How much math is involved in Data Science?

While it’s true that math is a core component of data science, you don’t need to know as much as you might think.

Let’s take a closer look at how professionals use math for data science and how much you’ll need to know to pursue a career in this exciting — and lucrative — field.

A Data Scientist's primary role is to mine, examine, and make sense of data. Math plays a role in each of these stages.

Data Scientists use mathematical skills to:

Data Scientists also use mathematical functions to perform data analysis and apply machine learning techniques like clustering, regression, and classification.

Clustering is a way to organize data into clusters or groups that share similarities with each other. It involves some calculus and statistics. A clustering algorithm organizes data into these groups to identify trends and reveal insights at the surface level.

For example, a company with a large customer base can use clustering to segment customers based on their demographics or areas of interest. When you are promoting products, you can better personalize your marketing messages based on data points like customer location, behavior, interests, and more.

Regression analysis is a way to measure how certain factors impact outcomes or objectives. In other words, it shows how one variable impacts another. It uses a combination of algebra and statistics.

Data Scientists use regression to make data-driven predictions and help businesses make better decisions. For example, they can use regression to forecast future sales or to predict if a company should increase the inventory of a product.

Data classification is the process of labeling or categorizing data to easily store, retrieve, and use it to predict future outcomes. In machine learning, classification uses a set of training data to organize data into classes. For instance, an email spam filter uses classification to detect if an email is spam or not.

All data professionals need a solid grasp of essential mathematical concepts, but that’s only part of the skill set needed to analyze data effectively. The ability to work with diverse types of information and create data visualizations are also crucial for gaining meaningful insights.

Data Analysts and scientists handle a wide range of data types, including:

You should know how to use Structured Query Language (SQL) to manage categorical and numerical data. This language allows you to query, organize, and filter information in relational databases.

Data Scientists often transform datasets into accessible graphic representations. These visualizations can reveal previously unnoticed patterns or anomalies in datasets. They also allow data professionals to communicate their findings with non-technical stakeholders.

Platforms like Microsoft BI and Tableau use machine learning models and mathematics to analyze data. They also have intuitive interfaces that allow you to design interactive dashboards and data visualizations. For example, you could use line graphs to represent economic trends over time.

You should also learn how to use data visualization libraries in Python. Popular frameworks include Gleam, Matplotlib, and Plotly. They have built-in templates and themes that you can use to create polished visualizations quickly.

Luckily, you don’t need to be a mathematician or have a Ph.D. in mathematics to be a Data Scientist. Data Scientists use three main types of math—linear algebra, calculus, and statistics. Probability is another math data scientists use, but it is sometimes grouped together with statistics.

Some consider Linear Algebra the mathematics of data and the foundation of machine learning. Data Scientists manipulate and analyze raw data through matrices, rows, and columns of numbers or data points.

Datasets usually take the form of matrices. Data Scientists store and manipulate data inside them and they use linear algebra during the process. For example, linear algebra is a core component of data preprocessing. It’s the process of organizing raw data so that it can be read and understood by machines.

At a minimum, Data Scientists should know Matrices and Vectors and how to apply linear algebra principles to solve data problems.

Data Scientists use calculus to analyze rates of change and relationships within datasets. These math skills help them understand how a change in one variable — such as changing customer preferences — affects another variable, like sales revenue.

Before you begin your data science journey, you should master the two main branches of calculus: differential and integral.

Differential calculus studies how quickly quantities change. Data Scientists should learn its foundational concepts, including limits and derivatives. Python libraries like NumPy and SymPy can speed up this learning process by performing complex calculations efficiently.

Data professionals apply differential calculus to optimize machine learning models and functions. For instance, gradient descent calculates the error between the predicted and actual results. This method allows neural networks and other types of algorithms to adjust their parameters iteratively, reducing errors and improving performance.

Integral calculus analyzes the accumulation of quantities over a specific integral. To effectively apply this technique, you must understand definite and indefinite integrals. Familiarity with Python libraries like SciPy can also help you calculate integrals.

Data professionals use this branch of mathematics to solve many problems in data science, such as forecasting the demand for a product and analyzing revenue. Machine learning algorithms also use integral calculus to calculate probability and variance.

Probability and statistics go hand in hand. Data professionals use these mathematical foundations to analyze information and forecast events.

Statistics is the branch of mathematics that collects and analyzes large data sets to extract meaningful insights from them. Data Scientists use statistics to:

Here are a few examples of statistics principles you’ll need to know to break into the data science field:

In contrast, probability is the likelihood that an event will occur. Data professionals use this method to analyze risk, forecast trends, and predict the outcomes of business decisions.

Data Scientists need to know these basics of probability:

Keep in mind that how much math you need to know may also depend on your role. For example, a junior Data Analyst focuses more on analyzing trends. Although they still need to know how to extract data and interpret information, they work less with complex mathematical concepts. Unless they need to work with machine learning algorithms, they’ll use math for data science less than a senior-level Data Scientist.

This is more of an introduction than an exhaustive list of how much math is involved in data science. If you are interested in learning data science and the math that Data Scientists use, Multiverse offers a Data Fellowship and Data Literacy program.

The emergence of artificial intelligence and big data has fueled the growing demand for data science professionals. The U.S. Bureau of Labor Statistics(opens new window) (BLS) predicts that the number of jobs for Data Scientists will increase by 36% between 2023 and 2033 — much faster than the average growth of all occupations.

Many Data Scientists have flexible hybrid or remote work arrangements and earn lucrative salaries. According to the BLS, the median salary for these professionals is $108,020, with top earners making over $184,000.

Data science offers many opportunities for career progression. Here are three job titles you could pursue as you gain experience:

A Machine Learning Engineer builds, deploys, and maintains machine learning applications. They use data science to design and train machine learning models.

Salary:

Source: Indeed

A Data Architect designs and maintains data structures, databases, and data pipelines. They’re responsible for integrating data from different sources so data flows smoothly throughout their organization.

Salary:

Source: Indeed

A Lead Data Scientist oversees data science teams, mentors junior Data Scientists, and manages complex projects. They also liaise between the data team and stakeholders.

Salary:

Source: Indeed

Math is an important part of data science. It can help you solve problems, optimize model performance, and interpret complex data that answer business questions.

You don’t need to know how to solve every algebraic equation—Data Scientists use computers for that. However, you should become familiar with the principles of linear algebra, calculus, statistics, and probability. You don’t need to be an expert mathematician, but you should broadly enjoy math and analyzing numbers to pursue a data science career.

Multiverse’s Data Fellowship and Data Literacy programs can help you learn the basic mathematical concepts you need to know. However, the focus is on how to apply those concepts in data science.

We'll guide you through the fundamental principles of data analysis, including identifying and solving problems with data. You also don’t pay for tuition—programs are free. You actually get paid to work in a data role and learn whilst you complete the program. The first step is to apply here(opens new window). If accepted, you’ll start learning data science and get on-the-job training at a company that pays you for your time.