 

SEMESTER II   

 

 

Subject Code

Subject

Credits

Subject Code

Subject

Credits

 

Mathematical Sciences

3

 

 Discrete Structures & Theory of Logic

3

 

Digital Electronics and Logic Design

3

 

Formal Languages and Automata Theory

2

 

Introduction to OS (with Linux) L(2+2)

4

 

Data Structures and Algorithms L

4

 

Probability and Statistical Inference

3

 

Introduction to C Programming L

4

 

Fundamentals of Computer Engineering

3

 

Introduction to Database L

4

 

Environmental Science

2

 

Leadership and Teamwork

2

SLLS 0101

Living Conversations

2

 

Critical Thinking and Writing

3

SLLS 0102

Learning how to learn

2

 

 

 

 

TOTAL

22

 

TOTAL

22

SEMESTER III

 

 

SEMESTER IV   

 

 

Subject Code

Subject

Credits

Subject Code

Subject

Credits

 

Linear Algebra & Combinatorics

3

 

Introduction to SWEng

3

 

Introduction to Python L

4

 

AI & Machine Learning

3

 

Advanced-Data Structure and Algorithms Lab

3

 

Data Mining and Predictive Analysis

3

 

Introduction to Networking L

4

 

Visual Analytics (Explorative data analysis)

3

 

Data Integration and Warehousing L

4

 

Introduction to Statistical ML (Regression, Clustering, Classification)

2

 

Sampling Distributions and Applications

3

 

NoSQL Database L

3

SLLS 0201

Design Thinking

2

SLLS 0202

Working with Data

2

SLLS 2001

Social Internship

0

SLSG 0201

Ethical Leadership in the 21st Century

3

 

TOTAL

23

 

TOTAL

22

SEMESTER V

 

 

SEMESTER VI

 

 

Subject Code

Subject

Credits

Subject Code

Subject

Credits

 

Cloud & DevOps Solutions

3

 

Social Network Analysis

2

 

Introduction to Optimization Techniques (math course)

3

 

Time Series & Forecasting Methods

3

 

Big Data Analytics

3

 

Advanced-Data Analytics Platforms(Big Query, Spark, AWS EMR) L

4

 

Data Analytics application in Industry (case studies)

3

 

Seminar

1

 

Project-I

4

 

Project-II

8

SLLS 0301

Persuasive Presence

2

Signature 5

Choose Anyone

3

SLSG 0306

Environment and Sustainability - Himalaya Fellowship

3

SLSG 0302P

Solving Complex Problems

 

 

 

 

SLSG 0303P

Technologies of the Future

 

 

 

 

SLSG 0304P

Future Casting

 

 

 

 

SLSG 0305P

Managing Relationships and Being Happy

 

 

TOTAL

21

 

TOTAL

21

Total Credits of B.Sc. -CS Cyber Security and Forensics (2023-2026)

 

 

 

131

Eligibility

50% marks in class X and XII with Mathematics/ Computer Science/ Information Technology as one of the major Subject in Class XII.

Selection Criteria

Personal Interview

INNOVATIVE TEACHING AND LEARNING

We at the department believe in experiential teaching and learning and strive to imbibe problem-solving skills and critical thinking in the students. The following practices are in place to improve the quality of Teaching-Learning and student experience:

  • Design and Review of individual Course Plans at the beginning of the session
  • Course Completion Report (CCR)
  • Academic Planning & Monitoring
  • Real-world/Project based assignments
  • Encouraging Advanced Learner
  • Slow Learners Support
  • ICT-enabled Classroom (sound system, mic and projector)
  • Guest lectures from industry experts
  • Certification Courses
  • Professional Software Training (PST) and Certification
  • NPTEL lectures
  • Participation in competitive events (In-house/National/International).
  • Participation in Conferences/Seminars/Workshops (National/International).
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