Data Science Course Eligibility and Prerequisites in 2025

Data scientists have seen a huge demand surge all over the globe. This surge is equally prevalent in India, too, as there are a large number of industries that rely on skilled professionals. The demand for these professionals has increased as they are instrumental in helping businesses drive ahead with efficiency and innovation.

This is what makes understanding the data science course eligibility criteria crucial for freshers. To enter this field, you'll need more than just passion. You need particular educational or professional qualifications, apart from the various technical and soft skills.

In this blog, we will understand the data science course eligibility in India, outlining the academic backgrounds, program-specific prerequisites, and the various skills that you need to make it big. You'll also get a few tips on how to groom yourself in a way that you meet the prerequisites for a data science course.

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Data Science Course Eligibility Criteria 

India also has a high demand for skilled data science professionals, and this demand is only set to grow higher. Below are the data science course eligibility criteria that one needs to have in order to ensure that they can have a successful career in this field.

Start your journey of career advancement in data science with upGrad’s top-ranked courses and get a chance to learn from industry-established mentors:

• MS in Data Science from Liverpool John Moores University
• Generative AI Mastery Certificate for Data Analysis
• Executive Diploma in Data Science and AI from IIITB

Now, let’s understand who is eligible for a data science course:

1. Academic Eligibility for Data Science Course

• Minimum Qualification:
  • For foundational courses: Completion of Class 12 (higher secondary education) is required.
  • For advanced programs: A Bachelor’s degree from a recognized university is required.
• Preferred Streams:
  • Students with backgrounds in computer science, engineering, mathematics, statistics, or science are often given preference.
  • These streams provide a strong understanding of analytical and technical concepts required for data science.
• Exceptions: Some beginner-level certification courses may not have strict educational prerequisites. This makes them suitable for anyone interested in learning data science basics.

Must Read: How to Become a Data Scientist – Answer in 9 Easy Steps

2. Professional Background

• For Working Professionals:
  • Individuals moving into data science, such as IT engineers, analysts, or software developers, are often ideal candidates.
  • Certifications and postgraduate programs may require 1–2 years of work experience in related fields like data analysis, programming, or software development.
• Relevant Skills Matter: Even if your job experience isn’t directly related to data science, knowledge of tools (e.g., Excel, SQL) or programming languages (e.g., Python) can strengthen your application.
• Executive Programs: Tailored for professionals with significant industry experience looking to upskill for senior data science roles.

3. Age and General Requirements

• Age Limit:
  • Most data science courses in India do not have an upper age limit, making them accessible to students and professionals of all ages.
  • Some scholarships or government-funded programs may impose specific age restrictions.
• English Proficiency:
  • Since data science courses are often conducted in English, a basic understanding of the language is usually necessary for following instructions and understanding course materials.

Let us further understand the data science course eligibility criteria according to the type of program.

Eligibility for Data Science Course: Program Type

Data Science is a multidisciplinary field that draws from statistics, mathematics, computer science, and domain knowledge. If you're planning to enter this field, understanding the eligibility requirements at each academic level is essential. Here's a detailed breakdown:

1. Data Science Course Eligibility for Undergraduate Programs

Examples: B.Sc. Data Science, BCA Data Science, B.Tech. Data Science, B.Sc. Data Science and Applications

Eligibility Criteria:

• Completion of Class 12 (10+2) from a recognized board
• A mathematics and/or Science background (Physics, Chemistry, Mathematics) is often mandatory
• Minimum marks required typically range between 50% and 70%, depending on the institution
• Some institutes may require a valid entrance exam score or merit rank
• Recommended: Familiarity with basic programming and analytical skills

Popular Institutes: IITs, NITs, University of Delhi, and other state and private universities

2. Data Science Course Eligibility for Postgraduate Programs (Master’s Degrees)

Examples: M.Sc. Data Science, M.Tech. Data Science, MBA Data Science, MCA with Data Science specialization

Eligibility Criteria:

• A Bachelor’s degree in a relevant field such as:
  • B.Sc. in Mathematics, Statistics, or Computer Science
  • B.E./B.Tech. in any engineering discipline
  • BCA or equivalent
• Minimum required marks: 50% to 60% in undergraduate degree (varies by institute)
• Programming knowledge and a strong foundation in math or statistics is often expected
• Some programs (especially MBAs) prefer candidates with 2–3 years of work experience
• Admission may also depend on entrance exams or academic merit

Offered By: IITs, IIMs, University of Delhi, and top business schools

3. Data Science Course Eligibility for Postgraduate Diploma Programs

Examples: PG Diploma in Data Science, PG Diploma in AI and Analytics, PG Diploma in Programming and Data Science

Eligibility Criteria:

• At least two years of undergraduate education or a Bachelor’s degree in a related field
• Preferred domains: Computer Science, Statistics, Engineering, IT, or related areas
• Admission may be based on merit or entrance scores, depending on the institution
• Duration: Usually 1 - 2 years

Ideal For: Professionals seeking specialization without committing to a full Master’s degree

