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B.Tech in Computer Science (Data Science)

B.Tech in Computer Science (Data Science)

School of Engineering and Technology (SET)

  • Program Code

    00001

  • Level

    Graduate

  • Duration

    4 Years

About the Programme

B. Tech in Computer Science with specialisation in Data Science (DS) is a new, exponentially growing field which consists of a set of tools and techniques used to extract useful information from data. The programme encompasses data Science as an interdisciplinary, problem-solving oriented subject that learns to apply scientific techniques to practical problems.
The course curriculum involves a blend of data inference, algorithm development, and technology to analytically solve complex problems. The programme imparts a confluence of skills in three major areas of mathematical expertise, technology hacking skills and business strategy and acumen. 
The core of this programme is the ultimate use of enormous data in creative ways to generate business value. Hidden insights are brought to the fore to enable companies to make smarter business decisions. Data Science programme orients on practical classes and self-study during the preparation of datasets and programming of data analysis tasks.

This course is for individuals who...

This programme is for students who are interested in learning about all aspects of uncovering facts from the available data. This programme is meant to mine out insights, so if you are a deep thinker with intense intellectual curiosity, and if adding value to understanding complex behaviours, trends and inferences excite you, this is the right choice. 

Students who are looking for...

a career with exceptional prospective fields and a challenging roles in a futuristic industry. The financial security, the freedom to relocate, the lifelong prospect of learning makes this programme an ideal career for computer science enthusiasts.

Course Fee
Fee Per Semester Fee Per Year
For National Students 92 500 1 79 000
For International Students 1950 3800
Course Structure

S.No.

Course Code

Course Title

L

T

P

Credits

1

CSP101

Introduction to Artificial Intelligence and Machine Learning

0

0

2

1

2

CSE113/CSP113

Programming for Problem Solving

3

0

2

4

3

SC4

Maths I(Bucket based)

3

1

0

4

4

SC8

Engineering Physics

2

1

2

4

5

SC12

Computer Aided Design & Drafting

0

0

3

1.5

6

SC11

Principles of Electrical and Electronics Engineering

2

1

2

4

7

SC16

Soft Skill - 1

0

0

4

2

 

Credits in Term 1

 

 

 

20.5

Syllabus for  the Courses:

Introduction to Computer Science and Engineering: This course focuses application areas of Computer Science and Engineering for students admitted in undergraduate program. The purpose of B. Tech. in Computer Science & Engineering is to be given through this course to students.

Programming for problem solving: Programming for problem solving gives the Understanding of C programming and implement code from flowchart or algorithm

S.No.

Course Code

Course Title

L

T

P

Credits

1

CSE114/CSP114

Application based Programming in Python

3

0

2

4

2

SC5

Maths II(Bucket based)

3

1

0

4

3

SC9

Advanced Physics (Bucket based)

2

1

2

4

4

SC10

Engineering Chemistry

3

0

2

4

5

SC13

Mechanical Workshop

0

0

3

1.5

6

SC17

Soft Skill - 2

1

0

2

2

7

CSP103

(Design/creativity) Multimedia Application Lab

0

0

2

1

 

Credits in Term 2

 

 

 

20.5

Application Based Programming in Python : Python is a language with a simple syntax and a powerful set of libraries. It is widely used in many scientific areas for data exploration. This course is an introduction to the Python programming language for students without prior programming experience. We cover data types control flow object-oriented programming.

Multimedia and Animation Lab: This course is an overview of the modern technologies used for the Web development.

S.No.

Course Code

Course Title

L

T

P

Credits

1

SC18

CTS-1 Building Essential Language and life skills

0

0

4

2

2

SC14

Introduction to Biology for Engineers

2

0

0

2

3

MTH201

Discrete Structures

3

1

0

4

4

CSE247

Computer Organization and Architecture

3

0

0

3

5

CSE242/CSP242

Data Structures Using C

3

0

2

4

6

CSE243/CSP243

OOPS Using Java

3

0

2

4

7

PC3

Project Based Learning (PBL) -1

0

0

2

1

8

 

Industrial Internship

0

0

2

1

 

CSD201

Applied Stsistical Analysis

3

0

0

3

 

Credits in Term 3

 

 

 

24

Object Oriented Programming Using JAVA: Basic  Object Oriented Programming  (OOP) concepts including objects   classes methods parameter passing information hiding inheritance and polymorphism are introduced and their implementations using Java  are discussed.

Data Structures: This course  starts with an introduction to data structures with its classification efficiency of different algorithms array and pointer based implementations and Recursive applications. As the course progresses the study of Linear and Non-Linear data structures are studied in details. The course talks primarily about Linked list stacks queue Tree structure Graphs etc. This Course also deals with the concept of searching sorting and hashing methods.

