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B.Sc Data Science is a three-year-long course that is aimed to construct the means for extracting business-focused information from data. It requires an understanding of how value flows in a business. The course is divided into both core and elective subjects making the course flexible and diverse.
The core subjects of BSc Data Science include Linear Algebra Probability and Inferential Statistics, Basic Statistics, Discrete Mathematics, Programming in C, Introduction to Data Science, Introduction to Data Structures and Analytics. The top recruiting companies for BSc Data Science graduates are Cartesian Consulting, Publicis Sapient, Tredence Inc., MuSigma, IBM, Amazon, TEG Analytics, Deloitte, HCL, GlobalAnalytics, etc.
BSc Data Science Course Details
| Course Name | BSc Data Science |
| Course Level | Graduation |
| Duration | 3 Year/ 4 Years |
| Admission Process | Through Common Entrance Test (CET) conducted at University Level. |
| Top Entrance Exam | SSU CET, AMET CET, Jain University Entrance Test, KR Mangalam Entrance Test |
| Eligibility | Class 12 with Science from a recognized board |
| Top Colleges | Navrachana University, Sri Sri University, Symbiosis Skill and Open University, NSHM- Knowledge Campus, Academy of Maritime Education and Training (AMET) |
| Average Fees | Under 6 Lakhs |
BSc Data Science Syllabus
| Semester I | Semester II |
|---|---|
| Linear Algebra | Probability and Inferential Statistics |
| Basic Statistics | Data Structures and Program Design in C |
| Programming in C | Computer Organization and Architecture |
| Communication Skills in English | Advanced Python Programming for Spatial Analytics |
| Fundamentals of Data Science | Discrete Mathematics |
| Introduction to Geospatial Technology | Machine Learning |
| Python Programming | Image Analytics |
| Semester III | Semester IV |
| Programming in C Lab | Data Warehousing and Multidimensional Modeling |
| Microsoft Excel Lab | Data Structure Lab |
| Research Proposal | Research Publication |
| Natural Language Processing | Exploratory Data Analysis |
| Genomics | Programming in R Lab |
| Semester V | Semester VI |
| Machine Learning II | Elective I |
| Big Data Analytics | Elective II |
| Data Visualizations | Grand Viva |
| Programming in Python Lab | Major Project |
| Introduction to Artificial Intelligence | - |
BSc Data Science Subjects
Students need to learn the subjects in order to study the different subject of BSc Data Science. The subjects are mentioned below:
BSc Data Science First Year Subjects
- Linear Algebra: Linear Algebra is related to the mathematical structures closed under the operations of addition and scalar multiplication. It includes the theory of systems of linear equations, determinants, linear transformations, etc.
- Probability and Inferential Statistics: Inferential statistics is based on the probability of a certain outcome by chance. Here, outcome refers to the result observed.
- Basic Statistics: The basics of statistics include the measure of central tendency and the measure of dispersion. Mean, median and mode are the central tendencies and dispersions include variance and standard deviation.
- Data Structures and Program Design in C: Data Structures in C are used to store data in an organized manner. C Programming language has many data structures such array, stack, linked list, tree, etc.
- Computer Organization and Architecture: Computer organization studies the internal working, and implementation of a computer system. Architecture in the computer system refers to the external visual attributes of the system.
BSc Data Science Second Year Subjects
- Data Warehousing and Multidimensional Modeling: It represents data in the form of data cubes. It is defined by dimensions and facts.
- Natural Language Processing: Natural language processing is concerned with the interactions between computers and human language. It means how to program computers to analyze large amounts of natural language data.
- Genomics: Study of the total or part of the genetic and epigenetic sequence information of organisms is known as genomics. It understands the structure and function of the sequences and of downstream biological products.
BSc Data Science Third Year Subjects
- Machine Learning II: Machine Learning teaches computers to learn from experience. It uses computational methods to learn information directly from data and not rely on the predetermined equation.
- Big Data Analytics: It describes the process of uncovering patterns, and correlations in large amounts of raw data which in turn help make data-informed decisions.
- Data Visualizations: The graphical representation of information and data is data Visualization. It uses visual elements like charts, graphs, and maps.
- Programming in Python Lab: Programming in Python Lab, students learn and practice basic python programming. They expand their skillset by learning and solving basic python problems.