4. Certification Programs and Online Courses

Examples: Data Science Certification, Machine Learning Bootcamps, AI & Analytics Courses

Eligibility Criteria:

• Open to both students and working professionals
• No strict academic requirements — even non-STEM backgrounds may apply
• Basic knowledge of statistics and programming is helpful, but it is not mandatory
• Duration ranges from 3 to 12 months
• Offered by platforms like upGrad, and institutes like IIT Madras, IIMs, and NPTEL

Ideal For: Upskilling or career transitions into data science

5. Data Science Course Eligibility for Doctoral Programs (PhD)

Examples: PhD in Data Science, PhD in Computer Science and Engineering, PhD in ICT

Eligibility Criteria:

• A Master’s degree (M.Tech., MCA, MSc, MPhil, ME) in relevant fields such as:
  • Computer Science
  • Information Technology
  • Statistics
  • Mathematics
  • Engineering disciplines
• Some programs accept B.E./B.Tech. graduates with excellent academic performance
• Minimum marks requirement: Typically 55% to 65%
• Candidates must often clear a national-level entrance test or meet institutional research criteria

Ideal For: Individuals aiming for advanced research roles, academic positions, or leadership roles in analytics-driven sectors

Prerequisites for Data Science Courses

Learning about the prerequisites for data science courses can give you more clarity and increase your chances of succeeding in this field. Just being eligible doesn't really suffice and ensure a successful career; having a strong blend of technical skills and analytical thinking, combined with the knowledge of data tools, adds more weightage. 

This section will cover the key prerequisites and skills you need before taking up data science. Understand how important math and coding backgrounds are for people with both tech and non-tech backgrounds.

Key Skills Required for Data Science Course

To succeed in a data science course and career, you need to develop a combination of technical, analytical, and interpersonal skills. Here’s a detailed breakdown of the essential skills required:

1. Technical Skills 

• Programming Languages: Python is the core language for data science, known for its versatility and rich libraries like Pandas, NumPy, and Scikit-learn. R is also widely used for statistical analysis, while SQL is essential for managing and querying data stored in relational databases.
• Mathematics and Statistics: A good grasp of linear algebra, calculus, and probability forms the foundation of machine learning. Statistical skills are crucial for hypothesis testing, data interpretation, and making data-driven decisions.
• Machine Learning Basics: Understanding basic machine learning algorithms, such as regression, classification, clustering, and decision trees, is necessary for solving analytical problems.

2. Soft Skills Required

• Analytical Thinking: The ability to examine large datasets, detect patterns, and derive meaningful insights is key in data science.
• Communication Skills: Data scientists should be able to present their findings in a simplified manner to non-technical stakeholders.

• Problem-Solving: Creative and structured approaches are important when handling real-world, unstructured data challenges.
• Team Collaboration: Data science projects often involve collaboration across departments, requiring adaptability and teamwork.

Also Read: Skills You Need to Become a Data Scientist

3. Tools and Technologies

• Spreadsheet Software: Excel remains a staple for basic data operations, pivot tables, and quick reporting.
• Visualization Tools: Tableau and Power BI help create engaging, interactive dashboards for visual storytelling and business intelligence.
• Python Libraries and Frameworks: Pandas is used for data cleaning and manipulation, while Matplotlib and Seaborn support data visualization. For deep learning and complex models, TensorFlow and PyTorch are widely adopted.

Boost your Python skills, focusing on key libraries: NumPy, Matplotlib, and Pandas, essential for data handling with this Free Certificate in Python Libraries.

Must Read: Top 30 Data Science Tools

Do You Need a Coding or Math Background?

Many students aspiring to enter the field of data science often wonder whether prior knowledge of coding or mathematics is a must. While these skills are undoubtedly valuable, they are not always mandatory for beginners. Here’s a structured explanation to help you better understand the prerequisites and alternatives:

1. Do You Need Coding Skills?

Coding is a fundamental part of data science, especially for tasks like data manipulation, visualization, and building machine learning models. Popular languages include Python and R.

• Beginner-Friendly Approach:
  Many beginner-level data science courses are designed to teach programming from scratch. This allows students without prior coding experience to learn step-by-step.
• Recommended Skills:
  Having a basic understanding of programming logic, such as loops, variables, and conditional statements, can make the learning process smoother but is not mandatory.

Must Read: Data Science Project Ideas for Beginners in 2025

2. Do You Need a Strong Math Background?

• Key Math Concepts:
  Mathematics forms the backbone of data science, with topics like statistics, probability, linear algebra, and calculus being particularly important.
• For Non-Math Students:
  If you lack a strong math background, many courses focus on practical applications rather than heavy theoretical concepts. They often provide simplified explanations to help you grasp the fundamentals needed for data science tasks.

3. What If You Come From a Non-Technical Background?

Students without a background in computer science, engineering, or mathematics may initially find data science concepts challenging but not insurmountable.

