Data analysts help organizations turn information into useful insights. They collect, clean, organize, and analyze data to identify patterns, answer questions, measure performance, and support business decisions.
For example, a healthcare organization might use data analysis to identify trends in patient wait times. A retailer might analyze purchasing data to determine which products sell best. A marketing team might examine campaign data to understand which channels generate the most customers.
There isn’t one required path to becoming a data analyst. Some professionals earn a bachelor’s degree in data analytics, statistics, computer science, business administration, or a related field. Others transition into analytics from another career by developing skills in SQL, spreadsheets, data visualization, and statistics. Certificates, portfolio projects, internships, and work experience can also help demonstrate those skills.
A typical path looks like this:
- Learn the fundamentals of data analysis.
- Earn a relevant degree or develop job-ready technical skills.
- Learn commonly used data analysis tools.
- Build a portfolio of projects.
- Gain practical experience.
- Apply for entry-level analytics positions.
- Continue developing your technical and business skills.
What Does a Data Analyst Do?
Data analysts examine information to help organizations understand what is happening and determine what they should do next.
Their work can involve much more than creating spreadsheets or charts. A data analyst may start with a question from a manager, locate the appropriate data, clean it, analyze it, create a visualization, and then explain the findings to people who may not have a technical background.
Common responsibilities include:
- Collecting and organizing data
- Cleaning inaccurate, incomplete, or duplicated information
- Querying databases
- Analyzing datasets
- Identifying trends and patterns
- Performing basic statistical analysis
- Creating reports and dashboards
- Building data visualizations
- Communicating findings to managers and other stakeholders
- Making recommendations based on data
The specific responsibilities depend heavily on the employer and industry.
For example, a marketing data analyst might examine website traffic, advertising performance, conversion rates, and customer behavior. A healthcare data analyst might work with patient outcomes, staffing information, costs, or quality measures. An operations analyst might analyze inventory, staffing, production, or supply-chain data.
Google describes data analysts as professionals who prepare, process, and analyze data, create visualizations, and provide recommendations based on their findings.
How to Become a Data Analyst
1. Learn What Data Analysts Actually Do
Before choosing a degree or certification, learn about the work itself.
Data analysis involves more than knowing how to use Excel or write SQL queries. Successful analysts need to understand the question behind the data and determine which information can help answer it.
Imagine that a company’s sales have declined by 10%.
A data analyst might investigate:
- Which products experienced the largest decline?
- Did sales fall in every region?
- Did customer traffic change?
- Did prices change?
- Are returning customers purchasing less frequently?
- Did a marketing campaign end?
- Did the decline occur during a particular month or season?
The analyst then uses available data to investigate these questions and communicate the results.
This combination of technical skills, analytical thinking, and communication is what makes data analysis useful to an organization.
2. Earn a Degree or Develop Relevant Skills
A bachelor’s degree is a common starting point for many analytical careers, but there is no single degree that every data analyst must have.
Potential degree fields include:
- Data analytics
- Data science
- Business analytics
- Statistics
- Mathematics
- Computer science
- Information systems
- Information technology
- Economics
- Finance
The best choice depends partly on the type of analyst role you want.
Someone interested in business reporting might prefer business analytics or information systems. Someone interested in statistical analysis may prefer statistics or mathematics. Someone who wants a stronger programming foundation may choose computer science or data science.
A degree can also be useful for people who want to keep their options open. Data analytics skills can lead to related positions in business intelligence, operations, finance, marketing, healthcare, and other areas.
3. Learn Data Analysis Tools and Technologies
You don’t necessarily need to master every analytics tool before applying for your first job. However, developing a strong foundation in several commonly used technologies can make you more prepared for entry-level work.
Excel and Spreadsheets
Excel remains useful for organizing data, performing calculations, creating charts, filtering information, and conducting basic analysis.
You may want to learn:
- Formulas and functions
- Pivot tables
- Charts
- Conditional formatting
- Data cleaning
- Lookups
- Basic statistical calculations
SQL
SQL, or Structured Query Language, is one of the most useful technical skills for people pursuing data analyst positions.
