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Is Learning Data Science Hard - A Complete Guide

18th Jan, 2024
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    Is Learning Data Science Hard - A Complete Guide

    Data science is a multidisciplinary field that combines computer programming, statistics, and business knowledge to solve problems and make decisions based on data rather than intuition or gut instinct. It requires mathematical modeling, machine learning, and other advanced statistical methods to extract useful insights from raw data. 

    Data scientists are employed by both small startups and large corporations such as Google, Facebook, Amazon, and Microsoft and government agencies like NSA and CIA. They analyze everything from customer transactions to weather patterns to inform business strategy or improve public policy decisions. Data scientists also work with artificial intelligence algorithms that automate product recommendations or fraud detection processes. 

    If you are confident about handling these, you can complete the data science training with a little effort. If you have been bothered by questions like is data science hard, why is data science so hard, this article is for you. 

    Is Learning Data Science Worth It? 

    With the increasing advent of technological developments, various tech-based food savers see continuous demand growth. Students feel especially driven towards courses like data science in MBA due to the field's high-paying job opportunities. Today, a lot of data is being created, exchanged, and sourced every day, and it needs to be managed. Therefore, companies require qualified persons to collect and organize the required data. 

    In 2021 data science job opportunities showed a 47.1 percent increase in India

    Data science provides several job roles with high salaries.

    • Data Scientist-(average salary: Rs 11 lakhs, can reach up to Rs 25 lakhs) 
    • Data analyst-(average salary: Rs 4.2 lakhs, can reach up to Rs 11.5 lakhs) 
    • Data architect-(average salary: Rs 23 lakhs, can reach up to Rs 38.5 lakhs) 
    • Data engineer-(average salary: Rs8.1 lakh, can reach up to Rs 20 lakhs) 
    • Market research analyst-(average salary: Rs 8 lakhs, can reach up to Rs 13 lakhs) 
    • Machine Learning Engineer-(average salary: Rs 7.5 lakhs, can reach up to Rs 21. 8 lakhs)

    Programming and Other Languages in Data Science

    There are a lot of programming languages that can be used for data science. It is important to choose a language that is easy to learn and use, but it is also important that the language you use will be able to give you the tools needed for your work. 

    Here are some of the most popular data science programming languages: 


    Python is one of the most popular languages for data science. It has been around for a long time (since 1991) and has gained popularity due to its flexibility and simplicity. It can be used for everything from web development to machine learning. 


    R is another popular programming language for data science and statistics. It was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, in 1993 as an offshoot of the S language developed by John Chambers at Bell Laboratories, which he created in 1976. Since then, it has gained popularity as a tool for statistical analysis and predictive analytics, among other things. 


    MATLAB (Matrix Laboratory) was originally developed by MathWorks in 1986 as an interactive environment for matrix computations. The software package evolved into a general toolbox with a wide range of functions, including plotting, optimization, curve fitting, statistical analysis, etc., making it incredibly useful.


    SQL is essential if you want to work with relational databases at any level of detail. SQL databases are structured differently than NoSQL databases - they store data in tables rather than documents or graphs - but they're still very useful when you want to structure your data in a way that makes sense for humans (and computers). 

    What Makes Data Science Difficult?

    Data science is a difficult field. 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., $10 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 strong grasp of statistics (at least at an introductory level) since much of what they do involves analyzing large volumes of data with algorithms like regression analysis. 

    • Data Science Is Collaborative. 

    Data scientists work with other people on a regular basis: other data scientists, software engineers, managers and executives, data analysts, and more. These roles require different skill sets and working styles that take time to learn. 

    Data science requires collaboration because data isn't just numbers; it's also text, images, and audio. Data scientists must understand how those pieces fit together and what questions they can answer using those types of data. 

    • Data Science Is Iterative. 

    You have to try things out and see what happens — over and over again! This makes it difficult to get started on projects because you don't know where they're going or how long they'll take (it's easier to predict how long a project will take if you're following an established process with well-defined steps). It also makes it hard to know when you're done — there's always more analysis that could be done! And finally, it means that there isn't really one answer for any question — there are always multiple interpretations (and maybe even multiple solutions). 

