Is Learning Data Science Hard? Here’s How to Start in 2025

Is learning data science hard? For many beginners, the answer is yes, but not because it’s impossible. It’s hard because it blends math, programming, and real-world problem-solving into one demanding discipline. But with the right mindset and structured learning path, it becomes manageable and rewarding.

Many learners struggle because they try to master everything at once or lack a structured approach. The steep learning curve is real, but not unbeatable. 

In this blog, you’ll read about what makes data science hard, what things you need to do to start a career in data science, and a few tips and insights to ensure you have a pleasant learning experience!

Ready to learn Data Science and secure a 57% salary hike? Explore upGrad’s Data Science course designed in partnership with leading Indian and global universities. Featuring a GenAI-integrated curriculum, this program offers hands-on learning in Python, ML, AI, SQL, and more. Enroll Now!

Is Data Science Hard To Learn?

Learning data science can be hard, yes. But it isn’t an impossible task. There are various challenges one might face while learning data science. Although with the right mindset and approach, it is possible to learn data science. However, your level of knowledge of math, logical reasoning, and analytical problem-solving skills can affect the overall learning experience.

Ready to build a future in data science and AI? Explore top-tier programs designed to equip you with practical skills and industry recognition. Below are the data science courses you can choose to upskill:

• M.Sc. in AI & Data Science – Jindal Global University
• PG Diploma in Data Science – IIIT Bangalore
• Executive Post Graduate Certificate Programme in Data Science & AI

Data science programs often expect foundational knowledge in mathematics, programming, and logical reasoning. For students coming from non-STEM backgrounds, this can be a significant barrier.

 Let’s explore the following pointers to get an answer to your question: "Is learning data science hard?

• High competition for limited seats: Admission to data science programs is highly competitive as demand outpaces supply in top institutions.

  Example: Demand for data science programs in India has surged, with institutions like IIT Madras reporting a rise in enrolments for their BSc in Data Science. Similarly, programs at ISI Kolkata and IIIT Hyderabad have seen growing competition, reflecting the field’s increasing popularity among Indian students.

• Foundational skills are essential: You need a solid base in mathematics (linear algebra, probability), programming (often Python), and logic to keep up with the coursework. Building these skills typically requires around 3 to 6 months of dedicated study, and depending on the resources you choose, costs can range from free online courses to paid programs costing between ₹8,000 and ₹40,000.

  For Example: IIT Bombay’s data science electives require students to pass rigorous first-year math and computing courses just to enroll, highlighting the importance of a strong foundation before diving deeper.

• Non-STEM students face added challenges: Candidates from non-technical backgrounds often struggle due to a lack of early exposure to coding and statistics.

  Example: A student from a commerce background entering a data science diploma program may need to take additional bridge courses in Python and math.

• Alternative entry routes are emerging: Some institutions are offering non-traditional, flexible pathways to open access to data science education.

  Example: IIT Madras offers an online BS in Data Science where students qualify through a foundational course and exam, skipping the JEE route altogether.

Also Read: 48 Software Engineering Projects in 2025 With Source Code

• Employers expect more than a degree: Companies prefer candidates who can demonstrate practical skills through real projects, not just academic credentials.

  Example: A candidate with a Kaggle profile showcasing end-to-end projects like fraud detection or movie recommendation engines is often favored over someone with only classroom experience.

• AI is reducing entry-level roles: Automation tools are replacing many tasks once assigned to junior data scientists, raising the bar for new graduates.

  Example: Platforms like ChatGPT and AutoML can now automate basic data cleaning and model training, which used to be entry-level work.

• Strong communication is crucial: The ability to explain data insights to non-technical stakeholders is a skill every data scientist needs.

  Example: Data science graduates in consulting roles at firms like McKinsey must present technical findings in business terms to CXOs and clients.

• It’s difficult, but worth it: Despite the hurdles, data science offers long-term value with strong salaries and cross-industry relevance.

  Example: Freshers in India with a strong data science portfolio earn packages between ₹6–15 LPA, and can find roles in sectors from healthcare to sports analytics.

To truly understand why data science can be challenging, it’s important to look at the technical foundation it demands. Let’s explore below what technical understanding you need.

What Makes Data Science Difficult?

There are many reasons for this, but the most important one is that it requires a broad set of skills and knowledge. 

The core elements of data science are math, statistics, and computer science. The math side includes linear algebra, probability theory, and statistics theory. The computer science part includes algorithms and software engineering. The other half of the equation is domain knowledge, which means knowing something about the field you're working in.

For example, if you work in marketing, you'll need to know what marketing campaigns are available (advertising channels), how they work (e.g., cost per impression), and how much they cost (e.g., ₹830 per thousand impressions), etc. If you work in healthcare or the government, specific regulations may apply to your work. 

• Data Science Is Interdisciplinary

  Data science draws from various disciplines, including statistics, machine learning, computer science, and mathematics. The skills needed to do data science well can't be learned in isolation; they require a broad understanding of these fields. Data scientists need a broad array of skills and knowledge,  from programming languages like Python or R to SQL database queries and math skills like calculus and linear algebra. They also need a basic understanding of statistics, as much of their work involves analyzing large datasets using methods like regression analysis.

• Data Science Is Collaborative

  Data scientists collaborate with engineers, analysts, and stakeholders to align ML pipelines, data schemas, and output formats with business requirements. Collaboration involves containerizing models using Docker, integrating APIs with FastAPI or Flask, and version-controlling code and datasets via Git and DVC.

  Handling diverse data types, such as tabular, text, image, audio, requires applying NLP, CNNs, MFCCs, and multi-modal preprocessing tailored to each data domain. Insights are shared through Jupyter, Streamlit, or dashboards built using Shiny, with production models tracked through MLflow. 

