Statistics vs Data Science: Key Differences Explained

You must have seen two job titles coming up repeatedly on hiring websites. Those 2 job titles are statistician and data scientist. The pay scale for these roles is usually similar, and the math seems to be similar. Which is why most people feel a simple question jiggling in their head: what is the difference between statistics and data science? 

They both are different yet are closely related. One of these fields is much older, whereas the other one is quite new and borrows heavily from it and then adds computing, engineering, and business skills on top. 

This article will help you walk through the concept of statistics vs data science in easy and readable language. You will read about the meaning of each field, why and where they tend to overlap, what the difference between them is, and which path is better for you. 

What do you understand by Statistics?

Statistics is the science of gathering, managing, and understanding numbers so that we can make sense of the world. It has been around for centuries now. Governments used traditional forms of statistics to count people and track trade way before computers existed. 

The mathematical backbone that everyone uses today developed mostly in the 1800s and early 1900s, through the work of scientists such as Karl Pearson and Ronald Fisher. A statistician's main job includes answering a question like this: if you have a small sample of information, what can you actually say about the larger group it has come from? 

For example, there is a hospital that wants to enquire about how the new medicine works. For that, the hospital will test the medicine on a smaller group and then use statistics to judge if the results are authentic or just a fluke. This will be decided on the basis of rules of probability, confidence, and error created over a very long period of time. 

What do you understand by Data Science?

Data science is a newer field. Its name itself has a gripping history behind it. Peter Naur used the words “data science” in a computing book back in 1974. Statistician William Cleveland raised an argument in 2001, claiming that the field of statistics must grow in order to include more computing skills, and he used the term “data science” to describe the broader field. The job profile of a data scientist, as most people see and use it today, only became popular in the 2010s, as companies started to collect much larger amounts of data than before. 

Data science is a combination of statistics, computer programming, and knowledge of a specific business or subject. A data scientist does not only study a small and organised sample. Instead, the job starts with unorganised, large, and raw data, such as website clicks, sensor readings, social media posts, medical scans, or years of sales records. 

The basic goal of data science is broader than that of statistics alone. A data scientist gathers, organises, studies, and models the data generally for developing software or automated systems that use those models every day. 

Also Read: What Is Data Science in Simple Words

Where do statistics and data science overlap? 

Before you jump into the differences between statistics and data science, it is important that you see the common ground that they share, as the overlap is quite large. 

Both data science and statistics depend upon the same mathematical base, such as probability, distributions, hypothesis testing, and regression. It is not possible for an individual to perform good data science without understanding statistics first. The separation between the two fields does not act like a wall between subjects that are completely unrelated. Instead, it acts more like 2 circles that overlap heavily, where data science acts as a larger and newer circle.

Both fields even share the same core purpose: converting raw numbers into a true and important answer. Both fields rely upon evidence instead of guessing. 

This is the reason that various discussions of statistics vs data science end up being repetitive and recurring. 

Statistics vs Data Science: The Core Differences

Now let's look at Statistics vs Data Science point by point, since this comparison is usually what people actually want to know.

Which path should you choose between both? 

This is usually the core reason people search for statistics vs. data science. It is not the curiosity about the concept but a decision about where you must invest the coming few years of study and work. 

Statistics may be accurate for the following individuals: 

• Those who prefer doing important, formal research and organised proof.
• Individuals who are inclined towards health, government policy, or academic research. 
• People with an interest in working with smaller and organised datasets.
• Those who are looking to build a career on strong mathematical theory.

Data science may be suitable for the following individuals: 

• Those who enjoy writing code as much as maths.
• People who are willing to work in industries like technology, retail, or finance. 
• Individuals who want to create tools that other people can use directly. 
• People who are comfortable with learning new software continuously because the tools in this field shift quickly. 

Ready to Build Your Career in Data Science?

Understanding statistics is an important step toward becoming a data scientist. Take the next step by developing practical data science skills and learning how to work with real-world data.

Explore the Data Science Program at Learning Saint and take a step toward your career goals.

Conclusion:

So what’s the real difference between Statistics and Data Science after all the noise is removed? Statistics is the older, deeper discipline, based on strict mathematical proof, usually applied to smaller, well-structured data sets. Data science is the newer, broader field that takes those same statistical ideas and adds in programming, engineering, and business skills, often on much bigger and messier data.

Statistics versus Data Science is, simply put, a story of growth, not replacement. One does not substitute the other. Statistics is the backbone of data science. Modern computing power has given statistics a much bigger stage to work on, in the form of data science.

When choosing a career, a course, or just trying to hire the right person for a project, remember the simple version: a statistician proves an answer is trustworthy, and a data scientist often builds the whole system that finds, tests, and delivers that answer every single day.