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.

1. Python

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.

2. R

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.

3. MATLAB

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.

5. Fortran

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.

6. SAS

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.

7. Julia

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.

You can download Julia and then check out the developer’s in-depth documentation.

8. Maple

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.

You can learn more about Maple, including its wide range of engineering and science applications on the Maple website.

Continue learning with us

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.

Still not sure what to learn? You can check out our sorting quiz, which is kind of like a personality test, but for programming. It’ll help you narrow down what language to learn next.

Math Courses & Tutorials | Codecademy

Mathematics is a subject that is foundational to many technical topics. Whether you’re diving into advanced Data Science content or building foundations for Computer Science, math will provide you with the theories, concepts, and applications necessary to succeed.

If you’ve never touched back-end development before, exploring databases or server-side languages can feel like wading into the unknown. But understanding how to work with databases is a practical skill to have, whether your goal is to get hired as a data scientist or develop an app on your own.

When it comes to database management systems, MongoDB is one of the most popular choices for anyone looking to branch out beyond SQL (Structured Query Language) databases. “Like most databases, MongoDB helps us store information that’s important to our applications,” explains David Patlut, Curriculum Manager at Codecademy. “But MongoDB’s flexibility and versatile cloud features can help push any application to the next level.”

Our free course Learn MongoDB will explain the unique advantages of using a “NoSQL” (short for “not only SQL”) database like MongoDB, and walk you through the fundamentals of working with MongoDB so you can feel confident using it professionally.

Want to know more about MongoDB and how the technology can boost your career potential as a developer? Here’s what MongoDB is, why you should learn it, the types of tech roles that use MongoDB the most, plus tips for getting started with the database software today.

What is MongoDB?

MongoDB is known as a general purpose document-based database, David says. Your typical “relational database” groups information in a table that’s organized in rows and columns based on relationships. For example, an Excel spreadsheet that classifies a list of students’ names and their corresponding ages and grades, or a PostgreSQL database that keeps track of a boutique’s product inventory ​​and location in a storeroom.

MongoDB doesn’t follow the traditional table format for storing data. The data isn’t just stored willy-nilly though; MongoDB stores data in documents, using a syntax that’s very similar to JSON, or JavaScript Object Notation. The main appeal of JSON is that it’s written in plain English and very readable. (Underneath, MongoDB technically uses BSON syntax, which stands for “Binary JSON,” the binary format of JSON that can be parsed faster.)

Here’s a sample snippet of JSON code — as you can see, it looks a lot like JavaScript objects that you’re probably already familiar with.

{
  "student": {
    "name": "Rumaisa Mahoney",
    "age": 30,
    "fullTime": true,
    "languages": [ "JavaScript", "HTML", "CSS" ],
    "GPA": 3.9,
    "favoriteSubject": null
  }
}

All NoSQL databases use their own custom querying language, and fortunately the MongoDB documentation for the MongoDB Query API is relatively easy to read, because it was created with developers in mind. (That said, it helps to have some background knowledge of software development before jumping in — our Code Foundations courses can give you a good lay of the land.)

There are lots of other nuanced differences between SQL and NoSQL databases, which you can read more about here.

The jobs that use MongoDB

Any developer building an application is going to need some kind of database to store, query, and manipulate its data, and which one you choose depends on the particular use case and data. In reality, most enterprises are polyglots — they use multiple programming languages and databases. Knowing how to work with both traditional relational databases and newer databases like MongoDB will set you apart as a job candidate. People who are Back-End or Full-Stack Software Engineers tend to work with MongoDB the most, but you’ll also find it used in fields like data science, machine learning, and of course careers like database administration.

Engineering teams typically use NoSQL databases when they need to store enterprise-level amounts of data (the name MongoDB actually is inspired by “humongous”), and want more flexibility in the structure of their data, or they’re working with semi-structured or entirely unstructured data.

Lots of household names rely on MongoDB to handle massive swaths of data, including Gap, Shutterfly, Verizon, Google, Rent the Runway, and eBay. MongoDB is also very common in gaming: SEGA, Electronic Arts, Square Enix, and Epic Games all use MongoDB.