• Data Science Is Iterative

  Data science is inherently iterative, involving continuous experimentation across data preprocessing, feature engineering, model selection, and hyperparameter tuning. Initial project scopes often evolve as exploratory data analysis (EDA) reveals hidden patterns, outliers, or data quality issues. 

  Each cycle may introduce new variables, reshape hypotheses, or trigger re-training using alternate algorithms. Such processes include shifting from a Random Forest to a Gradient Boosted Tree when faced with class imbalance or variance issues. 

• Data Science Requires Creativity 

  In addition to being interdisciplinary, data science also requires creativity, sometimes even more so than other disciplines do. You must be able to think outside the box and come up with novel solutions that nobody else has thought of before (or at least haven't implemented). That's not easy at all! 

  Despite these challenges, mastering data science is entirely achievable with the right guidance and resources. The breadth of skills and iterative nature may seem overwhelming at first, but structured learning can simplify your journey. 

This is why opting for the upGrad Masters in Data Science Degree from Liverpool John Moores University offers a globally recognized, affordable online program completed in just 18 months, along with an Executive Diploma from IIITB to boost your credentials.

Also Read: How to Become a Data Scientist - Answer in 9 Easy Steps

As interest in data science grows, many aspiring professionals wonder just how challenging it is to enter this field. The section below will answer this.

How to Start Learning Data Science (Step-by-Step)

Learning Data Science can be hard, but it doesn’t mean it isn’t possible. You can learn data science by using the right approach.  Here are a few things you can do to start learning data science.

1. Learn Python

Start with the basics of Python programming. You need to focus on the core concepts like functions, loops, and variables. After you're confident, move on to the libraries that are essential, like Pandas and Numpy. These libraries are used for data manipulation and numerical computing

2. Understand Math & Statistics

Math and Statistics form a major part of data science, and it's crucial to build a strong foundation in topics like linear algebra, probability, and basic statistics

3. Practice Data Handling

Constant practice is required to become more familiar with data handling. Working with data that's available, learning how to clean, transform, and analyze data using tools like SQL for querying structured databases and Python for unstructured data, can help you learn better.

4. Explore Data Visualization

An important aspect is visual storytelling, and to be well-versed with it, you need to work with tools like Matplotlib, Seaborn, and Tableau. Learning these tools can help you understand data and present it better.

5. Grasp Machine Learning Fundamentals

Learning about the basics of machine learning becomes crucial. It starts with understanding the difference between supervised and unsupervised learning. Start with intuitive models in scikit-learn, such as linear regression, decision trees, and clustering.

6. Work on Projects

Apply what you learn practically, usually through small projects. You should aim to complete 2–3 end-to-end projects that demonstrate data collection, analysis, modeling, and presentation. Working on projects is very crucial to build your basics.

7. Host & Share Your Work

Working in the shadows won't help. Build your digital portfolio. There are various platforms through which you can network and learn. Use GitHub to store your code and projects, competitions like Kaggle can help you build confidence greatly, and writing personal blogs over various channels to present your insights also helps greatly.

Here Are a Few Tips to Learn Data Science Faster and Smarter

Learning data science doesn’t always have to be a struggle. Yes, learning data science is a task, but if you do it right, you can do wonders. Here are a few tips that can help you learn data science in a faster and smarter way:

1. Stick to One Roadmap

   It's important to stick to one channel of learning, as you might get overwhelmed by multiple resources. You should choose a set learning structure and follow it consistently instead of juggling between countless YouTube channels or online courses.

2. Practice Consistently, Not Perfectly

   It's important to understand that consistency is more important than perfection. Learning through trial and error, building the habit of solving problems daily, adds up to making you better eventually.

3. Join Data Science Communities

   Various platforms like Reddit, Kaggle, LinkedIn, and Discord are great for finding like-minded professionals and learners. These communities can greatly help you by offering support, feedback, and exposure to various existing challenges.

4. Apply What You Learn Immediately

   Don't wait to finish the theory and then start with projects. Keep applying the concepts throughout by working on small projects. They might be messy at first, but continuous practice can enhance your skills greatly.

Conclusion

Data science is a high-demand career, but learning it can be both challenging and achievable. It requires mastering diverse skills, including Python programming, writing SQL queries to manage relational databases, handling large datasets with tools like Pandas, and creating data visualizations. 

While that may seem overwhelming, the good news is that numerous accessible resources are available to guide you. So, the answer to the question, ‘Is Learning Data Science Hard?’ is yes, but you can learn it with the right approach.

Want to simplify your learning journey? Get started with these additional Data Science courses to build core skills and gain confidence in this field.

• Professional Certificate Program in Business Analytics & Consulting in association with PwC Academy
• Data Science in E-commerce
• Advanced SQL: Functions and Formulas

If you’re unsure whether data science is the right path for you, let upGrad’s expert counselors help you decide. Receive personalized guidance to select the ideal program that aligns with your strengths and career objectives. Visit our offline centers or connect with us directly to begin your data science journey with clarity and confidence.

Unlock the power of data with our popular Data Science courses, designed to make you proficient in analytics, machine learning, and big data!

Explore our Popular Data Science Courses

Browse our popular Data Science articles and find courses that complement your learning, helping you build real-world expertise.

Read our popular Data Science Articles

Gain hands-on experience in the top Data Science skills with our comprehensive courses, crafted to accelerate your career growth.

Top Data Science Skills to Learn

Reference:
https://www.ey.com/content/dam/ey-unified-site/ey-com/en-in/services/ai/aidea/2025/01/ey-the-aidea-of-india-2025-how-much-productivity-can-genai-unlock-in-india.pdf