Dan Ariely, a well-known Duke economics professor, once said about big data: “Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”
This concept applies to a great deal of data terminology. While many people toss around terms like “data science,” “data analysis,” “big data,” and “data mining,” even the experts have trouble defining them. Here, we focus on one of the more important distinctions as it relates to your career: the often-muddled differences between data analytics and data science.
Data Analytics vs. Data Science
While data analysts and data scientists both work with data, the main difference lies in what they do with it. Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data scientists, on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.
Download Our Free Guide to Breaking Into Analytics
A guide to what you need to know, from the industry’s most popular positions to today’s sought-after data skills.
Working in Data Analytics
The responsibility of data analysts can vary across industries and companies, but fundamentally, data analysts utilize data to draw meaningful insights and solve problems. They analyze well-defined sets of data using an arsenal of different tools to answer tangible business needs: e.g. why sales dropped in a certain quarter, why a marketing campaign fared better in certain regions, how internal attrition affects revenue, etc.
Learn More: What Does a Data Analyst Do?
Data analysts have a range of fields and titles, including (but not limited to) database analyst, market research analyst, sales analyst, financial analyst, marketing analyst, advertising analyst, customer success analyst, operations analyst, pricing analyst, and international strategy analyst. The best data analysts have both technical expertise and the ability to communicate quantitative findings to non-technical colleagues or clients.
Data analysts can have a background in mathematics and statistics, or they can supplement a non-quantitative background by learning the tools needed to make decisions with numbers.
Skills and Tools
Top data analyst skills include data mining/data warehouse, data modeling, R or SAS, SQL, statistical analysis, database management & reporting, and data analysis.
Roles and Responsibilities
Data analysts are often responsible for designing and maintaining data systems and databases, using statistical tools to interpret data sets, and preparing reports that effectively communicate trends, patterns, and predictions based on relevant findings.
Working in Data Science
Data scientists, on the other hand, estimate the unknown by asking questions, writing algorithms, and building statistical models. The main difference between a data analyst and a data scientist is heavy coding. Data scientists can arrange undefined sets of data using multiple tools at the same time, and build their own automation systems and frameworks.
Learn More: What Does a Data Scientist Do?
Drew Conway, data science expert and founder of Alluvium, created a ven diagram that describes a data scientist as someone who has mathematical and statistical knowledge, hacking skills, and substantive expertise.
Skills and Tools
These include machine learning, software development, Hadoop, Java, data mining/data warehouse, data analysis, python, and object-oriented programming
Roles and Responsibilities
Data scientists are typically tasked with designing data modeling processes, as well as creating algorithms and predictive models to extract the information needed by an organization to solve complex problems.
Choosing Between a Data Analytics and Data Science Career
Once you have a firm understanding of the differences between data analytics and data science—and can identify what each career entails—you can start evaluating which path is the right fit for you. To determine which path is best aligned with your personal and professional goals, you should consider three key factors:
- Your educational and professional background
- Your personal interests
- Your desired career trajectory
1. Consider Your Personal Background
While data analysts and data scientists are similar in many ways, their differences are rooted in their professional and educational backgrounds, says Martin Schedlbauer, associate teaching professor and director of the information, data science and data analytics programs within Northeastern University’s Khoury College of Computer Sciences, including the Master of Science in Computer Science and Master of Science in Data Science.
As mentioned above, data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. To align their education with these tasks, analysts typically pursue an undergraduate degree in a science, technology, engineering, or math (STEM) major, and sometimes even an advanced degree. They also seek out experience in math, science, programming, databases, modeling, and predictive analytics.
Learn More: Is a Master’s in Analytics Worth It?
Data scientists, on the other hand, are more focused on designing and constructing new processes for data modeling and production. In addition, because they use a variety of techniques to comb through data—including data mining and machine learning—an advanced degree, such as a master’s or PhD, is essential for professional advancement, according to Schedlbauer.
“Data scientists are…much more technical and mathematical [than data analysts],” he says, explaining that this requires them to have “more of a background in computer science,” as well.
When considering which career path is right for you, it’s important to review these educational requirements. If you have already made the decision to invest in your career with an advanced degree, you will likely have the educational and experiential background to pursue either path. On the other hand, if you’re still in the process of deciding if going back to school is right for you, you may be more inclined to stick with a data analytics role, as employers are more likely to consider candidates without a master’s degree for these positions.
No matter which path you choose, thinking through your current and desired amount of education and experience should help you narrow down your options.
2. Consider Your Interests
Are you excited by numbers and statistics, or do your passions extend into computer science and business?
Data analysts love numbers, statistics, and programming. As the gatekeepers for their organization’s data, they work almost exclusively in databases to uncover data points from complex and often disparate sources. Data analysts should also have a comprehensive understanding of the industry they work in, Schedlbauer says. If this sounds like you, then a data analytics role may be the best professional fit for your interests.
Data scientists are required to have a blend of math, statistics, and computer science, as well as an interest in—and knowledge of—the business world. If this description better aligns with your background and experience, perhaps a role as a data scientist is the right pick for you.
Either way, understanding which career matches your personal interests will help you get a better idea of the kind of work that you’ll enjoy and likely excel at. Be sure to take the time and think through this part of the equation, as aligning your work with your interests can go a long way in keeping you satisfied in your career for years to come.
3. Consider Your Desired Salary & Career Path
Different levels of experience are required for data scientists and data analysts, resulting in different levels of compensation for these roles.
Data analysts have an earning potential of between $81,750 and $138,000, according to Robert Half Technology (RHT)’s 2019 Salary Guide. Since these professionals work mainly in databases, however, they are able to increase their salaries by learning additional programming skills, such as R and Python.
According to PayScale, however, data analysts with more than 10 years of experience often maximize their earning potential and move on to other jobs. Two common career moves—after the acquisition of an advanced degree—include transitioning into a developer role or data scientist position, according to Blake Angove, director of technology services at IT recruiting firm LaSalle Network.
Data scientists—who have typically earned a graduate degree, boast an advanced skill set, and are often more experienced—are considered more senior than data analysts, according to Schedlbauer. As such, they are often better compensated for their work. According to RHT, data scientists received the highest salary boost for IT professionals from 2016 to 2017 at 6.4 percent, landing them in the $116,000 to $163,500 average annual salary range.
The career trajectory for professionals in data science is positive as well, with many opportunities for advancement to senior roles such as data architect or data engineer.
Try It Out: PayScale provides a Career Path Planner tool for those interested in outlining their professional trajectory. Simply input your field into the search bar and see your potential path laid out for you, including positions at the entry-level, mid-level, senior-level, and beyond.
Which Is Right for You?
Data analysts and data scientists have job titles that are deceptively similar given the many differences in role responsibilities, educational requirements, and career trajectory.
No matter how you look at it, however, Schedlbauer explains that qualified individuals for data-focused careers are highly coveted in today’s job market, thanks to businesses’ strong need to make sense of—and capitalize on—their data.
Once you have considered factors like your background, personal interests, and desired salary, you can decide which career is the right fit for you and get started on your path to success.
This article was originally published in February 2019. It has since been updated for accuracy and relevance.