By: The Capella University Editorial Team with Bradly E. Roh, PhD, DBA and Interim Dean and Vice President for the School of Business, Technology and Health Care Administration
Reading Time: 14 minutes
Job growth projections and salary figures are drawn from Lightcast data and reflect national trends. Actual outcomes vary based on factors such as experience, industry, location, skills and economic conditions, and are not representative of Capella University graduate results.
Becoming a data analyst typically requires picking a learning path that fits your schedule, building skills in Excel, Structured Query Language (SQL) and data visualization and proving them with portfolio projects tied to real business questions.
Many companies are drowning in data and need people who can make sense of it. Data analysts do exactly that, turning a spreadsheet full of numbers into insights a company can act on.
To learn how to become a data analyst, you may need to know which skills to build, in what order and how to demonstrate them. Discover the available education options, the technical and communication skills that matter most, how to build a portfolio and where artificial intelligence (AI) comes into play.
The job roles mentioned throughout this article are examples intended to serve as a general guide. Some positions may prefer or even require previous experience, licensure, certifications, and/or other designations along with a degree. Because many factors determine what position an individual may attain, Capella cannot guarantee that a graduate will secure any specific job title, a promotion, salary increase or other career outcome. We encourage you to research requirements for your job target and career goals.
A data analyst collects, organizes and analyzes data to help guide business decisions. They identify patterns and trends within raw data and use them to produce insights. Businesses can use these insights to improve operations, leverage new market opportunities and support long-term growth.
Analysis typically falls into one of four categories, each answering a different question.
While an analyst’s responsibilities vary by role and industry, the core objective is to use data to answer questions and solve problems. Their main duties and tasks include:
“For example, let’s say that the executives of a national fast food chain are looking to add a new fresh food item to their menu,” explains Melissa Zgola, EdD, program director for the master’s-level information technology (IT) programs in Capella University’s School of Business, Technology and Health Care Administration. “A data analyst could collect and examine data on all fast food purchases over the past 5 years. In doing so, they could identify trends and patterns in the data that would suggest the most popular fast food items being sold by competitors.”
Data analysts, data scientists and data engineers are all related professions, but they’re not the same.
Analysts focus on interpreting and communicating data insights, while data scientists typically go deeper. They often build predictive models and use advanced statistical analysis or machine learning techniques to support innovation.
Data engineers, on the other hand, design and maintain the systems and pipelines that collect, store and process data.
In business environments, data analysts are often confused with business analysts. Business analysts focus on an organization’s processes and requirements, while data analysts focus on collecting, cleaning and interpreting the data.
Each step below builds on the one before it, so it can help to work through them in order.
There’s often no single required credential for becoming a data analyst. Most people choose between four routes, and the right one depends on your schedule, budget and how much structure you want.
| Pathway | Format | Flexibility | Cost | What it demonstrates |
|---|---|---|---|---|
Degree (bachelor’s or master’s) |
Structured program with faculty, online, on-campus or hybrid |
Online programs can fit around work |
Tuition and course materials for the full program |
A comprehensive foundation plus a statistical and business context |
Professional certificate |
Online courses |
Self-paced |
Cost of the certificate courses and certification exam |
Validated core skills on top of existing experience |
Bootcamp |
Intensive and project-focused |
Often a full-time commitment |
A single fee for the whole bootcamp |
Hands-on project work |
Self-study |
Online tutorials and open courses |
Fully self-directed |
Only what you choose to pay for, such as individual courses, books or software |
Initiative, though it can be the hardest path to sustain |
Formal education
A Bachelor of Science in Information Technology with a specialization in Data Analytics and Artificial Intelligence can give you a comprehensive foundation, covering technical skills alongside the statistical and business context that shapes how analysts think.
Some analysts choose to further their studies and pursue a Master of Science in Information Technology with a specialization in Analytics to develop skills relevant to more senior roles.
Capella cannot guarantee that a graduate will secure any specific job title, a promotion, salary increase or other career outcome.
If you’re working, an online IT degree can be a flexible option to earn a degree with support from experienced faculty without pausing your career.
Professional certifications
Recognized data analytics certificates like the Google Data Analytics Certificate and IBM Data Analyst Professional Certificate can help you learn and validate your core analytics skills.
They’re usually narrower in scope than a degree, but they can add credibility on top of your existing qualifications and experience. Some degree programs build certification prep in, too. Capella offers eligible students certification exam vouchers from CompTIA®, Cisco® and SAS®.
Once you’ve chosen a learning route, the next step is to build your technical skills.
Aspiring analysts can get stuck here because they try to learn everything at once. Technical skills generally build on each other, and starting in the wrong place can complicate the learning process. Here’s a sequence that can help make your technical learning more effective and easier to navigate.
Neither Strategic Education, Inc., Capella University, nor any of their affiliates promotes, endorses or has any business relationship with Tableau Public or Power BI Desktop.
Strong communication skills are crucial for data analysts, but they’re often one of the most overlooked topics in data analyst career advice.
