B.Sc – Data Science and Analytics
Turn Your Passion for Numbers into Global Impact
The BSc in Data Science and Analytics equips students with essential skills in data science, business analytics, and data visualization using advanced tools like Power BI and Tableau. The program emphasizes ethical data practices and compliance with data protection regulations, preparing graduates for roles in data-driven industries.
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Course Duration:
3 Years
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Course Fees:
₹2,53,600*
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Mode of Learning:
Online
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Medium of Instruction:
English
Students gain proficiency in statistical methods, machine learning algorithms, and programming languages like Python and SQL, along with expertise in managing large datasets and creating impactful visualizations. With a focus on problem-solving and critical thinking, graduates of the BSc Data Science course are well-prepared to tackle real-world challenges and make strategic, data-driven decisions across industries.
Key Benefits of Bachelor of Science in Data Science and Data Analytics
Expertise in Data Science Foundations
Graduates will master statistical methods, business analytics, and machine learning algorithms, alongside proficiency in programming languages such as Python and SQL for advanced data analysis.
Hands-on Experience with Modern Tools
Gain practical expertise in using tools such as Python, SQL, Hadoop, Spark, and cloud computing, along with real-world application of Power BI and Tableau for analytics challenges.
Problem-Solving and Critical Thinking
Develop the skills to address complex business problems using data science and analytics, creating strategic, data-oriented solutions for organizational growth.
Strategic Decision-Making Skills
Apply advanced analytics models to support strategic business decisions, preparing you to thrive in diverse organizational roles.
Program Highlights
- Live sessions by the prestigious and renowned IIIT Vadodara faculty
- An Esteemed Degree from IIIT Vadodara
- Hands-on experience with modern tools and technologies
- Flexible online learning environment
- Industry-aligned curriculum with practical applications
Program Objectives
- Gain expertise in statistical methods, business analytics, and machine learning, while becoming proficient in Python and SQL for advanced data analysis.
- Learn to handle large datasets, design databases, and use Power BI and Tableau to create dashboards that clearly communicate data insights.
- Apply advanced analytics models to guide business decisions and drive success across various industries.
- Sharpen your problem-solving and critical thinking skills to tackle business challenges with innovative data science solutions.
- Get practical experience with Python, SQL, Hadoop, Spark, cloud computing, and tools like Power BI and Tableau for real-world data analytics challenges.
- Improve your ability to present data findings to both technical and non-technical audiences, while collaborating with multidisciplinary teams on data-driven projects.
Course Eligibility:
For Indian Applicants
- Class 12 or equivalent (list of equivalents)
- The final examination of the 10+2 system, conducted by a Central or State Board recognized by the Association of Indian Universities (AIU).
- Intermediate or two-year Pre-University examination conducted by a Board or University recognized by the Association of Indian Universities.
- Final examination of the two-year course of the Joint Services Wing of the National Defence Academy.
- Senior Secondary School Examination conducted by the National Institute of Open Schooling with a minimum of five subjects.
- Any Public School, Board or University examination in India or in a foreign country recognised as equivalent to the 10+2 system by the AIU.
- H.S.C. vocational examination.
- A Diploma recognized by the All India Council for Technical Education (AICTE) or a State Board of Technical Education of at least 3 years duration.
- General Certificate Education (GCE) examination (London, Cambridge or Sri Lanka) at the Advanced (A) level.
- High School Certificate Examination of the Cambridge University or International Baccalaureate Diploma of the International Baccalaureate Office, Geneva.
- Candidates who have completed Class XII (or equivalent) examination outside India or from a Board not specified above should produce a certificate from the AIU to the effect that the examination they have passed is equivalent to the Class XII examination.
- In case the Class XII examination is not a public examination, the candidate must have passed at least one public (Board or Pre-University) examination earlier.
For International Applicants
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If an applicant has obtained their senior secondary/high school education from an institution located outside of India: they must provide an Equivalence Certificate issued by the Association of Indian Universities - which recognises their senior secondary/high school education as equivalent to Class 12 certificate issued from a recognised central or state board in India.