Analysts use SQL to retrieve information from databases. You should become comfortable with concepts such as:
- SELECT statements
- WHERE conditions
- Sorting
- GROUP BY
- Aggregate functions
- JOINs
- Subqueries
- Common table expressions
For example, an analyst at an online retailer might use SQL to determine how many customers purchased a particular product during a specific month.
Data Visualization
Analysts often need to communicate their findings visually.
Common platforms include:
- Tableau
- Microsoft Power BI
- Excel
- Looker and other business intelligence platforms
The goal isn’t simply to make attractive charts. A good visualization should help the audience understand a particular finding.
Python or R
Python and R are programming languages commonly used for data analysis.
R has traditionally been popular for statistics and data analysis, while Python is widely used for data analysis, automation, machine learning, and other technical applications.
You don’t necessarily need advanced programming language skills for every entry-level data analyst position. However, learning one of these languages can expand the types of analytics work you can pursue.
For example, Google’s current Data Analytics Certificate curriculum covers spreadsheets, SQL, Tableau, and R, while Google recommends Python as a potential next step for learners who want to continue developing their skills.
What Skills Do You Need to Become a Data Analyst?
Data analysts need a combination of technical, analytical, and communication skills.
Technical Skills
Common technical skills include:
- SQL
- Excel or spreadsheets
- Data visualization
- Database concepts
- Statistics
- Data cleaning
- Reporting
- Python or R
- Tableau or Power BI
You don’t have to learn all of these at an advanced level before pursuing your first position. The appropriate skill level depends on the job.
For example, an entry-level reporting analyst might spend much of the day working in Excel and an organization’s reporting platform, while another data analyst may spend considerably more time writing SQL queries and using Python.
Analytical Skills
Technical tools are only part of the job.
Analysts also need to be able to:
- Identify meaningful patterns
- Determine whether data is reliable
- Ask useful questions
- Compare different explanations for a result
- Interpret statistics
- Recognize limitations in a dataset
- Draw reasonable conclusions
Consider a simple example.
A company’s website receives 20% more visitors this month, but sales remain unchanged. A weak analysis might simply report the increase in traffic.
A stronger analyst would investigate whether the additional visitors came from the company’s target audience, whether conversion rates changed, whether certain traffic sources performed better than others, and whether technical or pricing issues affected purchases.
Communication Skills
Data analysts frequently have to explain their findings to people who don’t work with data.
That means you need to be able to answer questions such as:
- What did you find?
- Why does it matter?
- How confident are you in the finding?
- What might explain the result?
- What should the organization consider doing next?
Being able to explain a complicated analysis in straightforward language can make your work considerably more useful.
Education Pathways by Degree Level for Aspiring Data Analysts
Many data analyst jobs require a bachelor’s or master’s degree. However, there are other education pathways to finding employment in this field. Shorter, less expensive options like certificate programs and coding bootcamps can provide a strong foundation for success in this field.
Bachelor’s Degree
There are many bachelor’s degrees that teach you the skills needed to succeed in this field. These include:
These bachelor’s degrees give you a strong grasp of evaluative methods and problem-solving. And each one comes with advantages. For example, statistics will teach you how information can be displayed and interpreted, which is a core component of data analyst jobs.
Meanwhile, a degree like economics may help you learn more about how to present your findings in a way that helps a business or organization make good decisions.
Master’s Degree
Master’s degrees are more specialized, and if you know the type of industry you’d like to work in, you can use this to guide your choice of master’s program. Good master’s degrees for access to top data analysis roles include:
- Data analytics: This program will not only hone your skills but give you a great understanding of what life is like in data analyst jobs day-to-day.
- Business analytics: if you’re looking to move into a corporate role, this program can teach you how to use your skills in the framework of a business or organization.
- Data science: While this is a distinct role and slightly different from a data analyst job, there is substantial overlap between these fields. It can help you learn more about the technical side of your field.
Certificate Programs in Data Analysis
A certificate in data analytics is an excellent way to learn the fundamentals of a data analyst job without investing heavily in a college degree. They typically take a few months to complete and are very affordable compared to other options.
If you’re looking to transition to this field from another profession, this is a great place to start. It will introduce core components of the role, such as modeling information, and may give you a grounding in an essential programming language like SQL or Python.
