How Much Math Do Data Scientists Need?

Are you dreaming of becoming a data scientist one day? You may be wondering if you can be one with little or no background in mathematics, and which skills are essential in the field, so you don’t get bogged down with unnecessary details further in your journey. But how much math is too much or too little?

Data scientists need plenty of math, including linear algebra, calculus, and statistics. However, the opinions will vary. For the data science industry, you need intuition and practical application in business more than math.

Read on to find out what competing sides are saying and discover the specifics on the branches of math you need for your future career as a data scientist. Ultimately, you’re the one to decide whose standpoint is applicable in your particular situation. 

Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!

The Role of Mathematics in Data Science

Mathematics is essential in data science because its concepts help in identifying patterns and creating algorithms. An aspiring data scientist should at least understand the nuances of the Statistics and Probability Theory, which are important for implementing such algorithms.

Data Analysts vs. Data Scientists

Data analysts and data scientists both work with data but differ in how they handle it. Analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make strategic decisions.

Scientists glean information and design data modeling processes by creating algorithms, prototypes, custom analyses, and predictive models to solve complicated organizational problems. They employ techniques like data mining and machine learning to sift through data, so a master’s degree in data science is beneficial for professional advancement, but not mandatory.

Do You Have to Be a Math Wiz?

If you want to be a data analyst, you need a strong foundation in math. You should at least be comfortable with college algebra.

If you want to be a data scientist, you must be able to estimate the unknown by asking questions, writing algorithms, and building statistical models. You should be prepared to do heavy coding. You have to be adept at arranging undefined sets of data and building your automation systems and frameworks using multiple tools simultaneously.

The ideal data scientist is a master problem solver with expertise derived from mathematical and statistical knowledge. Nobody will force you to get a master’s degree or a Ph.D. but be aware that many data scientists hold postgraduate degrees, and such qualifications play an instrumental role in bumping up their salaries.

Academic Viewpoint

This is the answer from Flatiron School and Distance Calculus @ Roger Williams University: You need to be knowledgeable in math.

The Distance Calculus folks believe that to be a real data scientist, you need to be half programmer and half mathematician or statistician. Ergo, the data scientist aspirant needs a solid foundation in university mathematics, which starts with these undergraduate, lower-division courses:

  • Calculus I, II, and multivariable
  • Differential Equations
  • Linear Algebra
  • Probability Theory or calculus-based statistics

While most academics emphasize that there are no shortcuts in building up mathematician credentials for data science, many aspirants don’t have the resources for postgraduate education.

A math degree is undoubtedly desirable and valuable, but if you’re budgeted for time or money, you can learn the above subjects using online resources. Most are offered in the form of MOOCs (massive open online courses) and are frequently offered for free or at discounted price.

Popular examples of these online education providers that offer course on the above subjects and much more include the following –

  • edX
  • Coursera
  • Udacity
  • Khan Academy
  • MIT OpenCourseWare

In pursuing these options, you, however, must note that most MOOCs don’t award college credits. Many employers, especially giant corporations, are impressed with documents reflecting transferable academic transcripts that prove course completion. On the flip side, taking the above subjects as part of a certificate or diploma program or a university degree ensures proper credentials for building a tangible and accredited academic portfolio.

Requisites for Data Science

Necessary skills and tools include data mining/warehouse, data analysis/development, object-oriented programming, machine learning, Java, Python, and Hadoop. This is merely a microcosm of what needs to be mastered. There’s so much more to learn, but let’s focus on math for now because it’s essential to build a foundation as a data scientist.

Math in Analytics

Data scientist Benjamin Obi Tayo confirms that math skills are essential in data science and machine learning because the theoretical foundations of data science are crucial for building efficient and reliable models. Wannabe data scientists must study the mathematical theory behind each machine learning algorithm.

These are some of the recommended packages used for descriptive and predictive analytics:

  • Ggplot2
  • Matplotlib
  • Seaborn
  • Scikit-learn
  • Caret
  • TensorFlow
  • PyTorch
  • Keras

Regular folks, not just data specialists, can use the above systems to build predictive models or produce data visualizations. However, if you want to produce top-notch performing models, you need a solid mathematics background to fine-tune them.

If what data scientists do all day is merely building models, it would have made their jobs much easier to be performed by an average Joe. But they also have to interpret these models and extract meaningful conclusions from them to make data-driven decisions. So, they are needed to thoroughly understand each package’s mathematical basis instead of just using it as a black-box tool.

Math in Multi-Regression Models

If you’re going to build multiple regression models, you have to answer complex questions and address intricate situations daily. Without a strong math background, you’ll have difficulty resolving them. This is why Tayo stresses how tantamount mathematical skills are to data science and machine learning as programming skills.