Computer Organization and Architecture: This course discusses the basic structure of a digital computer and used for understanding the organization of various units such as control unit Arithmetic and Logical unit and Memory unit and I/O unit in a digital computer.

Applied Statistical Analysis: This graduate level course provides an introduction to the basic concepts of probability common distributions statistical methods and data analysis.

S.No.

Course Code

Course Title

L

T

P

Credits

1

SC19

CTS-2 Communicate to conquer

0

0

4

2

2

CSE244/CSp244

Principles of Operating System

3

0

2

4

3

CSE245/CSP245

Computer Networks

3

0

2

4

4

CSE246/CSP246

Data Base Management System

3

0

2

4

5

SC23

Management Course (from basket)

3

0

0

3

6

PC14

Project Based Learning (PBL) -2

0

0

2

1

7

SC7

Environmental Science

2

0

0

2

8

CSP201

Advance Java Lab

0

0

2

1

 

CSD202

Data Acquisition

3

0

0

3

 

Credits in Term 4

 

 

 

24

Computer Network: To familiarize with the basic taxonomy and terminology of computer networking area.

Data Base Management systems: This course introduces database design and creation using a DBMS product. Emphasis is on normalization data integrity data modeling and creation of simple tables queries reports and forms. Upon completion students should be able to design and implement normalized database structures by creating simple database tables queries reports and forms.

Principles of Operating system: This course introduces the design principles of operating systems resource management identifying challenges and applying respective algorithms.     

  Advance Java Programming Lab: This course is designed to meet the needs of those who want to be professional Java developers. Students should be familiar with Java programming techniques and should be comfortable with concepts such as Classes Objects Inheritance Interfaces I/O Streams Threading and Networking.

Data Acquisition: Major topics covered in this subjects are data acquision processs managing data Graphical representation of data Data Aggregation Group Operations Time series Visualization of data   Data Productization IoT   and Virtualization on Embedded Boards IoT.

S.No.

Course Code

Course Title

L

T

P

Credits

1

SC20

CTS-3 Impress 2 Impact

0

0

4

2

2

CSE341/CSP341

Design and Analysis of Algorithm

3

0

2

4

3

CSE342

Theory of Computation

3

1

0

4

4

CSE343

Software Engineering and Testing Methodologies

3

0

0

3

5

PE1

Program Elective-1

3

0

0

3

6

CSP302

Technical Skill Enhancement Course-1(LINUX Programming Lab)

0

0

2

1

7

PC15

Project Based Learning (PBL) -3

0

0

2

1

8

OE1

Open Elective - 1

3

0

0

3

9

 

Industrial Internship-II

0

0

2

1

10

CSD301

  Data Warehouse

3

0

0

3

 

Credits in Term 5

 

 

 

25

Theory of computation: The course introduces some fundamental concepts in automata theory and formal languages including grammar finite automaton regular expression formal language pushdown automaton and Turing machine. Not only do they form basic models of computation they are also the foundation of many branches of computer science e.g. compilers software engineering concurrent systems etc. The properties of these models will be studied and various rigorous techniques for analyzing and comparing them will be discussed by using both formalism and examples.

Software Engineering and Testing Methodologies: The objective of this course is to provide fundamental knowledge of software engineering and make student aware of best software engineering practices and contemporary software engineering tools.

  Linux Programming Lab: The course is designed to make the students research/industry ready as by using the open source applications along with any of the Linux flavor operating systems.

Design and Analysis of Algorithms: This course introduces concepts related to the design and analysis of algorithms. Specifically it discusses recurrence relations and illustrates their role in asymptotic and probabilistic analysis of algorithms. It covers in detail greedy strategies divide and conquer techniques dynamic programming and max flow - min cut theory for designing algorithms and illustrates them using a number of well-known problems and applications.

Introduction to Graph Theory and its Application: This course is to teach students the basic graph theory concepts and their applications in computer science.

Introduction Mathematical and Statistical Techniques: In this subject the fundamental concepts and principles of Mathematical & Statistical Techniques together with the challenging issues in Computer Science software development will be introduced.  Discussion on various topics related to mathematics and Computer Science will also be conducted. 

Data Warehouse: This course introduces advanced aspects of data warehousing encompassing the principles to analyze the data identify the problems and choose the relevant models and algorithms to apply.

S.No.

