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How much math is involved in Data Science?

By Team Multiverse

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Contents

  1. How do Data Scientists use math?
  2. What types of math do Data Scientists need to know?
  3. Boost your skills with comprehensive data scientist training

If you’ve considered becoming a Data Scientist, you might be put off by how much math is involved in data science. While it’s a core component of data science, you don’t need to know as much math as you might think.

Let’s take a closer look at how Data Scientists use math and how much you’ll need to know to pursue a career in data science.

How do Data Scientists use math?

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 math to:

  1. Understand and use machine learning algorithms
  2. Perform data analysis
  3. Identify patterns in data
  4. Forecast trends and growth

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

Clustering

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 patterns 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 marketing, you can better personalize your marketing messages based on data points like customer location, behavior, interests, and more.

Regression

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.

Classification

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.

What types of math do Data Scientists need to know?

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.

Linear algebra

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.

Calculus

Most data science fields require comprehension of fundamental calculus principles and their effect on machine learning models. However, calculus for data science is not like your high school or college calculus class.

Here are some calculus concepts that Data Scientists may use:

  1. Gradient descent - an optimization algorithm that trains machine learning models to learn over time and become more accurate
  2. Multivariable calculus -  machine learning uses multivariable calculus to build predictive models

Statistics

By far, statistics is the most important math you need to know for data science. Statistics is the branch of mathematics that collects and analyzes large data sets to interpret meaningful insights from them. Naturally, almost every aspect of data science uses statistics.

Data Scientists use statistics to:

  1. Collect, review, analyze, and form insights from data
  2. Identify and translate data patterns into actionable business insights
  3. Answer questions by creating experiments, analyzing and interpreting datasets
  4. Understand machine learning and predictive models

When combined with data science, statistics can help answer business questions like:

  1. What KPIs should you use to measure success?
  2. Which features are the most important to your users?
  3. What experiments do you need to test a strategy?

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

  1. Statistical experiments - how to create statistical hypotheses, do A/B testing and other experiments and form conclusions
  2. Data visualization - how to present your insights and communicate your statistical findings so they are easy for multiple stakeholders to understand

Probability

This math concept usually goes hand in hand with statistics. Probability is the likelihood that an event will occur.

Making predictions is a large part of data science. For instance, a Data Scientist may be tasked with identifying and quantifying how certain factors impact the likelihood of someone completing the checkout process.

Using statistics and probability, they may find that adding one-click payment options like Apple Pay increases the checkout completion rate by 40%.

Data Scientists need to know these basics of probability:

  1. Distributions
  2. Statistical significance
  3. Bayes' Theorem
  4. Hypothesis testing

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 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.

Boost your skills with comprehensive data scientist training

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.


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