5 Types of Math for Data Science

math used in data science

Types of Mathematics for Data Science

  • Algebra
  • Calculus
  • Probability
  • Linear Algebra
  • Statistics

Data science has taken the world by storm. Data science impacts every other industry, from social media marketing and retail to healthcare and technological developments.

Data science uses many skills, including:

  • data analysis
  • reading comprehension
  • visual adaptation
  • calculation

Not least among these skills are math skills. How much math is needed in data science?  You might be surprised!  Data science goes beyond basic math. Generally speaking, data science involves a considerable amount of math since it is the foundation for many data analysis techniques. The amount of math required depends on the type of work they want to do and their area of focus. While students may choose to specialize in one or two mathematical branches dependent on their professional goals, generally they will learn a variety of data science math skills before they graduate. Here are five different types of mathematics used in data science.



Although algebra is used in data science as more of a stepping stone mathematical system, mastery of algebraic skills is vital to success in the field. Algebra teaches data science students how to calculate variables in data science.  This skill is critical to making predictions and calculating data ranges. Some of the key reasons why algebra is so important include:

Representation of Data:  Algebra provides data scientists with a concise and systematic way to represent and manipulate data.  Data is often represented in matrix or vector forms, so algebra is used to help transform and compute data.

Statistical Analysis:  One of the core components of data science is statistical analysis.  Common statistical methods like linear regression use algebra to model relationships between variables and help make predictions.

Machine Learning Algorithms: Machine learning is used heavily in data science.  Many machine learning algorithms like neural networks and principal component analysis rely on linear algebra concepts.  These algorithms use linear algebra to find patterns in data and make predictions.

Data Visualization: Data visualization involves using aids like charts, graphs and plots to represent information and data.  A data scientist needs a strong understanding of algebraic relationships to interpret visualizations in an accurate manner.

Matrix algebra is a fundamental concept in data science.  It is used to represent and manipulate data and implement various data analysis techniques.


Calculus is another of the different types of math data scientists use on a regular basis.  an extremely abstract mathematical form, but understanding its function is important to data science. Studying calculus assists the student in understanding algorithm calculation, machine learning, and the assessment of changes in data streams and information in real-time or in a posteriori analysis.  Calculus is important in other ways including:

Understanding Rates of Change: Calculus is helpful in understanding how variables change concerning each other.  This is important when analyzing trends and patterns of data, specifically when determining the rate at which a specific variable changes over time.

Probability Distributions: Calculus can be used to derive probability density functions.  It can be used to derive cumulative distribution functions for a variety of probability distributions.  These are used in data science during statistical modeling and hypothesis testing.

Regression Analysis: Calculus is used in both linear and nonlinear regression analysis to find the best-fit curve or line to minimize the sum of squared errors.

The greater majority of data science degree programs require calculus, but students committed to completing their degree will be successful with careful attention to the development of mathematical skills leading to calculus and utilization of mathematical resources at their institution.

Probability Theory

Probability theory involves the use of statistics and prediction and is an important mathematical branch in data science. It uses a combination of continuous and discrete mathematics.  Discrete math involves studying structures that have distinct, separate values while continuous math involves those that have a continuous range of values. Probability is used to track and assess trends in data in every industry.  It is used heavily in retail- and sales-based industries to calculate product success. Probability uses skills the student develops in algebra and statistics classes.  Probably is used in a variety of other ways including:

Bayesian Inference: Probability theory uses Bayesian statistics to update beliefs about hypotheses as additional data becomes available.  It is most commonly used by a data scientist working with uncertain or limited data.

Risk Assessment and Decision Analysis: Probability Theory can be used to assess risk and make decisions.  Decision trees and Bayesian decision theory are used to help make the best choice based on probabilities and expected outcomes.

Sampling Techniques: Probability Theory plays a key role in designing sampling techniques.  Random sampling and stratified sampling can ensure that the data sample accurately represents the population.

Linear Algebra

Linear algebra is a convex of algebraic equations and geometry and is an execution-based form of mathematics versus abstract. Linear algebra also instructs the student in higher-form mathematical concepts such as planes, matrices, and vectors, and to analyze and manipulate potential outcomes visually via its practice. Additionally, it is used in machine learning and value decomposition.  Other ways linear algebra is used in data science include:

Data Representation: Linear algebra is used in matrix-based data representation.  Since data in data science is often represented as matrices and vectors, each row of a matrix can represent a data point.  Each column represents a feature.  Using matrix-based representation allows for efficient storage and data manipulation.

Data Transformation: Linear algebra operations like matrix multiplication and scaling are used to transform and preprocess data.  These transformations are critical for tasks that feature things like normalization and dimensionality reduction.

Graph Theory and Networks: Linear algebra concepts can be used to analyze graph-based data.  Some of these include social networks and transportation networks.


Statistics involves the algebraic calculation of existing data and assessing trends based on that data. This essential math allows data scientists to make estimates on trends and make predictions regarding them, as well as to analyze past trends and draw conclusions from them. Statistics are useful in data collection, analysis, and presentation.  Statistics are used in every industry and have an especially relevant impact on:

  • healthcare
  • retail
  • marketing

Statistics as a field typically encompass both descriptive statistics and inferential statistics.  Descriptive statistics refers to the branch of statistics that includes both summarizing and describing the primary features of a dataset.  They are used to help gain insights into the data and understand things like:

  • central tendency
  • variability
  • distribution

Common descriptive statistics include measurements like mean, median, and mode.

Inferential statistics goes beyond describing the data.  It includes making inferences about a larger population based on the sample data.  The techniques used in inferential statistics include confidence intervals and regression analysis to assess the statistical significance of the relations and make predictions.


According to the Bureau of Labor Statistics, data science is one of the fastest-growing science sectors. Each of these math types represents not just raw knowledge, but a critical skillset to success in data science. Students in data science who learn math will not only find success in their degree program but professionally upon graduation and in the years of their data science careers to come.

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