Course Code

Course Title

L

T

P

Credits

1

SC21

CTS-4 Ace the Interview

0

0

4

2

2

CSE344/CSP344

Compiler Design

3

0

2

4

3

CSE346/CSP346

Artificial Intelligence

3

0

2

4

4

CSP308

Technical Skill Enhancement Course-2(Statistical Analysis Lab)

0

0

2

1

5

PE2

Program Elective-2

3

0

0

3

6

PE3

Program Elective-3

3

0

0

3

7

PC16

Project Based Learning (PBL) -4

0

0

2

1

8

OE2

Open Elective - 2

3

0

0

3

 

CSD302

  Data Mining

3

0

2

4

 

Credits in Term 7

 

 

 

25

Compiler Design: To provide students with an overview of the issues that arise in Compiler construction as well as to throw light upon the significant theoretical developments and tools that are deep rooted into computer science.

Artificial Intelligence: This course introduces basic aspects of Artificial intelligence comparing the AI and conventional solutions to real world problems utilizing and analyze AI techniques for identifying optimal solutions to search strategies.

  Introduction to Cloud Computing: This course introduces advanced aspects of Cloud Computing encompassing the principles to analyze the cloud identify the problems and choose the relevant models and algorithms to apply.

Business Process Management (BPM) : Business Process Management (BPM) is a management discipline concerned with lifting an organization s performance through improvement management and control of business processes. It encapsulates methods techniques and software involved throughout all stages of the process lifecycle including analysis design enactment and control.

Social Media Analytics

Social Media Analytics is the science of analyzing data to convert information to useful knowledge. This knowledge could help us understand our world better and in many contexts enable us to make better decisions. 

Data Mining:   This course introduces advanced aspects of data warehousing and data mining encompassing the principles to analyze the data identify the problems and choose the relevant models and algorithms to apply.

S.No.

Course Code

Course Title

L

T

P

Credits

1

PE4

Program Elective-4

3

0

0

3

2

CSE458/CSP458

Web Technologies

3

0

2

4

3

SC26

Major Project- 1

0

0

6

3

4

SC22

Comprehensive Examination

0

0

0

0

5

SC28

Professional Ethics and Values

0

0

0

0

6

SC25

Industrial Internship

0

0

2

1

7

OE3

Open Elective - 3

2

0

0

2

8

 

CTS-5 Campus to corporate

0

0

2

1

9

CSD401

Business Intelligence

3

0

0

3

 

Credits in Term 7

 

 

 

17

Web Technology:   The purpose of this course is to give students the basic understanding of how different computers and devices to communicate and share resources as well as to give the basic overview of the different technologies.

Introduction to Deep Learning: This course introduces neural computational paradigm for critical & implementable understanding for automated learning based problem areas.

Business Intelligence: This course Have an overall understanding of the major issues and applications in business intelligence including a basic grasp of the algorithm classes and best practices for building successful BI projects.

S.No.

Course Code

Course Title

L

T

P

Credits

1

SC27

Major Project - 2

0

0

18

9

2

PE6

Program Elective-5

3

0

0

3

3

PE5

Program Elective-6

3

0

0

3

4

OE4

Open Elective - 4

3

0

0

3

5

 

Universal Human Value & Ethics

2

0

0

2

 

CSD402

Big Data Analytics

3

0

2

4

 

Credits in Term 8

 

 

 

24

Program Elective List:

PE-1

PE-2

PE-3

PE-4

PE-5

PE-6

                                                                                                                                                                  Introduction to Mathematical & Statistical Techniques in Computer Science                                                CSE348                             

Introduction to Cloud Computing  CSE351

Scocial Media Analytics CSD304

Introduction to Deep Learning CSA407

Web and Text    CSD403

Introduction to Internet of Things CSI201

Introduction to Graph Theory and its Applications CSE349

Business Process Management CSD303

 

 

 

Cluster Computing CSD404

Total Credits: __180___

Minimum Credits essential for the Programme:__180___

Eligibility Criteria
For National Students
  • Matriculation with-60% marks Sr. Secondary (10+2) 70% marks (Aggregate) & minimum 60% marks in PCM Computer Science/PCB for Biotechnology without gap between 10th and 12th.
  • Minimum 50% marks in Maths

+

  • SUAT (Online Test) Followed by Personal Interview

Or

  • JEE All India Rank upto 3 Lakh

Or

  • UPSEE All India Rank upto 50 000
For International Students The eligibility criterion for all programs for international applicants is minimum 50% in the qualifying examination and having studied the pre-requisite subjects for admission in to the desired program.
Career path you can choose after the course
  • Database Administrators
  • Data Architects
  • Data Visualizers
  • Data Engineers
  • Data Ecologists 
  • Data Science Consultant
  • Financial Modeler
  • Clinical and Pharmaceutical Analyst
  • Data Technologies Specialist
  • Amazon
  • CapGemini
  • Mphasis 
  • NTTData 
  • Quickheal 
  • SYNTEL 
  • TCS 
  • Vivo Mobile 
  • Wipro

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