    • 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! 

    Is Data Science a Hard Major to Get Into?

    Data science is a major that can be incredibly difficult to get into. The field is growing rapidly, and there are a lot of people who want to get into it. If you're interested in data science, you need to start thinking about how you can position yourself for success in the highly competitive job market. 

    Some of the best ways to do this are by developing strong technical skills and learning how to communicate your knowledge effectively. Technical skills will help you understand how data science works and how to use it for different purposes. Communication skills are important because they allow you to share what you know with other people in an effective way. 

    If you want a career in data science, it's important that you understand these two areas well enough so that you can build a strong foundation for your future career. 

    How Hard is It to Get into Data Science?

    Data science is a hot career field, but it's not easy to break into.The demand for data scientists is growing rapidly, and it shows no signs of slowing down. But there's a shortage of professionals who know how to analyze information and turn it into meaningful insights. 

    If you want to become a data scientist, you'll need some combination of education, training, and experience. These are the most common paths: 

    • Master's degree in statistics or computer science. Many colleges offer programs that combine statistics with skills in programming languages like Python or R, which are essential tools for working with data sets. You'll learn how to use statistical techniques such as linear regression and machine learning to find patterns in large amounts of data. You'll also get an introduction to database management systems like SQL (Structured Query Language) and NoSQL databases like MongoDB or Hadoop MapReduce. These skills will prepare you for entry-level positions as data analysts or consultants. 
    • Ph.D. program in statistics or computer science at an accredited university. These programs take about five years to complete and focus on advanced math concepts such as multivariate calculus, probability theory, and differential equations — all essential tools for analyzing big data sets using statistical techniques like regression analysis and machine learning. 

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    Bottom Line

    So, is data science hard to learn? The answer is yes, and no. The answer is yes because there are many different skills that you need to master in order to be a data scientist. You need to know how to program, how work with databases, how to handle large amounts of data, how to write reports that make sense, and how to communicate your findings clearly and persuasively. 

    The answer is no because there are lots of resources available online that teach you all these things. The only thing standing between you and becoming a data scientist is time — and the willingness to put in the effort required by learning a new set of skills. With knowledgeHut data science Bootcamp, you can get the basics down and ease into this competitive field.

    Frequently Asked Questions (FAQs)

    1Can I teach myself data science?

    The answer is yes and no. You can certainly learn about data science on your own, but you may not be able to develop the skills required to become a true data scientist. 

    • Learn the basics of statistics and machine learning. 
    • Understand how to use R and Python (or another programming language). 
    • Set up an environment where you can practice your new skills. 
    • Participate in different types of competitions that challenge you to apply your knowledge of statistics and machine learning algorithms in real-world scenarios.
    2How long does it take to learn data science?

    The answer depends on what kind of data science you want to do. If your goal is to become an expert in machine learning and AI, you should be prepared for years of hard work. If your goal is to get a job as a data scientist, then expect to spend anywhere from six months up to two years studying the subject. 

    3Is data science a lot of math?

     Data science includes mathematical knowledge, but it is not completely math. Math is required to develop your programming skills, machine learning, and to learn algorithms. 

    4Is data science a stressful job?

    Data scientists will have to work with a lot of data. The factors that make it stressful are the huge data workload, hard problems to be solved, and the pressure from management.


    Kevin D.Davis

    Blog Author

    Kevin D. Davis is a seasoned and results-driven Program/Project Management Professional with a Master's Certificate in Advanced Project Management. With expertise in leading multi-million dollar projects, strategic planning, and sales operations, Kevin excels in maximizing solutions and building business cases. He possesses a deep understanding of methodologies such as PMBOK, Lean Six Sigma, and TQM to achieve business/technology alignment. With over 100 instructional training sessions and extensive experience as a PMP Exam Prep Instructor at KnowledgeHut, Kevin has a proven track record in project management training and consulting. His expertise has helped in driving successful project outcomes and fostering organizational growth.

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