Study the theoretical and mathematical foundations of data science and machine learning because your ability to build efficient models applicable to real-world scenarios is dependent on mathematical skills.

To appreciate how math is applied in building a machine learning regression model, watch some free machine learning process tutorials on YouTube. That will help you build a perspective on the real world application of math in various machine learning solutions. 

Should Data Scientists Know Calculus?

Yes. It is part of Tayo’s recommended essential math skills for data science and machine learning needed to build math foundations and boost career portfolios:

  • Statistics and Probability – These are used for feature visualization or transformation, data preprocessing or imputation, dimensionality reduction, model evaluation, and feature engineering.
  • Multivariable Calculus – This is essential for machine learning models because scientists build them using data sets with various predictors.
  • Optimization Methods – This is needed to perform predictive modeling.

Real-World Viewpoint

We sourced the opinions of both academics and working scientists. We found that the former, though highly respected and mean well, tend to deliver information scary to neophytes. Scientists give us a clearer picture of what’s out there.

Data scientist Dario Radečić sheds light on how much math his ilk uses in real life. He assures wannabe scientists that not all data science jobs are reserved for math brainiacs.

Cultivate Your Intuitive Powers

Radečić points out that humans aren’t meant to do heavy-duty calculations manually. That’s what computers are for. They’re much faster and less prone to errors than humans. Instead, the onus of data scientists is to develop intuition behind every major math topic and identify specific situations in projects where this topic is applicable. Ignore the extra stuff that confuses and intimidates.

What It’s Like in Real Life

Radečić says that on the job, only the result matters. You produce what you can with your current knowledge. Your supervisor is only interested in the fact that you can solve the immediate problem, making them look good to the powers. So he won’t care which method you’ve implemented first or last. 

Do You Need a Math Degree?

No, you don’t, but having a math-heavy degree is the conventional way to break into data science. It looks great on paper, like on your resume. It also ensures smart alecks, also known as job interviewers, not to walk all over you.

Having advanced math credentials prompts potential employers and future colleagues to hold you in high esteem. Degrees, as with most status symbols, are merely masks of perception. It’s like in art, where an image of a can of soup is worth a lot of money because of its perceived value. That doesn’t mean it’s worth that sum in reality.

The Data Science Mantra

Oprah says, “Be the best version of yourself.” This is the equivalent in data science: “Be better at programming than an average mathematician. Be better at math than an average programmer.”

That said since data science is an amalgamation of various fields, it helps if you are excellent at some of them. Much better if you are adept in all of them.

Radečić explains that in an average data science job role, “Math is just a tool for getting needed results, and for most things, having a good intuitive approach is enough.”

The Middle Ground

The folks at Sharp Sight Labs reinforce Radečić’s opinion that beginners don’t need to know much math to break into data science. They agree that practical data science requires some math, but the skill in using the right tools is more important. Mastery of these tools’ mathematical details isn’t necessary.

Academic vs. Business Data Scientists

There’s a difference between theoretical and practical data science. The former is studied in an academic environment for research. The latter is practiced in business or industry.

Their priorities, focal points, and deliverables are different, so they use different tools. Academics produce papers and novel research. Industrial or business-oriented data scientists produce models, reports, analyses, and software.

Juniors vs. Seniors

Junior data scientists are different from their senior counterparts. An individual data scientist’s level in the hierarchy also dictates how much math he or she will need. Juniors don’t need the same depth of math knowledge as seniors because, as with most starting positions, they do routine work for the first year and a half.

They’re not assigned major projects right away. Rather, they act as gophers for seniors. Seasoned juniors sometimes produce simple reports and analyses. So aspirants, lower your expectations and adjust your mindset accordingly.

Industry rule says approximately 80% of a neophyte’s work is spent collecting and cleaning data from various sources like spreadsheets, text files, and databases for basic exploratory data analysis.

Industrial junior data scientists usually work with these foundational, also known as, core or fundamental skills:

  • Data manipulation
  • Data visualization
  • Exploratory data analysis

These are core because almost everything else in a practical setting is reliant on them. Most reporting, data analyses, and building machine learning models require them, especially in a junior capacity. Therefore, for almost all data science deliverables, you should know how to best leverage these three big skills listed above.

Should Data Scientists Know Math? How Much Math Do They Need for Foundational Skills?

Very little. You can learn most of these skills without prior math knowledge. This runs contrary to the assumption that data science requires mastery of math. According to Sharp Sight Labs, a shrewd first-year college student has enough math knowledge to perform the core skills. You need only the lower-level algebra and simple statistics already learned from grades 8 to 12.

Creating new variables requires almost no math skill. It’s no more complicated than dividing one variable by another or doing a basic statistical manipulation like calculating a mean. That being said, there are always exceptions. However, these exceptions are uncommon, at least for beginners, as 95% of all data manipulation requires only simple math.

