B.Sc. Data Science and Analytics
The 4-year B.Sc. (Hons) Data Science and Analytics programme, under CBCS Scheme, enables students to engage in the domains of data mining, analysis, and predictive modelling. The programme’s curriculum has more of application-oriented learning and the curriculum mainly stresses on the mastery of basic mathematics, statistics, and computer science. The emphasis of the programme is to expose students to advanced concepts, developments, and techniques in the realm of data analytics.
The programme is developed by the members of the faculty based on interactions with various universities, financial institutions, and industries. The curriculum is outcome based and it imbibes required theoretical concepts and practical skills in the domain. By undergoing this programme, students develop application-oriented learning skills, critical, analytical thinking, and problem-solving abilities for a smooth transition from academic to real-life work environment.
Activities
B.Sc. Data Science & Analytics: Syllabus
To give you a full insight into the degree and the related subjects, we have prepared a complete list of the cognate subjects which are usually included in the academic curriculum of B.Sc. Data Science & Analytics.
| Semester | Major | Minor | MDC | SEC |
| 1 | Data Structure and Program Design using C (P) | Introduction to Computer Organization | Computer Organization & Operating Systems | Data Analysis using EXCEL (P) |
| 2 | Working with Python (P)
|
Introduction to Python (P) | Basic Statistics & Probability | Applied Analytics using R (P) |
| 3 | Database Management Systems using MongoDB (P) | Introduction to DBMS (P) | Applied Mathematics
|
Research Methodology |
| Object Oriented Programming using Java(P) | ||||
| 4 | Design and Analysis of Algorithms | Data and Web Mining | ||
| Web Application Development (P) | ||||
| Data Mining and Warehousing | ||||
| 5 | Big Data Analytics | Introduction to Data Science | ||
| Machine Learning (P) | Business Intelligence | |||
| 6 | Data Science using Python (P) | Data Visualization (P) | ||
| Deep Learning | Cyber Security | |||
| Cryptography & Cyber Security | ||||
| 7 | Cloud Computing | Introduction to Cloud Computing | ||
| Data Visualization (P) | ||||
| Operations Research and Optimization Techniques | ||||
| 8 | Data Science in Industry and Real-World Applications | Data Centric Business Models | ||
| Capstone Projects |
Career Scope after completing B.Sc. Data Science & Analytics
In the present era, data-driven decisions are essential for the success of any organization and this gap needs to be filled. Data Science & Analytics is often clubbed with artificial intelligence to extract better results. Therefore, the future is anticipated to be glowing for Data Scienece & Analytics degree holders. There is a wide range of options on the table, the following are the top-paying ones:
- Data Scientist
- Process Analyst
- Data Solutions Analyst
- Web & Social Media Analyst
- CRM Analyst
- Research Analyst
- Business Intelligence Analyst
- Analytics Manager
- Data Engineer
- Data Architect, and many others.
Vocational Certificate Add-on Course
Data Analytics Using R Programming
[PWCACC DAR]
| 📚 Credits
6 Credits (90 Hours) |
📋 Modules
4 Core Modules |
🎯 Assessment
Theory 40% | Projects 60% |
Course Overview
This Vocational Certificate Add-on Course is designed to introduce students to the world of data analytics using R programming. The course provides a comprehensive understanding of data manipulation, visualization, and statistical analysis. Through hands-on projects and assignments, students gain practical experience and develop the skills necessary to analyse real-world data effectively.
Programme Outcomes
By the end of this programme, students will be able to:
- Demonstrate a solid understanding of data analytics concepts and techniques.
- Apply R programming skills to manipulate and analyse data.
- Create meaningful data visualizations to communicate insights.
- Conduct statistical analyses and interpret results.
- Develop and present data-driven projects.
Course Outcomes
Upon successful completion, students will be able to:
- Understand the basics of R programming and its applications in data analytics.
- Perform data manipulation and cleaning using R.
- Create and interpret data visualizations using ggplot2.
- Conduct basic statistical analyses and hypothesis testing.
- Discuss real-world case studies and analyse the findings.
Syllabus
Total: 6 Credits = 90 Hours | 1 Credit = 15 Hours
Module 1: Introduction to R Programming (20 Hours)
- Overview of Data Analytics
- Introduction to R and RStudio
- Basic R Syntax and Data Types
- Data Structures in R (Vectors, Matrices, Lists, Data Frames)
- Importing and Exporting Data
Module 2: Data Manipulation and Cleaning (20 Hours)
- Data Manipulation using dplyr
- Data Cleaning Techniques
- Handling Missing Data
- Data Transformation
- Working with Dates and Times
Module 3: Statistical Analysis and Data Visualization (25 Hours)
- Descriptive Statistics
- Inferential Statistics and Hypothesis Testing
- Regression Analysis
- Fundamentals of Data Visualization Techniques
- Basic Plotting with ggplot2
Module 4: Case Studies in Data Analytics (25 Hours)
- Real-world Data Analytics Projects
- Case Studies and Practical Applications
- Presentation and Discussion of Case Study Findings
Evaluation Criteria
| Module | Theory | Assignment | Practical (Project) | Viva | Total |
| Module I | 80 | 40 | 60 | 20 | 200 |
| Module II | |||||
| Module III | |||||
| Module IV |
Grading Mechanism
| Marks Range | Remarks | Grade |
| 75 – 100 | Excellent | A+ |
| 60 – 74 | Very Good | A |
| 50 – 59 | Good | B |
| 45 – 49 | Satisfactory | C |
| Less than 45 | Fail | D |
This course equips students with foundational skills in data analytics using R, preparing them for entry-level roles in the field and enabling them to tackle real-world data challenges with confidence.