Analysts often create value by influencing business decisions. A dashboard no one understands, or a finding no one acts on, isn’t useful.
In practice, this means analysts translate technical findings into plain language and structure insights as a cohesive story: context first, then findings and lastly recommendations.
Use this simple exercise to build your data storytelling skills: after every dataset you practice on, write one paragraph explaining what you found and what a business should do about it.
To help you become a better communicator, Capella’s online Bachelor of Science in Information Technology with a specialization in Data Analytics and Artificial Intelligence and Master of Science in Analytics include capstone projects, so you can practice applying classroom theory to real-world problems.
Portfolio projects can show what you can do with the knowledge you’ve gained, for example, making sense of a messy dataset and turning it into helpful business insights.
How to choose data analytics projects
Instead of projects that simply demonstrate technical skills, pick projects that mirror business questions, like “which product is performing well and why?” or “what’s driving customer churn?”
Use publicly available data from Kaggle, data.gov or your city’s open data portal for your project. Host your projects on GitHub or Tableau Public for easy sharing.
Data analysis portfolio project essentials
Every portfolio project should include:
Here are example ideas for a data analytics project in operations and marketing.
To be hired, data analysts may need to have some experience. If you’re a new graduate or professional from another field, you likely won’t have a previous data-related job title, and that’s okay.
“Analytics is such a diverse field. People are joining the field from a large number of different areas and backgrounds,” says Dr. Zgola.
In its February 2026 research, the Society for Human Resource Management (SHRM) found that 34% of organizations often or almost always use skills-first hiring strategies, which assess candidates on demonstrated skills instead of degrees or job titles alone.
Here’s how you can gain relevant experience.
Capella students and graduates can also use the Career Development Center to more easily connect with professionals and alumni working in data.
Even strong candidates can struggle to land interviews if they don’t present their skills effectively. Here are five tips to help you stand out:
According to Lightcast, there were 70,123 unique job postings for data analysts in the U.S. between August 2025 and August 2026, from 12,245 companies. Monthly postings rose from 4,921 in August 2025 to 5,511 in August 2026, and the median advertised salary across those postings was $98.6K.
The skills listed most often in those postings include Data Analysis (83% of postings), SQL (58%), dashboard (43%), Python (39%), Power BI (38%) and Tableau (33%), alongside computer science (31%) and data quality (28%). Many of them appear in the learning path earlier in this article.
AI tools and features are reshaping how data analysts work. According to Gallup’s December 2025 report on AI use at work, 76% of employees in technology or information systems roles used AI in their role a few times a year or more, the highest share among the industries Gallup reported.
Tools like Copilot in Excel and business intelligence platforms like Power BI can automate routine data cleaning and speed up report generation. ChatGPT and similar AI tools can also assist with basic SQL query writing.
Gartner’s June 2025 data and analytics predictions suggest this shift will continue: “By 2027, 50% of business decisions will be augmented or automated by AI agents for decision intelligence.” Even as more decisions get automated, human judgment is likely to remain important.
AI platforms can surface insights, but they may lack the context and judgment that businesses often depend on, including:
For example, an AI tool can generate a sales trend report in seconds, but it may not be able to pinpoint the exact reason for a spike in third-quarter numbers.
Let’s say the increase was driven by a one-time promotional event instead of genuine revenue growth. If the AI can’t determine the true reason, it might recommend increasing the overall budget, which would be a mistake. That kind of judgment typically comes from a human analyst who understands the business.
So, when you’re preparing to become a data analyst, it can help to prioritize the technical and soft skills that make you a good collaborator with AI platforms.
Analysts who use AI tools can work faster and produce more, but only if they use them effectively. You can learn AI for data analytics, for example, through Capella’s online Bachelor of Science in Information Technology with a specialization in Data Analytics and Artificial Intelligence.
Becoming a data analyst often starts with curiosity and continuous learning. A good next step is to pick one pathway from this article and one small project you can start this month.
If you want structure and faculty support, the Master of Science in Analytics covers areas such as statistics, data mining, predictive modeling and visualization. Capella’s online IT degrees, taught by experienced professionals, include real-world projects to help you prepare to pursue opportunities in data analytics.
It depends on where you’re starting. If you already hold a bachelor’s degree in an unrelated field, a professional certificate can add focused analytics skills to what you have. If you don’t have a degree yet, a bachelor’s program in IT or data analytics can build the technical, statistical and business foundation in one structured path.
It depends on the role, location and industry. Because pay varies widely, it helps to research the specific role you’re targeting. Compare data analyst postings in your region and industry, note the seniority level of each role and look at which tools appear alongside broader responsibilities. This can give you a more realistic picture of pay than any single national figure.
Yes. Many people move into data analytics from other fields by building experience through data projects at their current job, portfolio work with public datasets and online analytics communities. Transferable skills from roles in operations, marketing, finance or IT support, such as working with spreadsheets and reports, can give you a head start as you build your portfolio.
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