Creating basic charts and graphs for exploratory data analysis requires only simple data visualization techniques for analyzing data.

If you remember sixth-grade math and the Cartesian coordinate system, you can create simple scatterplots. The complication in scatterplots lies in the syntax, so you need to master syntax rather than math. Even when it’s time to apply the syntax and use visual tools properly, you still don’t need calculus.

The same goes for simple histograms. As long as you know what a histogram is. Even if you don’t, you can catch up with a quick perusal of YouTube tutorials. To manipulate and visualize data, all you need are basic problem-solving skills and algebra.

What About Machine Learning?

Machine learning requires more math than data manipulation and visualization. But the rigour of mathematical application in this field will also depend on whether you’ll practice machine learning in an academic setting or in a business environment. The former requires a considerable application of math, and the latter only requires limited mathematics.

The difference between theory and practice generally applies to data science and specifically to machine learning. Hence, you will beyond doubt need a solid understanding of calculus, linear algebra, statistics, and occasionally, information theory if you intend to produce machine learning papers in an academic environment.

In contrast, typical practitioners use a lot less math in machine learning, with certain exceptions. Senior data scientists sometimes need advanced math to solve problems, but juniors rarely need it, as they don’t usually work on machine learning projects. 

If ever they do in the future, they can learn many machine learning topics without advanced math. For almost every machine learning algorithm, anyone can learn how the algorithm works without knowing calculus or linear algebra.

Do It Yourself

Just read a book. Machine learning professor Larry Wasserman from Carnegie Mellon University, one of the best for machine learning, recommends An Introduction to Statistical Learning by Tibshirani et al. 

Despite its title, it’s considered the best initiation to machine learning with its broad overview of techniques, yet comprehensive explanations of every major tool and method.

It uses minimal concepts from statistics and computer science, and almost zero calculus and linear algebra!

Here’s an encouraging anecdote from Sharp Sight Labs. They know a couple of machine learning practitioners without advanced math training at Apple and Bank of America. Neither is a math genius, yet each rakes in a six-figure salary. Both these professionals have simply mastered the application of common machine learning techniques, and it is doing wonders for them.

The most important modules that you must consider learning if you are serious about building a career in machine learning include the following –

  • Basic Charts and Graphs – It is used to understand basic Cartesian plotting.
  • Functions – It is used to know what they are and how to plot them.
  • Basic Algebra – It is used to know variables and exponents; read math equations.
  • Basic Statistics – It is used to be familiar with standard calculations like median, mean, variance, and standard deviation.
  • Standard Math Notation – It is used to know variables, exponents, subscripts, and summation/sigma notation.

What Kind of Math Is Right for You?

Consider your natural inclination and temperament first. Then ask yourself these questions:

  • Do I veer toward numbers and statistics?
  • Am I more interested in computer science?
  • Do I have astute business acumen?

Data analysts specialize in numbers, statistics, and programming. Protectors of data mostly deal with databases to identify data points from complicated and disconnected sources.

Data scientists are much more technical and mathematical, so a background in computer science is useful. To narrow down your choices, take stock of your current and target level of education and experience. If you plan to pursue an advanced degree, choose a program that focuses on experiential learning and hands-on experience, such as Northeastern University programs.

If further study isn’t feasible, why not consider being a data analyst instead? Employers hire analysts without postgrad degrees, and once you gain some industry experience, it is not that difficult to transition from a data analyst to a data science role.

Author’s Recommendations: Top Data Science Resources To Consider

Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.

  • DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
  • MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
  • Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.


While data scientists need to have a combination of business smarts and ample knowledge in computer science, statistics, and mathematics, with enough gumption and motivation, you can learn almost everything else if you know the basic mathematical foundations mentioned above.

Studying the types of math that you’re interested in, not the ones forced on you, will carve your niche in the data science space. Aligning your education with your personality will secure a future career you’ll enjoy and excel in.

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.

  1. Radečić (2020, June 20). Better Data Science.
  2. DataRegressed Team. (2018, January 17). The role of mathematics in data science. Medium.
  3. First module of linear algebra for beginners: Open doors to great careers. (n.d.). Linear Algebra for Beginners: Open Doors to Great Careers | Online.
  4. How much math do you need for data science. (n.d.). Distance Calculus @ Roger Williams University.
  5. How much math do you need to become a data scientist? (n.d.). Learn Coding, Data Science, & Cybersecurity | Flatiron School.
  6. What does a data scientist do? (2020, August 13). Northeastern University Graduate Programs.
  7. You don’t need to know much math for data science. (2019, December 16). Sharp Sight.

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Daisy is the founder of Passionate for the field of Data Science, she shares her learnings and experiences in this domain, with the hope to help other Data Science enthusiasts in their path down this incredible discipline.

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