Is Learning Data Science Hard? Here’s How to Start in 2025
By Rohit Sharma
Updated on Sep 10, 2025 | 10 min read | 15K+ views
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Did you know? Only 8% of Indian enterprises can trace their AI and data science investments back to real business outcomes, according to a report from EY. That means a whopping 92% are flying blind, and it's no wonder so many people think learning data science is hard. When the impact isn’t clear, the path often feels confusing. |
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!
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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.
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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.
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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:
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.
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.
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.
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.
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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
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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.
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Reference:
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Frequently Asked Questions (FAQs)
1. Is data science hard to learn?
Yes, learning data science can be challenging because it blends mathematics, statistics, programming, and real-world problem-solving. Beginners often feel overwhelmed by the breadth of skills required. However, with a structured approach, consistent practice, and guidance through courses or projects, learning data science becomes manageable and highly rewarding.
2. Can data science be self-taught?
Yes, many learners successfully teach themselves data science using online courses, tutorials, and project-based learning. Platforms like Coursera, edX, Kaggle, and YouTube provide structured content. Self-learning requires discipline, consistent practice, and building a portfolio to demonstrate practical skills to potential employers.
3. Can I become a data scientist in 3 months?
Becoming a fully proficient data scientist in three months is unrealistic for most beginners. However, you can learn the basics of Python, statistics, and data handling in this timeframe. Focus on completing small projects and understanding core concepts, then continue building skills through hands-on projects and advanced courses.
4. How long would it take to learn data science?
On average, it takes 6–12 months to gain foundational skills in data science if learning consistently. Mastery of advanced topics like machine learning, cloud platforms, and deployment may take 1–2 years. The timeframe depends on prior knowledge, learning pace, and commitment to practice and project work.
5. Can a fresher become a data scientist?
Yes, freshers can enter data science roles if they build a strong foundation in programming, mathematics, and statistics. Completing projects, internships, and certifications can help demonstrate practical skills. Non-technical graduates can also succeed by taking bridge courses in Python, SQL, and data analytics to align with industry expectations.
6. What kind of projects should I include in a beginner data science portfolio?
Focus on small end-to-end projects demonstrating problem-solving, analysis, modeling, and visualization. Examples include customer segmentation, sales forecasting, and sentiment analysis. Host your projects on GitHub and use dashboards with Tableau or Streamlit to showcase your work. Clearly document your methodology and results to make your portfolio professional and compelling.
7. How important is domain knowledge in data science?
Domain knowledge is critical for contextualizing data, interpreting results, and improving model performance. For instance, understanding marketing metrics helps predict customer behavior accurately. Without domain insight, even technically sound models may produce misleading outcomes. Employers value data scientists who combine analytical expertise with industry understanding.
8. How do I transition to data science from a non-tech background?
Start with core skills like Python, SQL, and statistics. Take structured courses that combine theory and hands-on projects. Begin with small datasets and practical tasks, gradually moving to machine learning. Focus on building a portfolio, gaining certifications, and participating in internships or competitions to demonstrate your capabilities to employers.
9. How does data science differ from data engineering?
Data scientists focus on analysis, building predictive models, and extracting insights using tools like Python, R, and scikit-learn. Data engineers design and maintain ETL pipelines, manage databases, and ensure data quality at scale. Both roles overlap in skills like SQL but differ in responsibilities: scientists experiment with data, engineers ensure infrastructure stability and scalability.
10. Are internships necessary to land a full-time role in data science?
Internships are highly recommended for gaining real-world experience. They allow hands-on work with datasets, analytics workflows, and collaboration with teams. Internships help build portfolios, understand business requirements, and develop soft skills like communication and project management, making you more attractive to employers for full-time roles.
11. What tools should I learn beyond Python and R?
Learn Git for version control, Jupyter or Colab for notebooks, and Tableau or Power BI for visualization. Docker and Flask are useful for deploying models, while Apache Airflow or dbt help with ETL pipelines. Knowledge of cloud platforms like AWS, GCP, or Azure further boosts industry readiness.
12. Do data scientists need to know cloud platforms?
Yes, cloud skills are essential for deploying scalable pipelines and managing large datasets. Tools like AWS S3, BigQuery, SageMaker, and Kubernetes enable efficient workflow orchestration. Understanding cloud IAM, VPCs, and storage policies ensures secure, cost-effective, and compliant data science operations in production environments.
13. What soft skills are most valued in data science roles?
Communication, problem-solving, critical thinking, business acumen, and collaboration are vital. Data scientists must explain insights to non-technical stakeholders, evaluate model outcomes beyond metrics, and work effectively with engineers, analysts, and managers to deliver actionable solutions aligned with business goals.
14. How do product-based and service-based companies differ in their data science roles?
Product-based companies focus on improving the product experience, such as recommendation engines or personalization algorithms, while service-based firms handle client-specific projects across industries. Product roles often involve large datasets and continuous optimization, whereas service roles demand adaptability, domain knowledge, and timely delivery within client constraints.
15. How can I stay updated with the latest in data science?
Follow blogs like KDnuggets and Towards Data Science, subscribe to podcasts like Data Skeptic, and read research papers from conferences such as NeurIPS or ICML. Join communities on GitHub, Reddit, or LinkedIn to collaborate on projects and contribute to open-source initiatives. Continuous learning ensures skills remain current.
16. What is the syllabus for data science?
A typical syllabus includes Python/R programming, SQL, statistics, probability, data visualization, and machine learning basics. Advanced topics cover deep learning, NLP, AI integration, and cloud deployment. Courses often include hands-on projects, internships, and real-world case studies to develop end-to-end problem-solving skills.
17. How long does it take to learn Python for data science?
Learning Python basics for data science typically takes 4–8 weeks of consistent practice. Core concepts include variables, loops, functions, and libraries like Pandas, NumPy, and Matplotlib. Mastery comes through implementing projects and solving real-world data problems using Python.
18. How long does it take to learn SQL?
SQL can be learned in 3–6 weeks for basic data querying, joining tables, filtering, and aggregation. Advanced topics like stored procedures, indexing, and performance optimization may require additional practice. Hands-on experience with databases reinforces learning and is critical for data manipulation in real-world scenarios.
19. What is the 80/20 rule in data science?
The 80/20 rule states that 80% of results come from 20% of effort. In data science, it means that mastering core concepts, Python, SQL, and statistics enables you to solve most practical problems, while advanced topics provide additional optimization but are often less critical initially.
20. Why do 85% of data science projects fail?
Projects often fail due to unclear goals, poor data quality, lack of domain knowledge, and insufficient collaboration between teams. Misalignment between technical outcomes and business objectives, combined with inadequate evaluation metrics, leads to high failure rates. Effective planning, communication, and stakeholder involvement increase project success.
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Rohit Sharma is the Head of Revenue & Programs (International), with over 8 years of experience in business analytics, EdTech, and program management. He holds an M.Tech from IIT Delhi and specializes...
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