IIT Madras BSc Data Science syllabus
| Semester I | Semester II |
|---|---|
| English 1 | English 1 |
| Math 1 | Math 1 |
| Statistics 1 | Statistics 1 |
| Computational Thinking | Programming in Python |
| Semester III | Semester IV |
| Database Management Systems | Programming Concepts Using Java |
| Modern Application Development 1 | Modern Application Development 2 |
| Programming, Data Structures and Algorithms Using Python | Machine Learning Techniques |
| Machine Learning Foundations | Machine Learning Practice |
| Business Data Management | Business Analytics |
| Skill Enhancement 1 | Skill Enhancement 2 |
| Semester V | Semester VI |
| Core Courses | Core Courses |
| Elective Courses | Elective Courses |
| Strategies for Professional Growth | Skill Enhancement Courses |
| Skill Enhancement Courses | - |
BSc Data Science syllabus Mumbai University
| Semester I | Semester II |
|---|---|
| Descriptive Statistics | Probability and Distributions |
| Descriptive Statistics Practical | Probability and Distributions Practical |
| Introduction to Programming | DatabaseManagement |
| Introduction to Programming Practical | RProgramming |
| Web Technology | EnvironmentalScience |
| Web Technology Practical | Project Presentationon Data Sciencein Environmental Science |
| Business Communication and Information Ethics | Calculus |
| ICT Practical | - |
| Precalculus | - |
| PrecalculusTutorials | - |
| Semester III | Semester IV |
| Testing of Hypothesis | Optimization Techniques |
| SPSS Practical | Optimization Techniques Practical |
| Data Structures | Big Data |
| Data Structures Practical | ECommerce and Business Ethics/Fundamentals of Accounting |
| Microeconomics / Principles Of Management | MATLAB Practical |
| Case Studies on Microeconomics | Algorithmsin Data Science |
| Data Warehousing | Algorithmsin Data Science Practical |
| Linear Algebra and Discrete Mathematics | Numerical Methods |
| Tutorialson Linear Algebra and Discrete Mathematics | Numerical Methods Practical |
| Semester V | Semester VI |
| Artificial Intelligence | Machine Learning |
| Artificial Intelligence Practical | Machine Learning Practical |
| Business Research Methods | Cloud Computing |
| Business Research Methods Practical | Cloud Computing Practical |
| Data Mining | Internet of Things |
| Data Mining Practical | Internet of Things Practical |
| Campus to Corporate | Business Forecasting |
| Project Dissertation | Business Forecasting Practical |
| Electives | Electives |
| Data Visualisation with Power BI/Ta bleau | Project Implementation |
BSc Data Science Syllabus in Andhra University
| Semester I | Semester II |
|---|---|
| Maths for Data science | Introduction to Data science With R |
| Maths for Data science tutorial | R Programming Lab |
| Semester III | Semester IV |
| Big Data Technology | Data Mining and Data Analysis |
| Big Data Technology through Hadoop Lab | Data Mining and Data Analysis lab |
| - | Big data Acquisition and Analysis |
| - | Big data Acquisition and Analysis lab |
BSc Data Science in Osmania University
| Semester I | Semester II |
|---|---|
| Fundamentals of Information Technology | Problem solving and Python Programming |
| Fundamentals of Information Technology (Lab) | Problem solving and Python Programming (Lab) |
| Semester III | Semester IV |
| University Specified | University Specified |
| Mini Project | Mini Project |
| Data Engineering with Python | Machine Learning |
| Data Engineering with Python (Lab) | Machine Learning (Lab) |
| Semester V | Semester VI |
| Natural Language Processing | Big Data |
| No SQL Data Bases | Deep Learning |
| Natural Language Processing (Lab) | Big Data (Lab) |
| No SQL Data Bases (Lab) | Deep Learning (Lab) |
| Data Structures and Algorithms | Major Project |
BSc Data Science Teachings Methods
There are various techniques and methodology used for teaching BSc Data Science. The faculty of this department can teach with a mixture of traditional lectures, modern lectures, practical sessions, seminars and group discussions as well. Below are the teaching methodology and strategies:
- Lectures
- Practical Sessions
- Research Papers
- Seminars
- Group Discussions
- Internships
BSc Data Science Books
| Name of the Book | Author |
|---|---|
| Python for Data Analysis | Wes McKinney |
| Python Data Science Handbook | Jake VanderPlas |
| Understanding Machine Learning: From Theory to Algorithms | Shai Shalev-Shwartz and Shai Ben-David |
| R for Data Science | Hadley Wickham and Garret Grolemund |
BSc Data Science FAQs
Ques. What is BSc Data Science?
Ans. The BSc Data Science is three-year graduation course of an innovative interdisciplinary course designed with industry. This course is for those wishing to work/research in the data science sector.
Ques. What are the eligibility criteria for BSc Data Science?
Ans. Candidates who have passed 10+2 examination with the science stream and have a minimum aggregate of 55% from a recognized board are eligible to apply for BSc Data Science.
Ques. What are the job options after BSc Data Science?
Ans. Data Architect, Data Administrator, Business Intelligence Manager, Data Scientist, Data Analyst, and Data Architect are good job options for BSc Data Science.
Ques. What is the average salary of a BSc Data Science graduate?
Ans. Data Scientist salary in India from 1 year to 8 years of experience is between INR 4.5 LPA to INR 26 LPA, with an average annual salary of INR10.5 LPA
Ques. What are the core subjects of BSc Data Science?
Ans.
- Linear Algebra Probability and Inferential Statistics
- Basic Statistics
- Discrete Mathematics
- Programming in C
- Introduction to Data Science
- Introduction to Data Structures and Analytics
Ques. What are the electives in BSc Data Science?
Ans. Some of the electives in BSc Data Science:
- Reinforcement Learning
- Marketing and Retail Analytics
- Supply Chain and Logistics Analytics
- Financial Analytics
- HR Analytics
- Social Media Analytics
- Healthcare Analytics
- Nature Processing Analytics
Ques. What are the popular topics for BSc Data Science project?
Ans. Some of the popular topics for BSc Data Science project:
- Human Action Recognition
- Forest Fire Prediction
- Road Lane Line Detection
- Recognition of Speech Emotion
Ques. Is it worth doing BSc Data Science?
Ans. Yes, because Data Science Course is highly popular among students and in India as well as abroad there are a plethora job opportunities for B.Sc. Data Sciencea graduates. There is high demand for this course in tech companies, consultancies, and market research firms.
Ques. What are the top colleges for pursuing BSc Data Science?
Ans. Navrachana University, Sri Sri University, Symbiosis Skill and Open University, NSHM- Knowledge Campus, Academy of Maritime Education and Training (AMET) are some of the top colleges for BSc Data Science.
Ques. What are the areas of recruitment for B.Sc Data Science?
Ans. Banks, Research Firms, CRM Systems, Data Mining are the popular areas of recruitment for B.Sc Data Science graduates.
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