Data Analytics Bootcamps
Data analytics bootcamps are great educational pathways to get you started in your search for a data analyst job. Like in a certificate program, you’ll learn programming languages like SQL and Python in coding bootcamps. However, you’ll have increased 1-to-1 tuition over a greater length of time (often over a year).
These programs will also give you substantial insight into the day-to-day life of someone working in a data analyst job. As you learn from home, they’re often more accessible than college courses.
Self-Study Courses in Data Analysis
Self-study is an essential component of these roles, even if you have qualifications like a master’s degree. The field is always evolving, which means there’s always more to learn. However, self-study can also be a great way to teach yourself fundamentals, like learning your first programming language.
SQL is considered a top programming language for writing analysis programs. Python is another extremely popular choice, and you can begin to learn these languages through online courses at your own pace.
What Degree Is Best for a Data Analyst?
There is no single “best” degree for every aspiring data analyst.
Data Analytics Degree
A data analytics degree is one of the most direct options. Programs typically combine statistics, databases, data visualization, spreadsheets, and programming.
This can be a good choice if you already know that you want to work with data.
Business Analytics Degree
Business analytics programs combine analytical techniques with business concepts.
This may be particularly useful if you want to work with data in areas such as:
- Marketing
- Operations
- Finance
- Sales
- Management
- Business intelligence
Statistics Degree
Statistics provides a stronger mathematical and analytical foundation.
This can be useful for students who are interested in statistical analysis, research, experimentation, forecasting, or eventually pursuing more advanced analytical roles.
Computer Science Degree
A computer science degree can provide stronger programming and computing fundamentals.
It may be a good choice if you are interested in eventually moving toward data science, software development, machine learning, data engineering, or other technical careers.
Information Systems or Information Technology Degree
These programs can be useful for students interested in the intersection of technology, databases, business processes, and analytics.
Economics or Finance Degree
An Economics or finance degree can provide useful analytical skills while preparing students for industry-specific positions.
For example, someone with a finance degree and strong SQL and visualization skills could pursue financial analytics roles.
Data Analyst Career Pathways
Below we give an overview of the diverse roles in this career path.
Data Visualization Analyst
Data visualization is a key facet of most careers in this field, but some specialize in this area. As a specialist in visualization, you’ll be in charge of creating intelligible, useful ways to show information to stakeholders. You’ll have expert knowledge of visualization processes and writing software that interrogates the most relevant information and displays it in a way that your organization can draw actionable conclusions from.
Marketing Analyst
As a marketing analyst, you’ll be in charge of examining information on market trends and the field that your organization operates in. You’ll be tasked with evaluating the success of different marketing strategies and identifying key demographics that should be the target audience for your company. Your work will inform key decisions on your company’s future marketing strategy.
Business Analyst
This position is focused on using data to improve the performance of an organization. You’ll evaluate internal reports and gather information from them to identify areas for improvement in terms of productivity. You’ll present your findings to stakeholders, along with actionable suggestions
Information Security Data Analyst
This career path is ideal for data analysts who possess strong coding skills. You’ll write software to monitor your organization’s computer security systems, using your findings to assess vulnerabilities and deliver reports on how to increase your company’s security structure.
Market Research Analyst
Like a market research analyst, you’ll work closely with your business’s marketing department, but you’ll be at the forefront of collecting information. You’ll need a strong understanding of research methodologies to help you design research surveys and information. This includes gathering software to help your company better understand its audience.
Data Analyst Career Advancement
Data analytics can be a starting point for several different career paths.
One possible progression is:
Junior Data Analyst → Data Analyst → Senior Data Analyst → Analytics Manager → Director of Analytics
Other professionals use analytics experience to move into roles such as:
- Business Intelligence Analyst
- Business Intelligence Developer
- Product Analyst
- Marketing Analyst
- Financial Analyst
- Operations Analyst
- Data Scientist
- Data Engineer
- Analytics Manager
The skills required for advancement depend on the direction you choose.
Someone moving toward management may need stronger project management, communication, and leadership skills. Someone moving toward data science may need more advanced statistics, programming, and machine learning. Someone moving toward data engineering may need deeper knowledge of databases, data pipelines, cloud platforms, and software development.
