If you are going to pursue a career in the field of data science, you need to know the difference between data science and statistics. And while some experts will say they are one in the same, there are some key differences worth knowing. In fact, knowing the difference between the two can help you decide the direction you want to take your career.
What Is Data?
To perform statistics, you must have data. So whether a person is a data scientist or a statistician, data is going to be at the core of this person’s career.
Big data has become extremely prevalent over the past decade. We are seeing the use of data in our every day lives more and more. Whether it be using GPS navigation or scrolling through Facebook, data is used to present us with information that we are interested in. Along with big data has come about a variety of terms, including data mining and data sciences. And although these terms are centered around data, they do differ slightly in their meaning. For those who don’t work in data sciences, the terms likely mean the same thing. For those who have a data science degree, though, it is easier to pinpoint the differences. Whether you are a data science guru or someone simply interested in knowing the difference, it is helpful to look at the definitions of data science and statistics. According to Investopedia, here are the definitions of the two terms:
“Statistics is a form of mathematical analysis that uses quantified models, representations and synopses for a given set of experimental data or real-life studies. Statistics studies methodologies to gather, review, analyze and draw conclusions from data.”
“Science data is a field of Big Data which seeks to provide meaningful information from large amounts of complex data. Data Science combines different fields of work in statistics and computation in order to interpret data for the purpose of decision making.”
After reading through their definitions, it is clear to see why the terms become indistinguishable from one another. Both terms focus on using data to draw meaningful information. Once you take a closer look at the definitions, though, you can see that data science focuses on a broader domain of data, while statistics narrows its focus on a specific fragment of data science. To put it in as simple terms as possible, data science provides the big picture, while statistics provide the details.
Data Science and Statistics: Viewing the Two as Toolboxes
You can also acquire a better understanding of the difference between data science and statistics by viewing the two as toolboxes. Statistics is going to have a much smaller toolbox than data science. The tools kept in a statistician’s toolbox will likely be any of the following:
- Regression analysis
- Variance analysis
- Frequency analysis
- Mean and median equations
In a data scientist’s toolbox, however, there are going to be many more tools:
- All of a statistician’s tools as well as tools and principle models stemming from correlating disciplines
Does this mean that a data scientist has more knowledge than a statistician? No, not exactly, but it does mean that a data scientist can deduce data to an even further degree than a statistician, providing their employers will strategic interpretations that can fuel decision-making processes farther than interpretations made by statisticians alone.
Close Look at the Different Job Duties of a Statistician and Data Scientist
To understand the difference between statistics and data science, it is helpful to look at the job duty differences between these two roles. Regardless of the job title, both data scientists and statisticians spend their time gathering information. And much of the time, this data gathering will be performed for very similar purposes. The difference, however, can be seen in how they collect this data. A data scientist will likely gather data in a much larger quantity than a statistician. In fact, data scientists spend a large portion of their time creating computer programs to collect data for them. Statisticians, on the other hand, tend to use traditional methods of data collection, including experiments and surveys.
Job Responsibilities of a Data Scientist
If you have ever watched a show on Netflix, then you know how good the platform is at recommending new shows and movies. How does Netflix do this? It’s actually quite simple. It uses algorithms to guess which shows you are most likely to be interested in. These algorithms are created by data scientists. If you choose to pursue the career of a data scientist, your duties will likely include:
- Researching data science trends
- Developing new ways to collect, sort, and manage data
- Create visuals of the data you collect through the use of graphs and charts
- Create reports including the visuals
- Enhance existing algorithms
- Use business data to improve operational processes
Job Duties of a Statistician
Statisticians spend their time understanding patterns. They collect data and use various tools to pinpoint these patterns. Statisticians, the same as data scientists, are needed in every industry and are especially beneficial in the fields of healthcare and technology. If you choose to become a statistician, some of your job duties will likely include:
- Creating surveys and experiments
- Pinpointing samples to study
- Perform research and lead research teams
- Create reports on your findings
- Test the validity of data by creating and performing further analyses
You hopefully have a better understanding of the difference between data science and statistics. If you are considering a career as either a statistician or data scientist, your preferred skill set will vary depending on the career you choose. For example, as a statistician, you need to be well-versed in the various tools and core concepts of statistics. As a data scientist, though, you need to master your arithmetic skills as well as those relating to visualization, analytics, interpretations, and more. Regardless of the career choice you make, you can expect to earn good money as a statistician or data scientist. As a statistician, you will likely earn upward of $80,000 a year, while as a data scientist you can make more than $120,000 a year. And while the money is great in both of these career fields, it’s important to note that you will spend several years in school earning a degree. In fact, most people working in these fields have a Ph. D. To keep up with the competition found in this career, you will want to earn an advanced degree as well.