If you’re considering a career in data or business, you may have come across these three popular job titles: business analyst, data analyst, and data scientist. Though the responsibilities may overlap, they are certainly different positions, but what exactly are those key differences — and which position might be best for you?
In this career guide, we’ll provide you with the information you need to make an informed decision if you’re contemplating one of these important positions.
What is a Business Analyst?
A business analyst, as defined by LiveAbout, listens to leaders, stakeholders, experts, etc. and helps a company get where it needs to be. “According to the BABOK® Guide produced by the International Institute of Business Analysis, or IIBA®, business analysis involves understanding how organizations function to accomplish their purposes and defining the capabilities an organization requires to provide products and services to external stakeholders.”
This position is more focused on the big picture. As career resource Indeed explains, business analysts “work within the core of many companies small and large to improve and streamline processes that help an organization meet its objectives and reach goals. They help initiate greater employee productivity and they ultimately support business information systems that provide solutions to a variety of business issues across multiple departments.”
What is a Data Analyst?
As the name implies, a data analyst works with and analyzes data. CIO explains it this way: “Data analysts work with data to help their organizations make better business decisions. Using techniques from a range of disciplines, including computer programming, mathematics, and statistics, data analysts draw conclusions from data to describe, predict, and improve business performance.”
CareerExplorer compares a data analyst to a sleuth — “They study what’s happening now to identify trends and make predictions about the future. They are like detectives, figuring out how things work and helping to make sense of everything.”
This is a much more technical, numbers-oriented position than a business analyst, and one that is in high demand. According to Investopedia, “skilled data analysts are some of the most sought-after positions in the world.”
What is a Data Scientist?
This position is also data-centered, similar to a data analyst, but typically more focused on long-term research and prediction.
Here’s the difference, according to CIO: “A data analyst might help an organization better understand how its customers use its product in the present moment — what works and doesn’t work for them. A data scientist might use the insights generated from that work to help design a new product that anticipates future customer needs.”
SAS Software defines this position as “part mathematician, part computer scientist, and part trend-spotter.”
“A data scientist really is a scientist at heart,” says Scott Beliveau, chief of the enterprise advanced analytics branch within the U.S. Patent and Trademark Office’s Office of the Chief Technology Officer in a U.S. News & World Report article. “But rather than using chemicals or other things, a data scientist uses data — numbers, zeros, sometimes it’s textual information — to try and solve and answer problems.”
U.S. News & World Report also ranks data scientist #4 in Best Technology Jobs, #7 in Best STEM Jobs and #8 in Best 100 Jobs.
7 Key Differences [Comparison Table]
Now that we’ve discussed all three positions, let’s explore the key differences among them.
| Business Analyst | Data Analyst | Data Scientist | |
|---|---|---|---|
| History of position | This position has been around for years but has become more prominent in government organizations | This has become a more popular position throughout the years, especially as technology and computing has advanced. Data analysis comes from statistics, which does have a long history | The field of data science is still relatively new but has grown out of statistical analysis and data mining. According to Oracle, the title “data scientist” emerged by 2008 |
| Responsibilities | Brainstorms business solutions, gains an understanding of a company’s processes relevant to goals, creates a detailed business analysis, budgets and forecasts, evaluates current business processes and makes recommendations for improvement, researches business processes | Collects and interprets data, analyzes data results, communicates results to business and stakeholders, identifies patterns and trends in data | Collects, interprets, and publishes data. Focused on long-term research and prediction. Creates reports for businesses. Builds tools to automate data collection. |
| Average salary | Ranges from $69,000 to $87,000 | Ranges from $86,000 to $147,000 | Ranges from $105,000 to $200,000 |
| Skills needed | Computer knowledge, problem-solving skills, analytical skills, communication skills, math and writing skills | SQL, Microsoft Excel, Tableau, Python, R, SAS, PowerPoint, Microsoft SQL Server, Oracle, analytical and mathematical skills, communication skills | Data mining, programming (examples include Python, R and SQL), statistics, probability, math, visualization, optimization, predictive modeling, machine learning, deep learning, data engineering, cloud computing, time series analysis, natural language processing |
| Tools/Certifications | The International Institute of Business Analysis (IIBA) offers numerous certifications, including the Certified Business Analysis ProfessionalⓇ and the Certification of Capability in Business AnalysisTM | Associate Certified Analytics Professional (aCAP), Certified Analytics Professional, Cloudera Certified Associate (CCA) Data Analyst, Microsoft Certified Data Analyst Associate | Common data science certifications include: Data Science Council of America (DASCA) Senior Data Scientist, DASCA Principle Data Scientist, Dell EMC Data Science Track, Google Professional Data Engineer Certification, IBM Data Science Professional Certificate, Certified Analytics Professional (CAP), Microsoft Certified: Azure Data Science Associate |
| Education | Bachelor’s degree in related area (finance, accounting, business administration, economics, statistics, political science) | Bachelor’s degree in related area (math, information management, computer science, statistics, economics) A master’s degree is not required, but some employers prefer one. | Bachelor’s degree or higher in computer science, math, software engineering, statistics, data science or related field. About 94% of people in data science hold advanced degrees. |
| Work Environment | Typically works in an office setting but may need to travel to collect information or meet with others | Typically works in an office setting. May work for small businesses, financial firms, tech companies, government, etc. | Typically works in an office setting; may work in academia, finance, government, retail or e-commerce |
Sources: LiveAbout, Robert Half, CIO, Indeed, Indeed (Business Analyst), Indeed (Data Scientist), Integrate.io, Oracle, Salary, ZipRecruiter, CareerExplorer, The Burtch Works Salary Report: 2022 Edition, University of San Diego, edureka! and CIO.
How to Prepare for a Business Analyst, Data Analyst or Data Scientist Position [How to Prepare]
In many (or most) cases, a bachelor’s degree in a related field is required. In some instances, a master’s degree in a related field is preferred or even required for top-level positions. Even if an advanced degree isn’t required, it shows your commitment to continued education in your field. It may also give you the experience you need and an edge over the competition.
In addition to an advanced degree, industry certifications are important. You should also consider an internship or networking opportunities.
This information was brought to you by the University of San Diego’s Master of Science in Applied Data Science. This program is designed to equip graduates with the technical strategies and skills they will need to apply powerful, modern analytical tools to real-world applications. Designed to be completed in 20 months, the MS-ADS is ideally suited to those with a background in science, mathematics, engineering, or technology but is also structured to train those from other backgrounds who are motivated to transition into data science.