How to Build a Data Analyst Portfolio
A portfolio can help demonstrate what you can actually do with data.
You don’t need dozens of projects. A few well-developed projects can be more useful than a large collection of unfinished exercises.
A good project should answer a specific question.
For example:
Project: Analyze retail sales data
Instead of simply creating a dashboard, establish a question such as:
Which product categories generate the most revenue, and how does performance vary by region?
You could then:
- Obtain a dataset.
- Clean the information.
- Explore the data.
- Analyze sales by product and region.
- Create visualizations.
- Identify meaningful patterns.
- Explain what the findings could mean for the business.
Other portfolio projects could include:
- Analyzing hospital readmission data
- Examining housing prices
- Studying customer purchasing behavior
- Evaluating marketing campaign performance
- Analyzing employee turnover
- Creating a financial performance dashboard
- Examining public transportation data
The project should demonstrate your ability to move from question → data → analysis → finding → recommendation.
How to Gain Experience as a New Data Analyst
Experience can be one of the biggest challenges for someone trying to land a first analytics position. Fortunately, professional experience isn’t the only way to demonstrate your abilities.
Consider:
Internships
Internships can provide exposure to real datasets, business questions, reporting systems, and workplace expectations.
Projects at Your Current Job
If you already work, look for opportunities to use data in your current position. For example,
- A customer service employee might analyze call volume, resolution times, or customer satisfaction.
- A marketing employee might analyze campaign performance.
- An administrative employee might create a dashboard tracking expenses or operational performance.
Those experiences can give you concrete examples to discuss during interviews.
Volunteer Work
Nonprofits and small organizations may have data that could be analyzed but lack the staff or expertise to do it.
A volunteer project could potentially give you experience while producing something useful for the organization.
Freelance Projects
Small businesses may need help organizing spreadsheets, creating reports, or analyzing customer or sales information.
Even a small project can help you practice working with someone else’s business question rather than an artificial classroom dataset.
How to Apply for Your First Data Analyst Job
Don’t limit your search to jobs with the exact title Data Analyst. Organizations use many different titles for analytical positions. Potential entry-level titles include:
- Junior Data Analyst
- Associate Data Analyst
- Data Analyst
- Reporting Analyst
- Business Analyst
- Operations Analyst
- Marketing Analyst
- Financial Analyst
- Business Intelligence Analyst
Read the job description carefully rather than relying only on the title.
One company might use “business analyst” for a position that focuses primarily on requirements gathering and project management. Another might use the same title for a position involving SQL, dashboards, and quantitative analysis.
Look for descriptions that mention skills such as SQL, Excel, reporting, dashboards, data visualization, statistics, databases, or business intelligence.
How Long Does It Take to Become a Data Analyst?
There isn’t one standard timeline. Your starting point makes a major difference.
If You’re Starting After High School
A bachelor’s degree typically takes about four years of full-time study. During that time, you can develop technical skills, complete projects, participate in internships, and prepare for entry-level positions.
If You Already Have a Bachelor’s Degree
You may be able to transition into analytics without earning another degree.
The timeline could depend on how much technical experience you already have and how quickly you can develop skills in SQL, spreadsheets, visualization, statistics, and other relevant areas.
If You’re Changing Careers Without a Degree
You might use a combination of:
- Structured coursework
- Professional certificates
- Self-directed learning
- Portfolio projects
- Volunteer or freelance experience
- Your existing industry knowledge
Some data analytics certificate programs are designed to be completed in less than six months with part-time study, but completing a program and becoming competitive for a particular job are not necessarily the same thing.
How Much Does a Data Analyst Make?
There isn’t a single reliable national salary figure for everyone with the job title “data analyst.” Employers use the title across a variety of occupations and industries, so compensation can vary substantially.
Salary can depend on:
- Experience
- Location
- Industry
- Employer
- Technical skills
- Education
- Job responsibilities
- Specialization
For context, the U.S. Bureau of Labor Statistics reports a 2025 median annual wage of $88,940 for operations research analysts, an occupation that overlaps with some data and analytics work. BLS projects employment for operations research analysts to grow 12% from 2025 to 2035.