- The process to apply for an equivalence Certificate is detailed here
(https://www.aiu.ac.in/evaluation.php) - To apply for Equivalence, students must start by applying here
(https://evaluation.aiu.ac.in/Student/login/) - If an applicant’s educational documents (mark sheets and certificates) were issued in a language other than English: they must provide copies of such documents translated into English by a sworn translator.
Program Outcomes:
- Graduates will possess proficiency in statistical methods, business analytics, and machine learning algorithms, as well as in-depth understanding of data analysis techniques using programming languages (e.g., Python, SQL) and data manipulation tools.
- Graduates will be capable of managing large datasets, designing databases, and utilizing advanced data visualization tools such as Power BI and Tableau, effectively communicating insights through dashboards, graphs, and reports.
- Graduates will understand ethical and privacy concerns in data collection, storage, and analysis, and will implement responsible practices that ensure compliance with data protection regulations, particularly in the context of analytics and business intelligence.
- Graduates will be proficient in the theoretical foundations of analytics, including descriptive, diagnostic, predictive, and prescriptive analytics, enabling them to derive actionable insights and make data-driven decisions across industries
- Graduates will possess strong problem-solving and critical thinking skills, utilizing data science and analytics techniques to solve complex business problems and develop data-driven strategies for organizations.
- Graduates will be proficient in the use of modern data science tools like Python, SQL, Hadoop, Spark, and cloud computing services, and will demonstrate practical expertise in handling real-world data analytics challenges using Power BI and Tableau.
- Graduates will be able to apply data-driven decision-making frameworks, supporting strategic business decisions through the use of advanced analytics models and techniques.
- Graduates will demonstrate effective communication skills, presenting data findings to both technical and non-technical stakeholders, and collaborating on data science and analytics projects within multidisciplinary teams.
Course Structure
Semester I
| Course Code | Course Name |
|---|---|
| BSCCS 101 | Introduction to Computer and Problem-Solving Techniques |
| BSCCS 102 | Programming Lab |
| BSCCS 103 | Discrete Mathematics |
| BSCCS 104 | Environmental Sustainability and Climate Change |
| BSCCS 105 | Introduction to Data Science and Analytics |
| BSCCS 106 | Communication Skill - 1 |
| BSCCS 106 | Ethics and Social Implications of AI |
Semester II
| Course Code | Course Name | |
|---|---|---|
| BSCCS 201 | Object Oriented Programming | |
| BSCCS 202 | Object Oriented Programming Lab | |
| BSCCS 203 | Computer Networks | |
| BSCCS 204 | Computer Organization and Architecture | |
| BSCCS 205 | Elective | |
| BSCCS 206 | Elective Lab | |
| Elective | ||
| BSCCS 207 | Introduction to Cloud Computing | |
| BSCCS 208 | Introduction to Cloud Computing Lab | |
| BSCCS 209 | Advance MS-Office | |
| BSCCS 210 | Advance MS-Office Lab | |
Semester III
| Course Code | Course Title |
|---|---|
| BSCCS 301 | Data Structures and Algorithms |
| BSCCS 302 | Data Structures and Algorithms Lab |
| BSCCS 303 | Python Programming |
| BSCCS 304 | Python Programming Lab |
| BSCCS 305 | DBMS |
| BSCCS 306 | DBMS Lab |
| BSCCS 307 | Introduction to Software Engineering |
| BSCCS 308 | Introduction to Power BI and Tableau |
| BSCCS 309 | Seminar |
Semester IV
| Course Code | Course Title | |
|---|---|---|
| BSCCS 401 | EDA and Visualization | |
| BSCCS 402 | EDA and Visualization Lab | |
| BSCCS 403 | Introduction to Operating System | |
| BSCCS 404 | Machine Learning | |
