While the field of statistics is key to data science, and data science is one of the most interesting paths for someone with a statistics background, data science and statistics aren’t the same thing. Both do involve collecting and analyzing data, usually to make decisions in a business context, but their differences are numerous, and it’s important to know the differences between the two if you’re considering a career in either.
In this article, we’ll explore those differences. By the end of this article, you won’t just know the differences between the two. You’ll also be able to make an informed decision about which career path is right for you in this AI-driven era.
Is There a Difference Between Data Science and Statistics?
Yes, there is a difference between data science and statistics. In general, statistics is the study of numerical or quantitative data to make predictions or draw conclusions about a population. Data science is an applied subset of statistics that uses statistical methods to analyze large amounts of data and understand the results better.
What Is Data Science?
Data science is the process of extracting knowledge from data in various forms. It involves data cleaning, integration, visualization, and statistical analysis of data sets to uncover patterns and trends. Data scientists use tools such as Python, R, SAS, and Tableau to analyze large amounts of data.
What Is Statistics?
Statistics is a branch of mathematics that deals with collecting, analyzing, and interpreting numerical data. It can be used to help identify patterns in data, determine whether there are any significant differences between two or more data groups, and make predictions about future outcomes based on current trends.
Data Science vs. Statistics: Applications
Let’s look at the different applications of data science and statistics to determine which career would suit you best.
What Are Data Science’s Various Uses?
One of the most popular uses of data science is computer programming. This enables the development of decision-making and problem-solving programs. Your search history could be analyzed by a program that makes product recommendations based on your needs or interests. Making artificial AI bots that can carry out tasks or converse with people online is another method.
Marketing strategies are another area where data science is used. Businesses use historical data to forecast what consumers might want in the future and make plans based on that information so they can provide those products before their rivals do. Because they can provide exactly what their customers want without second-guessing themselves, this strategy has helped many businesses succeed in their industry.
What Are Statistics Various Uses?
Numerous sectors, including business, government, and the scientific community, rely heavily on statistics. Statistics can be used for a variety of purposes, such as estimating the number of attendees at an event or assessing the safety or health of a product.
Data Science vs. Statistics: Process
The data science process is much more complex than the statistical process. It involves several steps, each requiring significant time and effort.
What Does the Data Science Process Look Like?
The first step in the data science process is collecting data from various sources. The next step is cleaning the data, removing irrelevant information, and combining different datasets.
Then comes analysis and interpretation, where statistical techniques are applied to the datasets to find patterns or trends that can be used for decision-making.
Finally, there’s visualization, where graphs or charts are generated based on the data analysis results so that people can easily interpret them.
What Does the Statistical Process Look Like?
On the other hand, the statistical process starts by collecting the data through surveys, questionnaires, and interviews. This data is then analyzed and tested for statistical significance.
After this phase is complete, it’s time to create hypotheses that can be tested on the population of interest. The next step is to design an analysis plan that will allow you to test your hypothesis as thoroughly as possible. Finally, you’ll run your experiment and analyze the results before deciding whether or not your theory is true.
Data Science vs. Statistics: Career
Data science and statistics are both highly sought-after skills in the modern workplace, but the two roles are often quite different in terms of the work they involve.
What Does a Data Scientist Do?
A data scientist is an analytical professional who uses qualitative and quantitative methods to solve business problems. They use their expertise to collect, analyze, and interpret data to help their organizations better understand their customers.
What Does a Statistician Do?
A statistician analyzes data using scientific methods and mathematical models to predict future events or trends. Statisticians may also assist in experimental design used by other researchers to test hypotheses about how things work or how people behave under certain circumstances.
Data Science vs. Statistics: Education
Whether you’re an aspiring data scientist or statistician, you need to be aware of the education, skills, and other prerequisites required to get a job in this competitive field.
What Are the Requirements to Become a Data Scientist?
A bachelor’s degree program in computer science, statistical science, or a related field is the minimum requirement for most data science positions. A master’s degree may be preferred, particularly for senior positions. In some cases, a Ph.D. is preferred or required.
However, it’s increasingly common for companies to hire data scientists that have only completed a bootcamp, such as Springboard’s Data Science Career Track.
What Are the Requirements To Become a Statistician?
If you’re considering becoming a statistician, you’ll need lots of math and science classes under your belt—at least through college or graduate school. Statistics is all about numbers, so having a solid mathematical background will help prepare you for this career path.
Data Science vs. Statistics: Career Path
Career paths differ between statisticians and data scientists. Here’s what you should expect.
What Does the Typical Career Path of a Data Scientist Look Like?
Usually, data scientists start their careers by pursuing an entry-level job that involves analyzing data, finding patterns and trends, and communicating the findings to colleagues.
The next step is becoming a data analyst, managing projects and teams, and conducting a deeper analysis of large datasets. You’ll also need experience with machine learning or other advanced statistical tools like R or Python.
After a few years of experience, you can take on more complex tasks than before and become an expert in your field of study. You will be responsible for building statistical models that help businesses make better decisions about their products or services based on historical data sets collected over time.
What Does the Typical Career Path of a Statistician Look Like?
Statisticians are often employed at universities, teaching classes and conducting research. They also work for private companies and government agencies, using the technical skills they’ve learned to analyze data and solve problems.
Some statisticians focus on applied mathematics rather than more theoretical fields of study. The career paths available to these statisticians differ from those who pursue academic careers.
Statisticians often progress from interns or junior-level data analysts to senior-level data analysts at companies with large data science teams.
Choose the Right Path With Texas A&M Statistics Online Learning:
In today’s AI data-driven world, both data science and statistics offer exciting growth opportunities, regardless of your preference. Additionally, Texas A&M Statistics Online Learning can assist you in advancing your career with a top-notch education. With the same curriculum and diploma given to on-campus students, you can complete a reputable graduate degree or certificate—including the online MS in Statistical Data Science – 100% online through Texas A&M’s Statistics Online program. This implies that you can balance your professional and personal obligations while developing in-demand analytical skills. Texas A&M’s online curriculum offers a broad range of courses, from data mining and analysis to Bayesian methods and more, giving you practical, real-world tools employers value across industries. By choosing an online program, you join a community grounded in Texas A&M University’s core values of excellence, integrity, and leadership, and you benefit from a nationally recognized brand that stands out on your resume. Apply now and book your seat!