What Is the Career Outlook for Data Analysts?
If you’re considering a career as a data analyst, the employment outlook is encouraging. Organizations across industries use data to understand customers, improve operations, manage costs, and make business decisions, creating opportunities for people with data analytical skills.
Companies use the title for a variety of jobs, including business, marketing, operations, financial, and technical analytics.
For example, operations research analysts are projected to see employment grow 12% from 2025 to 2035, compared with 3% for all occupations. The BLS estimates that about 7,500 openings for operations research analysts will be available each year, on average, during that period. These professionals use data, mathematics, and analytical techniques to help organizations solve problems and make decisions.
Another related field is market research analysis. Employment of market research analysts is projected to grow 7% from 2025 to 2035, with approximately 82,000 openings per year. These analysts examine data to understand customers, evaluate marketing strategies, and identify factors that affect demand for products and services.
| Related occupation | Projected growth, 2025–2035 | Average annual openings |
| Operations research analysts | 12% | 7,500 |
| Market research analysts | 7% | 82,000 |
| Data scientists | 35% | 24,800 |
| All occupations | 3% | — |
Why Are Data and Analytics Skills in Demand?
One reason is simple: organizations have more data than ever, but having data isn’t the same as knowing what to do with it.
Businesses collect information about sales, customers, website activity, inventory, employees, finances, and operations. Someone has to organize that information, identify meaningful patterns, and explain what those patterns mean.
Consider a retailer that notices sales have fallen in one region. A data analyst might examine sales by product, store, customer demographic, season, and marketing channel. The analysis could reveal that the decline is concentrated in a particular product category or that customers are purchasing less frequently.
The analyst’s job isn’t simply to produce a spreadsheet showing that sales declined. It’s to help the organization understand why the change occurred and what the data suggests doing next.
How Is AI Affecting the Data Analytics Job Outlook?
Artificial intelligence is changing how analysts work, but it doesn’t necessarily eliminate the need for people who understand data.
AI tools can assist with tasks such as writing code, cleaning data, identifying patterns, and creating initial analyses. At the same time, organizations still need people who can determine whether the data is appropriate, ask the right questions, recognize limitations, and communicate findings accurately.
For someone preparing for a data analyst career, this makes a combination of technical skills and analytical judgment particularly useful. Learning SQL, Excel, data visualization, statistics, and potentially Python or R can provide a foundation, while understanding a particular industry can help you apply those skills to real business problems.
Where Do Data Analysts Work?
One advantage of data analytics is that the underlying skills can be applied to many industries.
Healthcare
Healthcare analysts may work with:
- Patient outcomes
- Hospital operations
- Staffing
- Costs
- Quality measures
- Claims data
- Patient utilization
For example, an analyst could examine emergency department data to identify patterns in wait times and determine whether certain times of day consistently produce longer delays.
Finance
Financial analysts and other finance professionals use data to evaluate:
- Revenue
- Expenses
- Investments
- Risk
- Customer activity
- Financial performance
Someone with both financial knowledge and technical analytics skills may be well positioned for finance-focused analytical roles.
Marketing
Marketing analysts may examine:
- Website traffic
- Advertising performance
- Conversion rates
- Customer acquisition
- Email campaigns
- Customer behavior
An analyst could, for example, compare the performance of several advertising channels and determine which sources produce customers at the lowest cost.
Retail
Retail analysts can examine:
- Product sales
- Inventory
- Pricing
- Customer behavior
- Store performance
- Seasonal trends
Technology
Technology companies employ analysts to study:
- Product usage
- Customer behavior
- Subscription activity
- Revenue
- User engagement
- Operational performance
Government
Government agencies also use data to evaluate programs, monitor trends, allocate resources, and improve operations.
This broad industry application means you can build analytics expertise while also developing knowledge in a particular field.