“For bigger companies, there are typically dedicated back-end engineering roles, and likely some kind of dedicated database administrator roles,” David says. “Data science folks might use MongoDB as well.” For instance, content can easily be scraped and converted directly into MongoDB documents that can be used for natural language processing and machine learning, which is less tedious than having to parse content and convert it into a tabular form.

How learning MongoDB can help you get a job in tech

Most developers will get to a point in their career where they need to learn database technology, David says. Even folks who aren’t building products from scratch can benefit from knowing how databases work, because data drives so many business decisions today. Companies of all sizes rely on collecting, storing, and analyzing massive amounts of data as a key part of their business models.  

So it makes sense that database fluency is a sought-after skill that will make you stand out as a job candidate. “It’s nice for an entry-level job seeker to be able to say, ‘I do a lot of work with SQL databases, but I’ve also done NoSQL, specifically MongoDB,’” David says. (BTW, if you want to get better at “tooting your own horn” in job interviews, check out these tips.)

If it’s your first foray into databases, MongoDB is a great place to start, because it’s widely used by organizations from start-ups to established FAANG companies. “You could put yourself on top of the stack if you have MongoDB on your resume,” David says.

For more experienced developers, they might want to diversify their portfolio of database technologies to include NoSQL products like MongoDB. For example, sometimes companies might switch over to a non-relational database technology for a specific project, in which case developers would need to learn the new database technologies quickly on the job, David says.

Our new free course Learn MongoDB covers these essential MongoDB skills and more. You’ll even get hands-on practice modeling data in MongoDB and creating your own database using MongoDB Atlas.

Understanding how databases like MongoDB work is just one way to round out your technical skills and land your dream job in tech. For more job-search tips, check out the top soft skills you need in tech, a guide for applying to jobs when you don’t have “enough” experience, and more reasons why learning to code can help you in today’s job market.

SQL Courses & Tutorials | Codecademy

SQL is the standard relational data management language. We live in a data-driven world, and there are many businesses that store their information inside large, relational databases. This makes SQL a great skill not only for data scientists and engineers, but for anyone wanting to be data-literate.

Ask a developer to explain the difference between SQL and NoSQL databases, and they’ll probably launch into a high-level conversation about the characteristics of relational and non-relational databases. And while that is the most notable difference between the databases, you might still be wondering what exactly that means for you.

At some point in your coding journey, you’ll need to learn to use a database, explains David Patlut, Curriculum Manager at Codecademy. “Anybody who’s getting into programming is going to run into using a database, because you have to persist [keep] data on any kind of application that you build,” he says.

Differentiating between the types of databases (namely SQL and NoSQL) is a valuable skill for a couple reasons. If you’re building your own app, you’ll have to choose the right database technology for your budget and needs, explains Ben Stone, Senior Curriculum Developer at Codecademy. And if you’re applying for jobs as a Software Engineer, you might be asked to explain the difference between relational and non-relational databases. “It’s a pretty common interview question, so you need to be able to articulate and compare and contrast them,” Ben says.

3 important differences between SQL vs. NoSQL

For starters, when we talk about “SQL vs. NoSQL,” we’re referring to the way data is managed. SQL (Structured Query Language) is a language that you can use to manipulate and query data in a relational database with predefined categories. (Developers often refer to relational databases as just “SQL,” because you have to use SQL to query them.) NoSQL is short for “not-only SQL,” and it refers to any other database that doesn’t store data in tables or queries data by other means than SQL. Here are the main differences between SQL and NoSQL databases that you should know about.

Organization

SQL and NoSQL databases structure and organize data in distinctly different ways.

A SQL, or relational, database enables us to store and access data with clearly defined relations; in other words, each item in a table is connected to the others in some capacity. For example, picture an Excel spreadsheet with people’s names and addresses, or a table of dates and average daily temperatures — in these tables, every field (e.g. temperature) is directly associated with another (e.g. dates). Some examples of relational database management systems (RDBMS) include MySQL, PostgreSQL, and Oracle Database.