• Beginner-Friendly and Bridge Courses:
  • Many institutes and online platforms offer beginner-friendly courses that start with the basics of coding and mathematics.
  • Bridge courses are also available to help students build foundational knowledge before diving into advanced topics.
• Self-Paced Learning Options:
  Platforms like upGrad provide flexible learning paths that allow non-technical students to learn coding, statistics, and data analysis at their own pace.

4. Recommended Path for Non-Technical Students

• Step 1: Start with basic courses on programming (Python or R) and statistics.
• Step 2: Take a beginner-friendly data science course that gradually introduces advanced concepts.
• Step 3: Use free or paid resources for additional support, such as video tutorials, e-books, or interactive coding platforms like Codecademy or DataCamp.

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Tips to Prepare for Data Science Courses

Preparing for a data science course requires a mix of foundational knowledge and practical exposure, depending on your background. Here’s a detailed guide tailored for both students and working professionals:

1. Tips for Students

If you’re a student aspiring to build a career in data science, starting early can give you a strong edge.

• Strengthen foundational knowledge:
  • Focus on Class 12 topics in mathematics, especially statistics, algebra, and probability.
  • Learn the basics of programming languages like Python or C++ to build logical thinking.
  • Study introductory concepts of data handling and analysis using tools like Excel.
• Use free resources:
  • Platforms like YouTube and NPTEL offer beginner-friendly tutorials on data science topics, including programming and data visualization.
  • Follow step-by-step guides and complete assignments provided in free online courses to gain practical understanding.
• Participate in extracurricular activities:
  • Join coding clubs, participate in hackathons, or attend webinars on data science to develop problem-solving and analytical thinking.
  • Explore online communities and forums such as Kaggle or Stack Overflow to learn from peers and experts.

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2. Tips for Professionals

If you’re a working professional looking to transition into data science, balancing preparation with your job can be challenging but achievable.

• Bridge Your Skills: Enroll in short-term courses or certifications in programming, statistics, or machine learning.
• Get Practical Experience: Work on projects like analyzing Kaggle datasets or building machine learning models with scikit-learn.
• Gain Industry Exposure: Seek internships or freelance roles to apply your data science knowledge professionally.
• Manage Your Time: Dedicate regular weekly hours to learning and practicing alongside other responsibilities.
• Master Key Tools: Learn to use Tableau, Power BI, and SQL for effective data visualization and database handling.

Here is your chance to upskill with Free Data Science Courses from upGrad. Check now!!

Conclusion

In current times, data science course eligibility is not limited to a particular background. People from various fields have the opportunity to learn and upskill themselves, based on their academic qualifications, experience, and skill set. The eligibility for data science courses may vary depending on the type of programs. However, it is not limited to people with a tech background.

Having the right blend of skills, tools, and approach, you can easily take up a data science course. Consider beginner-friendly certifications to upskill yourself from places like upGrad, as it can help you build a strong foundation and help you make bigger leaps in your career.

How Can upGrad Help?

If you're looking to enhance your expertise in Data Science, upGrad offers a comprehensive range of courses designed to help you master the essential tools and techniques.

upGrad’s Data Science courses cover everything from foundational concepts to advanced techniques, equipping you with the skills needed to analyze complex datasets, build predictive models, and derive actionable insights. These courses provide hands-on experience with popular tools and technologies like Python, R, SQL, and machine learning frameworks, preparing you to excel in the fast-growing field of data science.

1. Post Graduate Certificate in Data Science & AI (Executive)- IIIT-B

• Explore 7+ Case Studies and Industry Projects
• Immerse in 300+ Hours of Online Learning Content
• Mock Interviews with Hiring Managers
• Hassle-free, No Cost EMI Options Available
• Exclusive Job Portal and AI Profile Builder

2. Master’s Degree in Artificial Intelligence and Data Science- OPJGU

• India’s First Recognised One-Year Master’s Degree
• Earn Complementary Microsoft Certification Credentials
• Designed and Delivered by top data science experts mentored by Prof. Dinesh Singh
• Extensive Support for Learners without coding background
• GenAI Modules for Advanced AI Competency
• Capstone Projects Across Multiple Industry Domains

3. Professional Certificate Program in AI and Data Science - upGrad

• Earn Triple Certification from Microsoft, NSDC, and an Industry Partner
• Solve Real World Case Studies from Uber, Sportskeeda, or Teach for India
• Learn Advanced Curriculum with Generative AI, 17+ Tools, and 12+ Projects

4. Business Analytics Certification Programme- upGrad

• Designed for Working Professionals
• 3+ Industry Projects & Case Studies
• Regular Live Sessions with Industry Experts
• AI-Powered Resume Builder with Exclusive Hiring Portal
• 100+ Hours of Online Learning Content

Want to explore data science learning options with experts? Book a free counselling session with us and our career experts will help you gain valuable insights to take your career to a higher level.

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Reference Links:
https://www.ambitionbox.com/profile/data-scientist-salary
https://www.ambitionbox.com/profile/data-analyst-salary
https://www.bls.gov/ooh/math/data-scientists.htm
https://www.statista.com/statistics/869092/worldwide-software-developer-survey-languages-used/
https://market.us/report/data-science-platform-market/