The technology and computer industries grow and adapt to change as the world.
There are numerous educational and employment opportunities for people interested in a computer or information technology-related profession. Deciding which path to take can be difficult.
Understanding the differences between data science versus statistics will help you make a more informed career decision. Let’s dig deeper into this tutorial!
Similarities and Differences
There are many parallels between data science and statistics. Both are concerned with obtaining data, analyzing, and solving real-world problems.
We’ll compare them in terms of description of duties, requirements for education, job prospects, pay, and skills required to succeed in each field.
A statistician collects and displays numerical data to help organizations make sense of it, find trends, and make forecasts. One of the common objectives is to design data gathering trials and evaluate the results.
Data scientists use their analytical, statistical, and programming skills to acquire, analyze, and understand massive data sets. He then uses this information to develop data-driven solutions to challenging business problems.
In a nutshell, statistics is a math-based discipline that aims to gather and evaluate quantitative data. On the other hand, data science is a multidisciplinary subject that uses scientific techniques, procedures, and systems to extract information from various data sources.
Although there is a core difference between the two professions, they are closely related. Statistics are at the heart of advanced machine learning algorithms in data science, identifying and converting data patterns into usable evidence.
Data scientists use statistics to collect, assess, analyze, derive conclusions from data and apply quantified mathematical models to relevant variables.
You’ll need a bachelor’s degree in data science to work as a data scientist. Most data science positions, however, will need a master’s degree.
Meanwhile, a bachelor’s degree in statistics, applied math, computer science, or a related discipline is necessary to get a foot in statistics.
Since you’ll be dealing with complex statistical software programs, it’s a good idea to take a mix of math and IT courses.
To meet a requirement for data scientists, you have to have skills to work with algorithms. Math, machine learning, and artificial intelligence can be helpful for students to get this experience.
Moreover, you’ll also require good analytical abilities. You must analyze data, define problems and questions, and devise techniques to provide the data needed to answer the question.
On the other hand, statisticians require mathematical skills. Your employment necessitates the capacity to execute complex calculations and choose the best solutions for a particular project. So, you need to know calculus, linear algebra, and probability in addition to statistics.
Communication skill is another necessary thing for a statistician. Together with presenting their findings, they collaborate with other team members to gather data or conduct analysis.
According to the Bureau of Labor Statistics, there is a high demand for data scientists. These jobs may grow by 22% over the next decade, much faster than the overall job growth rate.
Meanwhile, the total employment of statisticians may expand by 33% between 2020 and 2030.
The average annual income for a government statistician in the United States is $105,570. The average pay for data scientists is $124,540.
Your payment will vary depending on various criteria such as your experience, qualifications, region, and industry. These figures are just to be used as a guide.
|Transform raw data into the essential insights that a company needs to thrive and compete
|Address real-world issues using statistical approaches and models
|A bachelor’s degree in computer science or a comparable discipline
|A bachelor’s degree in statistics, computer science, applied math
Processing large data sets
|grow 22% from 2020 to 2030
|grow 33% from 2020 to 2030
Which Is For You?
The field of data science is tremendously rewarding and has great potential for future growth with a competitive salary and plentiful benefits.
Careers in statistics are also often well-paying, provide levels of job satisfaction, and promote a healthy work-life balance.
They both play essential roles.
Become a statistician if you wish to concentrate on testing, distribution, experimental design, and diagnostic charting. If you want to practice coding and automating machine learning models, become a data scientist.
If you prepare enough technical knowledge and related skills, being a statistician is not tricky. Those with a mathematics or statistics background are most suitable for this job.
Although there are various ways to become a data scientist, working in a relevant entry-level position is an excellent place to start. Looking for jobs that include a lot of data, like statistician or data analyst, will help.
Both of these jobs are vital. If you wish to focus on significance, analysis, experimental design, distribution, and diagnostic charting, become a statistician.
Become data scientists if you want to do more software engineering, such as coding and machine learning model automation.
Data science is a subset of statistics. Statisticians can gather data and utilize it to conduct analysis like data scientists. So, statisticians can do data science.
For data scientists, statistics is an essential component and a must-have skill. We can only be tool experts if we have sufficient statistical expertise.
A data scientist is a person who is better at statistics than any statistician. In other words, statistics is an inextricably linked component of data science.
Data science is not a rebranding of statistics. But, data science occurs when combining statistical research with programming.
Statistics is a math-based discipline that aims to gather and evaluate quantitative data. Data science is a multidisciplinary subject that uses scientific techniques and systems to extract information from various data sources.
Understanding the distinctions between data science and statistics will assist you a lot in finding the appropriate career and a professional route.
The comparisons in this article hopefully aid you in visualizing the work of these two jobs and allowing you to have the right option. Thank you for reading!