With a non-relational database, data isn’t beholden to relationships. “A non-relational database is typically much more flexible than its relational counterpart,” David says. “You can still build relationships, but they’re easier to adjust as the use-case of the database changes.”

Non-relational databases can take a couple different forms, but the most common type is a document-based database, where information is represented in a structure similar to JSON. MongoDB is a popular document database that’s a great choice if you’re starting out, because it’s a general-purpose NoSQL database. “Because MongoDB is considered a general purpose database, it means it can be used for a variety of use cases,” David says. “It can help do most things people want to accomplish with their data.” You can learn MongoDB’s basic functions in our free course Learn MongoDB.

Other types of non-relational database formats you might encounter include graph (like Neo4j), key-value (like Amazon’s DynamoDB), and column-oriented hybrid (like Amazon’s Redshift). MongoDB can actually implement most of these types of non-relational databases.

Flexibility

NoSQL databases tend to be more flexible than SQL ones, because data doesn’t need a predefined schema. “If you’re looking to build applications quickly, MongoDB is perfect. You don’t need to spend much time defining your structure or relationships, you can just put it in the database and decide later how you’d like to change it,” David explains. NoSQL databases are well-suited for situations when your data is only partially structured or you need to quickly build and scale something.

Interestingly, NoSQL databases came on the scene in the 2000s and they were built to fit the Agile framework for software development. NoSQL databases can easily adapt and evolve with the iterative changes and updates that are inherent to the Agile approach. SQL, on the other hand, has been around since the ‘70s, and has a well-established presence in the developer ecosystem and thorough documentation.

SQL databases are more rigid. With most SQL databases, you’d need to manually update all of the fields to ensure that they have the required schema to make changes, or complete lots of migrations to reformat data, which can be tedious, Ben says.

That said, SQL’s squareness and precision can be a positive thing. All relational databases must follow four specific criteria to ensure their reliability: atomicity, consistency, isolation, and durability (aka “ACID”). Taken together, these properties give you peace of mind that your database is going to stay consistent.

(One cool thing about MongoDB is that it supports ACID transactions, even though it’s technically a NoSQL database. And it has a schema validation feature so you can apply more rigidity to your data structure if you need.)

Scalability

Companies that deal with big data or developers working on enterprise-level applications need to consider database scalability, David says. If an app becomes hugely popular, how will a company keep up with its database needs?

Both SQL and NoSQL databases can handle lots of data, but scale differently. SQL databases are scaled vertically, which means that as a company’s database grows, you have to invest in more (often expensive) server hardware and processing units to handle the increasing load. As you scale in a SQL environment, handling the myriad of tables for data modeling can get unwieldy.  

NoSQL was designed to be scaled horizontally, meaning you can add cheaper commodity servers as needed. For this reason, companies that deal with huge swaths of data (including Amazon, Google, and Netflix) often opt for NoSQL databases. With NoSQL databases, you don’t have to deal with table joins, which can be a huge pain in relational databases.

TL;DR

There’s a time and a place for both SQL and NoSQL databases — one is not inherently better than the other. Understanding the nuances between the databases can help you choose the technology that makes the most sense for your needs.

Ready to start learning databases? Here are the courses and paths to check out:

• Learn MongoDB: This free beginner-friendly course will teach you how to structure a document database using MongoDB. It also dives into more advanced operations like atlas clustering.
• Learn JavaScript: Knowing JavaScript is a useful skill in general. In this case, it’ll help you understand JSON syntax better.
• Analyze data with SQL: Start building your own SQL databases and get practice answering technical interview questions.
• Design Databases With PostgreSQL: Relational databases can be elegant and sophisticated, too. In this path, you’ll use PostgreSQL to make fast and sleek relational databases. Back-End Engineer Career Path: Learn all of the major technology that a Back-End Engineer uses, including databases. With this path, you’ll be able to design your own PostgreSQL database and better understand software scalability.

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If you’re not sure where to begin or what to learn next, this is a great place to start. Check out our top coding courses, Skill Paths, and Career Paths.