Data Analyst vs. Data Scientist
Data analysts and data scientists both work with data, but their responsibilities can differ.
| Data Analyst | Data Scientist | |
| Primary focus | Analyze data and answer business questions | Build advanced models and generate predictions |
| Common tools | SQL, Excel, Tableau, Power BI | Python/R, SQL, machine learning tools |
| Statistics | Foundational to moderate | Often more advanced |
| Machine learning | May be limited or unnecessary | Frequently part of the role |
| Typical work | Reporting, analysis, dashboards, trends | Modeling, prediction, experimentation |
| Common entry path | Degree or skills-based training plus experience | Often bachelor’s degree or higher, depending on role |
The distinction isn’t absolute. Some experienced data analysts perform sophisticated statistical work, while some data scientists spend considerable time performing traditional analysis.
Google similarly describes data analysts as professionals who use tools such as SQL, R, visualization software, and statistical analysis, while data scientists generally work with more advanced techniques to make predictions.
Is Data Analytics a Good Career?
Data analytics can be a good career choice for people who enjoy solving problems, working with numbers and information, and explaining findings to others.
It also offers flexibility because analytical skills can be applied in many industries.
Potential advantages of a Career in Data Analytics:
- Multiple educational pathways
- Applications across many industries
- Opportunities to specialize
- Transferable technical skills
- Potential advancement into other data-related careers
- Opportunities to combine analytics with an existing area of expertise
There are also challenges to consider.
Entry-level positions can require candidates to demonstrate practical skills, not simply complete a course or earn a credential. Analytics also changes as employers adopt new software, automation, and AI-based tools.
As a result, continuing to learn is part of working in the field.
AI tools can already assist with tasks such as data cleaning, structuring datasets, generating formulas, and helping develop analysis workflows. Google’s current data analytics curriculum, for example, includes training on using AI to support analytics tasks.
That doesn’t eliminate the need for analytical judgment. An analyst still needs to determine whether the data is appropriate, whether the analysis makes sense, and whether a conclusion is supported by the evidence.
FAQs About Becoming a Data Analyst
What degree is best for a data analyst?
There isn’t one universally best degree. Data analytics, business analytics, statistics, mathematics, computer science, information systems, economics, and finance can all provide relevant preparation.
Do data analysts need to know SQL?
SQL is one of the most useful skills for data analysts because many analyst positions involve retrieving and working with information stored in databases. The level of SQL knowledge required varies by employer.
Do data analysts need to know Python?
Not necessarily. Some entry-level positions may not require Python, while others list it as a preferred or required skill. Learning Python can broaden your opportunities, particularly if you eventually want to pursue data science, automation, or more advanced analytics.
Can I become a data analyst with an associate degree?
It is possible, although some employers may prefer or require a bachelor’s degree. If you have an associate degree, building strong technical skills, completing portfolio projects, gaining practical experience, earning a data analytics certificate, and targeting employers that emphasize skills can improve your options.
How long does it take to become a data analyst?
The answer depends on your starting point. A student beginning a bachelor’s degree may spend about four years preparing for the field, while someone who already has a degree or relevant work experience may be able to transition more quickly through targeted skills training and practical projects.
Is data analytics a good career for someone changing careers?
It can be. Career changers may be able to combine newly developed analytics skills with knowledge from their previous industry. For example, a healthcare professional could pursue healthcare analytics, while someone with marketing experience could pursue marketing analytics.
What certifications are useful for data analysts?
Professional certifications can help structure your learning and demonstrate that you’ve completed relevant training. Their value depends on the employer and your overall qualifications. A certification is generally more useful when paired with practical projects and demonstrable technical skills.
Start Your Data Analytics Career
Becoming a data analyst doesn’t require following one predetermined path.
A college student might earn a bachelor’s degree in data analytics, statistics, computer science, or business analytics and complete internships before graduation. A working professional might build SQL and visualization skills and transition into an analytics position within their current industry. Someone changing careers might combine a professional certificate with portfolio projects and their existing work experience.
The most important step is to build skills that allow you to work with real data and explain what that data means.
If you’re considering a degree, compare programs based on more than the degree title. Look at the courses included in the curriculum, opportunities to work with real datasets, programming and database coursework, data visualization training, project-based assignments, and the type of career support the school provides.
Ready to explore your options? Compare online data analytics and related degree programs to find a program that fits your career goals, educational background, and schedule.
