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22 January 2024

Comparing Software Engineering and Data Science in Engineering

Engineering is the application of science to amend the problems of real-life world problems that make day-to-day life a smoother process. Software engineering and Data Science engineering are two engineering branches of the new world. Software engineering is the field of science that deals with the systematic approach to the development, maintenance, and innovation of software. The course of B-Tech Software Engineering is designed to equip students to use various programming and software technologies to create scalable and reliable applications for the advancement of the world.

Data Science Engineering is a highly integrated field with a flexible approach to its purpose with respect to the industry chosen to work with. In short, Data Science Engineering helps with meaningful and adequate extraction of data and analysis of metrics with the respective requirement. The curriculum of Data Science Engineering is comprehensive after considering the industry's emerging needs and demand.

Software Engineering vs. Data Science Engineering: Course Duration and Eligibility

Software Engineering and Data Science Engineering are professional courses and the curriculum is designed for a span of 4 years. These four years are inclusive of theoretical classes, practical classes, internships, projects, and industrial visits.

The minimum eligibility criteria for enrolling in Software Engineering is passing Grade 12 or equivalent studies as accepted by the University with a percentage falling under the cut-offs.
The minimum eligibility criteria for enrolling in Data Science Engineering is passing Grade 12 or equivalent studies as accepted by the University with a percentage falling under the cut-offs.

Getting qualified for national engineering entrance exams like JEE is a plus criterion when considering the application for admissions.

Software Engineering vs. Data Science Engineering: Course Curriculum

The course Curriculum for the Bachelor of Technology in Software Engineering and Data Science Engineering is enhanced and extensive.

Below are the subjects learned throughout the course duration:

Software Engineering Course Curriculum:

Semester I

  • Soft Skills I
  • Calculus and Solid Geometry
  • Physics
  • Physics Lab
  • Chemistry
  • Chemistry Lab

Semester II

  • Soft Skills II
  • Advanced Calculus and Complex Analysis
  • Material Science
  • Principles of Environmental Science
  • Programming using C AND C++

Semester III

  • Language
  • Aptitude -I
  • Transforms and Boundary Value Problems
  • Computer Organisation & Architecture
  • Data Structures & Algorithms
  • Software Engineering
  • Programming using JAVA
  • Data Structures & Algorithms LAB
  • JAVA Programming Lab

Semester IV

  • Language
  • Aptitude II
  • Probability and Queuing Theory
  • Principles of Operating System and Compiler
  • Computer Networks
  • Software Architecture
  • Software Design
  • Software Project Management
  • Dep. Elective –I

Semester V

  • Aptitude III
  • Discrete Mathematics
  • Data Base Management Systems
  • Cloud Computing
  • Software Testing
  • Software Measurements and Metrics
  • Industrial Training
  • Dep. Elective -II
  • Open Elective I

Semester VI

  • Aptitude IV
  • Web Programming
  • Analysis of Software Artefacts
  • Software Quality Management
  • Software Maintenance and Administration
  • Minor Project
  • Dep. Elective III
  • Open Elective II
  • Open Elective III

Semester VII

  • Service Oriented Architecture
  • Software Process Maturity Models
  • Agile Software Process
  • Industrial Training II (Training to be undergone after VI semester) -
  • Dep. Elective IV
  • Dep. Elective V

Semester VIII

  • Major Project/ Practice School

Data Science Engineering Course Curriculum

Semester I

  • Professional English and Soft Skills
  • Matrices and Calculus
  • Engineering Physics/Engineering Materials
  • Problem-Solving Using C
  • Introduction to Digital Systems / Engineering and Design
  • Engineering Immersion Lab

Semester II

  • Analytical Mathematics
  • Engineering Physics/ Engineering Materials
  • Engineering Graphics and Computer-Aided Design
  • Engineering and Design
  • Sustainable Engineering Systems
  • Data Structures
  • Python for Data Science
  • Engineering Immersion Lab
  • Engineering Physics Lab/ Materials Chemistry Lab

Semester III

  • Applied Linear Algebra
  • Design and Analysis of Algorithms
  • Database Management Systems
  • Java Programming
  • R for Data Science
  • Department Elective-I
  • Non-Department Elective- I
  • Database Management Systems Lab

Semester IV

  • Discrete Mathematics
  • Digital Marketing Analytics
  • Data Wrangling
  • Data Handling and Visualisation
  • Department Elective-II
  • Non-Department Elective–II
  • Data Wrangling Lab
  • Data Handling and Visualisation lab
  • Design Project-I
  • Internship

Semester V

  • Probability and Statistics
  • Business Intelligence and Analytics
  • Predictive Modeling and Analytics
  • Artificial Intelligence
  • Professional Ethics and Life Skills
  • Department Elective-III
  • Non-Department Elective–III
  • Business Intelligence and Analytics Lab
  • Design Project with IoT

Semester VI

  • Software Project Management
  • Machine Learning
  • Data Warehousing and Data Mining
  • Modern Software Engineering
  • Business Economics
  • Department Elective-IV
  • Non-Department Elective–IV
  • Data Mining Tools Lab

Semester VII

  • Text Analytics and Natural Language Processing
  • Big Data and Analytics
  • Time series analysis and Forecasting
  • Deep Learning
  • Department Elective–V
  • Non-Department Elective-V
  • Real-time Case Study Lab
  • Design Project-III

Semester VIII

  • Project & Viva – voce

Software Engineering vs. Data Science Engineering: Career Opportunities

Software engineering as well as Data Science Engineering, these professional courses are booming and promising multiple career opportunities. Listed below are a few among the numerous career opportunities available:

Software Engineering Career Opportunities:

  • Video game designer: Incorporates the knowledge of software, computer science, graphic designing, and storytelling to create an engaging narrative-based video game
  • SQA engineer: Involves with evaluating and testing software to ensure it meets the quality standards and other specifications
  • Cyber security engineer: Designs and crafts secure network solutions to protect data against cyber attacks, hackers, and other data invasion threats
  • Applications Engineer: Engages with understanding the requirements, designing and improvising the software through multiple revisions and quality checking
  • Software project manager: Understands the client needs and assigns the software projects to various teams and guides them over the same
  • Software test engineer: Works with newly created software and applications and runs trial runs and tests to ensure the proper work and accuracy of the software

Data Science Engineering Career Opportunities:

  • Data analyst: Involves in gathering the data and interpreting the results to solve a specific or target problem faced by the venture
  • Data Engineer: Analyses and drafts data for operational usage of the enterprise
  • Business intelligence: Analyses a set of business strategies and converts it to actionable with impact (for boosting revenue generation)
  • Data Architect: Visualises and crafts an organisation’s data framework
  • Software Developer: Creates and crafts computer and mobile applications that the clients require as per their needs

In Short

After having gone through the course curriculum and other details of both subjects, it is understood that software engineering and data science engineering are two distinct in themselves but with certain cross-overs in specific areas. Both these professional courses are career-promising and in high demand in the industry for the pivotal role played by technology in almost all industries these days. The continuous progress and evolution of technology as a science and sector is indeed an insight into the subject’s existence in the world from a future perspective.