| BSCCS 405 | Machine Learning Lab | |
| BSCCS 406 | Statistics for Data Analysis | |
| BSCCS 407 | Internship / Project | |
| BSCCS 408 | Power BI and Tableau Lab | |
| Elective | ||
| BSCCS 501 | Introduction to Artificial Intelligence | |
| BSCCS 502 | Artificial Intelligence Lab | |
| BSCCS 503 | Data Science Applications | |
| BSCCS 504 | Research Methodology | |
| BSCCS 505 | Business Analytics | |
| BSCCS 506 | Data Analytics using Python | |
| BSCCS 507 | Mini Project | |
Semester V
| Course Code | Course Title |
|---|---|
| BSCCS 501 | Introduction to Artificial Intelligence |
| BSCCS 502 | Artificial Intelligence Lab |
| BSCCS 503 | Data Science Applications |
| BSCCS 504 | Research Methodology |
| BSCCS 505 | Business Analytics |
| BSCCS 506 | Data Analytics using Python |
| BSCCS 507 | Mini Project |
Semester VI
| Course Code | Course Name | |
|---|---|---|
| BSCCS 601 | Deep Learning | |
| BSCCS 602 | Deep Learning Lab | |
| BSCCS 603 | Data Science Applications in Analytics | |
| BSCCS 604 | Advanced Data Science and Analytics | |
| BSCCS 605 | Elective – 2 | |
| BSCCS 606 | Elective – 2 Lab | |
| BSCCS 607 | Major Project | |
| Elective | ||
| BSCCS 608 | Cyber Security | |
| BSCCS 609 | Generative AI and its Application | |
| BSCCS 610 | Computer Vision | |
| BSCCS 611 | Pattern Recognition | |
| BSCCS 612 | Business Intelligence | |
| BSCCS 614 | IoT | |
| BSCCS 615 | Cloud Computing Lab | |
| BSCCS 616 | Generative AI and its Application Lab | |
| BSCCS 617 | Computer Vision Lab | |
| BSCCS 618 | Pattern Recognition Lab | |
| BSCCS 619 | Business Intelligence Lab | |
| BSCCS 620 | Data Analytics using Python Lab | |
| BSCCS 621 | IoT Lab | |
Fee Structure
| Fees Components | Sem I | Sem II | Sem III | Sem IV | Sem V | Sem VI | ||
| Caution Deposit (Refundable) | 10000 | 10000 | - | - | - | - | ||
| I Card Fee (One time) | 100 | - | - | - | - | - | ||
| Institute Development Contribution (IDC) (One time) | - | - | - | - | - | - | ||
| Tuition Fee | 37500 | 37500 | 37500 | 37500 | 37500 | 37500 | ||
| Campus Immersion Fee (optional) | 3000 | - | - | - | - | 3000 | ||
| Institute Registration Fees | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | ||
| Alumni Fee | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | ||
| Convocation Fee | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | ||
| Life Insurance* | 100 | 100 | 100 | |||||
| Medical Insurance* | 1400 | 1400 | 1400 | |||||
| Total | 55100 | 50500 | 42000 | 40500 | 42000 | 43500 | ||
Note:
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FAQs
- What is the aim of the B.Sc. in Data Science and Analytics program? To equips students with skills in data science, business analytics, and data visualization using tools like Power BI and Tableau
- What are the eligibility criteria for this program? Applicants need 10+2 in Science with a minimum of 60% and valid scores in recognized national or international entrance exams. Relaxations are available for eligible categories
- What technical skills will students gain? Graduates will learn statistical methods, machine learning algorithms, programming (Python, SQL), and data manipulation tools, which are core components of a Bachelor of Science in Data Science and Analytics curriculum.
- What career opportunities are available? Graduates can work as data scientists, business analysts, data engineers, or in analytics-driven roles across industries. B.Sc. Data Analytics graduates are highly sought after for roles that require expertise in analytics and data-driven decision-making.
- What tools and technologies are covered in the program? Students will gain expertise in Python, SQL, Hadoop, Spark, Power BI, and Tableau
Admission Process
Online Application
Payment of Application Fees
(Non Refundable)
Document Verification
Payment of Admission Fees
