Data scientists and statisticians have similarities but also differences in their occupations. Let’s look at the two fields of study to see how they are different, as well as the commonalities, to help you decide which one to pursue as a career or to help you better understand how they relate to data.
How Data Science and Statistics are Alike
The two fields are closely related in that they both are part of the process of learning from data. Studying data forms their core, although they do so in different ways.
Both data science and statistics draw conclusions from data sets, which means that the two fields provide vital information to guide organizations’ decisions. The numbers can be useful in several different ways, including behavior prediction, strategies to boost efficiencies, and ways to lower expenses.
They are also both disciplines that experts typically enter after completing university. By taking a specialized degree program, these professionals can enter their chosen field with the skills, tools, and knowledge to excel on the job. They have the education to learn from data to help others effectively.
Furthermore, statisticians and data scientists use methods from a range of fields. They are interdisciplinary. Click here to learn more about earning an online master’s in applied statistics.
The Main Differences Between the Two Disciplines
While data science and statistics are terms often used interchangeably, they are, in fact, distinct. Data science draws on a lot of different methods, including statistics. Thus, data science is the bigger picture that uses stats as a tool to draw conclusions.
Thus, statistics is a component of data science. This means that inferring that data science and statistics are the same is to insult the vastness of the discipline.
Stats is a limited entity with certain tools. A few examples of said tools are mean, median, and variance analysis, as well as skewness.
In comparison, data science uses techniques and expertise to compile data and categorize it into data sets. One of these techniques is statistics, with the goal to draw conclusions to make more informed decisions.
Put another way, one application of data science is statistics. But it also includes exploring data sources and quantifying data in a broader sense. Data mining is a continually evolving concept with many opportunities and methods.
In terms of meaning, statistics include a range of methods that represent data. Meanwhile, data science draws on a range of scientific techniques, including mathematics, design, and engineering.
Skills for Statisticians vs. Data Scientists
For data science, there are more operations involved in the process than statistics. Thus, the skills that workers need to succeed in the two fields are different.
For a statistician, this professional must understand the key methods and principles of statistics. They also need to be proficient in analyzing numbers. Quantitative analysis is also an important part of the job, so understand what this involves (and why) will help the individual do well on the job.
As for a data scientist, they analyze in-depth data problems to reduce them for relevant insights. Extracting any good opportunities within the issue is the purpose here. To do that, they must be good at interpretation, be tech-minded, and have the appropriate computer skills. Data mining is an important skill, as is database management.
Clearly, data scientists must have a strong understanding of statistics and how to use them to comprehend data well. While statistics are a part of data science, it is also a field that many professionals choose to pursue as their long-term career.
Both statisticians and data scientists typically require post-secondary education to advance on this career path. Mathematicians and statisticians generally require a master’s degree. An applied statistics degree program, for example, prepares you for real-world problem solving, strengthens leadership qualities, and improves competencies with software like Python and SAS, which will come in handy for both career paths.
The careers that data science degree grads will pursue will also be different from those who have a statistics degree. The data scientist is qualified for positions such as data analyst, financial analyst, data scientist, and computer systems analyst.
With a statistics degree, you can take on careers that use numerical data to solve business issues. Often, those who have a bachelor’s degree in statistics go onto a more advanced degree; a master’s degree in statistics is one example. Among the careers available with this education are biostatisticians and mathematical statisticians.
Language Differences
There are differences in the languages that each field uses too. In statistics, “estimating” is a word that you will hear again and again. For a data scientist, the equivalent would be “learning.”
In statistics, you would likely be speaking quite a bit about variables. For example, “response variable,” “dependent variable,” and “independent variable.” In data science, “label” and “feature” are relevant terms to use instead.
Another language comparison is “indicator coding” in statistics. A relevant term on the data science side is “one-hot coding.” Finally, data scientists talk about an “example,” whereas a statistician discusses an “observation.”
Concluding Words
The next time someone uses the two terms data science and statistics interchangeably, you can set them right by pointing out the differences. While they have many similarities, which can blur the boundaries between them, there are certainly differences in how the two fields study data.
Based on the details you’ve read, you likely are getting a good idea of which field you want to pursue as a career. Both disciplines are not likely to go anywhere, given the value they have for businesses and other organizations.
The information you uncover will be vital to the decision-making processes of your business. Start now on the desired path by getting a degree that prepares you for a data-centered workplace.
A track that fits with both your personal and professional goals will bring personal fulfillment on the road to success. Aligning your work and interests will make for a satisfying career for many years into the future.
Look forward to taking knowledge from data based on the specific career. The work you do can help to take people beyond their limited beliefs, based on the accurate conclusions you find in the data sets.
