How Is Statistics Used in Data Science? A Beginners Guide

November 23, 2022

Have you ever wondered why it is vital for a data scientist to study and master mathematical concepts such as Statistics?

Many people believe that data science is all about programming, but practically everything included in data science deals with Statistics and formulating predictions. 

Statistical models help with the extraction of insights from enormous volumes of datasets. It is important in this sector as data science deal with data storage, portability, evaluation, and practical application, and they are vital in organizing raw data and assessing its unpredictability.

Of course, you must be proficient in coding and programming. However, data science is not limited to these domains. In this blog article, you will learn about the significance of Statistics in data science and how Statistics is used in data science. 

What Is Statistics?

Statistics is a collection of mathematical approaches and tools that allow us to solve crucial data queries. It is divided into two sections:

  • Descriptive Statistics: provide strategies for summarizing data by converting raw observations into useful information that is simple to analyze and publish.
  • Inferential Statistics: this provides tools for studying experiments on small data samples and drawing conclusions for the total population.

Statistics and data science are two fields of study that are closely related. Statistics is essential for applied machine learning as it allows us to choose, analyze, and understand models.

Relation between Statistics and Data Science

Statistical data structuring reveals many relevant insights hidden behind your gathered information. For example, knowing the proportion of persons impacted in a medical emergency can help you create solutions. Organizing your customers based on distinct age groups in data science helps you develop adverts and better understand your target demographic.

But you can't discover these facts by gathering individual, irrelevant details. Statistics helps visualize the data in organized formats using tools such as pie charts, line graphs, bar graphs, and more.

Application of Statistics in Data Science

Here are some essential Statistics concepts every beginner must know:

  • Classification

Classification is an overarching concept for data mining approaches. During this phase, we divide the data into subgroups depending on numerous parameters. These elements can be discovered via study, depending on the objectives, and ultimately, we can segregate the data utilizing patterns identified in visual analytics and sampling. 

In data science, classification is a common application. Data scientists and analysts are always challenged with determining whether emails are spam or essential. Similarly, AI categorizes news based on prior searches, frequently visited, read times, and more.

However, there are more categorization approaches to classify data. To forecast qualitative responses as precisely as possible, you must constantly update your system and methodologies.

If programming is your strong suit and you want to work in the data science field, online Statistics for Data Science courses can help you brush up on your number game. The online course offers live interactive sessions led by Industry experts. Learn at your pace with real-life projects anywhere, anytime.

  • Logistic Regression

Logistic Regression is the most prominent classification approach that predicts qualitative responses based on observable patterns. The process signifies the values of an unknown variable based on its association and the values of other variables on the chart.

The procedure, however, is more complex than it appears. Logistic Regression seeks the closest relationship between the two variables on the graph—the dependent and independent variables.

Data Science employs the method in various fields. Using logical regression, AI can anticipate whether a picture shows an object, dog, cat, person, or something else.

  • Resampling Methods

Resampling is a common strategy for analyzing big data samples impartially and precisely. While examining enormous volumes of data, the approach eliminates the uncertainty of population parameters.

The approach collects samples from large amounts of data to produce a smaller and more specific sampling distribution that depicts the original data. The resampling method confines all potential study outcomes, improving precision and lessening bias.

Benefits of Learning Statistics for Data Science

The following are the benefits of learning Statistics for data scientists:

  • Assists in Data Management

Accurate data classification is required for businesses to develop marketing strategies. Not only that, but data classification and structuring assist the organization in improving its goods and services in a targeted manner. Unorganized data is useless and a waste of time and resources in data research.

  • Helps to Identify Patterns

Data collection can be intellectually, physically, and financially draining. Concentrated research may save you a lot of time and money. With the help of Statistics, data scientists identify trends early in their investigation, allowing them to concentrate on their research topic effectively.

  • Assists in Estimation and Probability Distribution

Data analytics and machine learning are built on logistic regression, cross-validation, and other techniques that assist the machine in predicting your next move. 

  • Makes Data Visualization Easier

Visualization tools such as histograms, pie charts, and bar graphs contribute significantly to big data research by making data more interactive and informative. They give an engaging and simple way of interpreting complicated data.

These statistical techniques assist in the early detection of trends and make them understandable to the layperson. As a result, reaching conclusions and developing action plans becomes easier.

  • Assists in Accounting for Data Variability

Statistics can account for a wide range of variables in model-based data analytics, such as clusters, time, and location. Failure to use statistical procedures can result in data analysis that does not account for variability, resulting in erroneous estimations.


Statistics, therefore, contributes significantly to data science advancements to the levels it has reached. Every algorithm, big data analysis, or specialized market research needs an intermediate level of statistical understanding.

Statistics is the instrument for understanding, interpreting, and concluding from unorganized data. If you are a beginner looking for a career in data science, it's time to elevate your Statistics game.

You can check out the Introduction to Data Science course for beginners and working professionals. The course prepares aspiring students for immediate job opportunities and long-term career planning. 

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