If you stop and think about it, we use math in a lot of everyday tasks — baking a cake, calculating a tip, setting a budget, and even dancing all use math. Most of the math involved in these tasks can be done in our heads or with the help of a simple calculator. But what about the math a GPS app requires to calculate the position of a moving vehicle? Or the mathematical models used to predict weather patterns?
Potentially millions of calculations are needed to solve large-scale mathematical problems like these, which is why many mathematicians, engineers, data scientists, and others turn to programming languages. But if you’re interested in leveraging the power of coding to solve complex problems, which language should you learn?
That’s actually a complicated question since math is a massive field with many different disciplines and branches. Think: algebra, geometry, calculus, statistics, and probability (just to name a few). There isn’t a single “best” programming language for math. Instead, there are specific languages that will be useful in certain subjects and scenarios. For example, you’d pick a different language for number theory than you would for statistical analysis.
Here are eight programming languages that are popular in the world of mathematics and when they’re used. But if you’re not quite ready to jump into a programming language, you can check out our more math-focused courses first, including Discrete Math, Probability, Differential Calculus, or Linear Algebra courses.
Python is the most popular programming language in the world, and many of the biggest tech companies rely on it for data analytics, machine learning, artificial intelligence, web development, game development, business applications, and more. Python is a top choice because it’s easy to use and read, and it also has many accompanying industry-standard tools, like Pandas and NumPy.
On top of all that, Python has a powerful math module that can perform many advanced mathematical operations, including exponential, logarithmic, and trigonometric functions. The math module is conveniently packaged with the Python release, so you don’t have to install it separately. You just need to import it using the below command, and then you can start using it.
We have a variety of courses that can teach you how to implement mathematical procedures using Python. For example, you can learn how to analyze financial data with Python, perform statistical analysis with Python, visualize data with Python, and analyze data with Python. If you’re new to Python, you can start with the course Learn Python.
R is used extensively in data science and will be a very powerful tool for you to learn if you’re interested in working in this field. Data Scientist, Data Analyst, Data Architect, and Statistician are all roles that use R to develop statistical software and analyze data in both academia and the business world.
R can be used to analyze data from hypothesis testing, such as running t-tests and comparing distributions. And since R was designed with statistical analysis in mind, its graphics and charting capabilities are top-notch. You can access many quality R libraries, such as Ggplot, to create just about any type of visualization imaginable, like histograms, pie charts, scatter graphs, bar plots, box plots, mosaic plots, dot charts, and more. These features make it easy to present and visualize your results.
You can get an introduction to both fundamental statistical concepts and the R programming language with our course Learn Statistics with R; or you can check out our beginner-friendly Analyze Data with R skill path or our course Learn R, which requires no previous coding experience.
Many engineers and researchers use MATLAB, which does numeric computing and can analyze data, develop algorithms, and create models. MATLAB is particularly popular in the automotive industry where it can be used to run simulations that help engineers develop rapid prototypes, perform fuel economy analysis, and test algorithms.
While MATLAB is a programming language, the MATLAB environment is where you access tools, run commands, manage your files, and view and analyze data. And the environment’s command window is where you can write simple commands. For example, you can input a formula and click the Execute button and MATLAB will display the result, and you can also use the MATLAB plot function to create a graph using x and y coordinates.
As a programming language, MATLAB is more straightforward than most languages, and it’s easier to learn because it’s closer to the language we speak than to computer or machine languages. You can use MATLAB with other programming languages, like Python and C/C++, and it also allows for parallel computing using multicore desktops, GPUs, clusters, and clouds.
TIP: If you’re looking for a free alternative to MATLAB, GNU Octave (or just Octave) is an open-source clone of MATLAB that you can easily download from the Octave website. It shares most of the same syntax and features as MATLAB.
4. Wolfram Mathematica
The Wolfram Language powers Mathematica, a software system that’s widely used in scientific, engineering, mathematical, and computing fields. Mathematica is ideal for academic research because it has access to a large number of algorithms across a wide variety of areas, as well as high-performance computing capabilities and powerful graphics and visualization features to present your results.
If you’re working in machine learning, the Wolfram Language comes with many built-in machine learning functions that you can use to train your own models, like Classify and Predict. There are also functions for machine vision, like ImageIdentify, and natural language processing, like LanguageIdentify, and more. Plus, with intuitive function names like these, the Wolfram Language can be easier to read, write, and learn than other languages.
You can check out Mathematica and learn more about the Wolfram Language on the Wolfram website.
Even though Fortran is the oldest commercial programming language, it’s still used in many fields today. It’s popular in the science and engineering disciplines, including applied mathematics, statistics, and finance, and was designed for mathematical and scientific computing.
For example, you could use Fortran for the design of bridges, airplane structures, storm drainage, and factory automation control. It’s also used in Doppler radar weather forecasts, and by farmers who use it in animal breeding practices to help with the selection of multiple traits in livestock.
Fortran-lang.org has a list of resources for learning the language, along with links to courses for specific research areas and best practices.
SAS is another popular programming language for data science and statistical analysis — its name literally stands for statistical analysis software — and it’s capable of working with internal and external databases (or data sets), such as SQL. Many companies, such as Amazon, use SAS in various branches of business for everyday analytics.
For business intelligence, SAS is used for analyzing customers’ needs, fighting fraud, managing risk, and optimizing supply chains. SAS has also played a major role in the field of medicine by helping providers make clinical decisions and monitor risk.
You can learn more about SAS and how to use it on the SAS website.
Julia is a general-purpose language that was primarily designed for scientific computation, machine learning, and statistical tasks. And while Julia was initially popular in the scientific fields (like chemistry, biology, and machine learning), it’s now being used more broadly. Today, you’ll find it used in web development, game development, and more. For example, Julia powers a web app that’s used by financial planners to help their clients prepare for retirement.
Julia’s math-friendly syntax makes it a good match for mathematical computation. The math operations in the language look similar to the way math formulas are written outside of the computing world, which makes it easier for beginners and non-programmers to pick up the language.
Maple is a programming language and interactive problem-solving environment that was designed for mathematics. It’s used in education, applied science, and math-based research. Because Maple was specifically developed for math, it uses data structures and processes that make it relatively straightforward to implement scientific and math functions.
Another advantage of Maple is that it doesn’t require expert programming skills. In fact, it has a large library that contains thousands of specialized functions that make writing useful programs relatively easy. If you’re looking for programming functionality that includes calculus, linear algebra, number theory, or combinatorics, then Maple could be the right fit for your project.
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From cryptography to biology to finance, math programming languages are applicable to tons of fields and professions. In our Fundamental Math for Data Science skill path, you’ll learn probability, statistics, linear algebra, and calculus skills that are necessary to pursue advanced technical data science work. And if you’re looking to use Python for statistical analysis in your work, check out our Master Statistics with Python skill path.