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Data analysts collect, clean, and interpret data sets to solve problems or answer questions, such as: What kind of customers should a business target in its next ad campaign? What age group is most vulnerable to a particular disease? What patterns in behavior are connected to financial fraud?

Data analysts work in many industries, including business, finance, criminal justice, science, medicine, and government. In this article, we'll discuss more about what data analysts do, what skills you'll need to pursue this career, and how you can start on a path to becoming one. Afterward, if you want to start working toward a data career by building job-relevant skills and earning a respected credential, consider enrolling in the Google Data Analytics Professional Certificate.

What is data analysis?

Data analysis is the process of gleaning insights from data to inform better business decisions. The process of analyzing data typically moves through five iterative phases:

• Identify the data you want to analyze

• Collect the data

• Clean the data in preparation for analysis

• Analyze the data

• Interpret the results of the analysis

Data analysis can take different forms, depending on the question you鈥檙e trying to answer. Descriptive analysis tells us what happened; diagnostic analysis tells us why it happened; predictive analytics forms projections about the future; and, finally, prescriptive analysis creates actionable advice on what actions to take.

Data analyst tasks and responsibilities

A data analyst gathers and interprets data in order to solve a specific problem. The role involves plenty of time spent with data but also entails communicating findings to stakeholders.聽

Here鈥檚 what many data analysts do on a day-to-day basis:

• Gather data: Analysts often collect data themselves. This could include conducting surveys, tracking visitor characteristics on a company website, or buying datasets from data collection specialists.

• Clean data: Raw data might contain duplicates, errors, or outliers. Cleaning the data means maintaining the quality of data in a spreadsheet or through a programming language so that your interpretations won鈥檛 be wrong or skewed.聽

• Model data: This entails creating and designing the structures of a database. You might choose what types of data to store and collect, establish how data categories are related to each other, and work through how the data actually appears.

• Interpret data: Interpreting data involves finding patterns or trends in data that could answer the question at hand.

• Present: Communicating the results of your findings will be a key part of your job. You do this by putting together visualizations like charts and graphs, writing reports, and presenting information to interested parties.

What tools do data analysts use?

During the process of data analysis, analysts often use a wide variety of tools to make their work more accurate and efficient. Some of the most common tools in the data analytics industry include:

• Microsoft Excel

• Google Sheets

• SQL

• Tableau

• R or Python

• SAS

• Microsoft Power BI

• Jupyter Notebooks

Learn how to perform data analysis, including data preparation, statistical analysis, and predictive modeling using R, R Studio, and Jupyter in the IBM Data Analytics with Excel and R Professional Certificate.

Data analyst salary and job outlook

The average base salary for a data analyst in the US is $74,052 as of June 13, 2025, according to Glassdoor. This can vary depending on your seniority, where in the US you鈥檙e located, and other factors.

Data analysts are in high demand. The World Economic Forum listed them as one of the fastest-growing jobs between 2025 and 2030 []. The US Bureau of Labor Statistics (BLS) also reports that related occupations have extremely high growth rates. From 2023 to 2033, operations research analyst positions are expected to grow by 23 percent, data scientists by 36 percent, market research analysts by 8 percent, and mathematicians and statisticians by 11 percent. That鈥檚 a lot higher than the total employment growth rate of just 4 percent over the decade.

Data analyst vs. data scientist: What鈥檚 the difference?

Data analysts and data scientists both work with data, but what they do with it differs. Data analysts typically work with existing data to solve defined business problems. Data scientists build new algorithms and models to make predictions about the future. Learn more about the difference between data scientists and data analysts.

Types of data analysts

As advancing technology has rapidly expanded the types and amount of information we can collect, knowing how to gather, sort, and analyze data has become a crucial part of almost any industry. You鈥檒l find data analysts in the criminal justice, fashion, food, technology, business, environment, and public sectors鈥攁mong many others.

People who perform data analysis might have other titles, such as:

• Medical and health care analyst

• Market research analyst

• Business analyst

• Business intelligence analyst

• Operations research analyst

• Intelligence analyst

Interested in business intelligence?

Gain the business and data skills you need for an entry-level business analyst role with IBM's Business Analyst Professional Certificate program. In as little as three months, you'll gain critical skills like business systems analysis, project risk management, and interactive data visualization.

How to become a data analyst

You can find data analytics jobs in all sorts of industries, and there鈥檚 more than one path toward securing your first job in this high-demand field. Whether you鈥檙e just getting started in the professional world or pivoting to a new career, here are some steps toward becoming a data analyst.

1. Get a foundational education.

If you鈥檙e new to the world of data analysis, you鈥檒l want to start by developing some foundational knowledge in the field. Getting a broad overview of data analytics can help you decide whether this career is a good fit while equipping you with job-ready skills.

It used to be that most entry-level data analyst positions required a bachelor鈥檚 degree. While many positions still do require a degree, that鈥檚 beginning to change. You can develop foundational knowledge and enhance your resume with a degree in math, computer science, or another related field鈥�or you can also learn what you need through alternative programs, like professional certificates, bootcamps, or self-study courses.

Resources to start your data analyst career

We've curated a collection of resources to help you decide whether becoming a data analyst is right for you鈥攊ncluding figuring out what skills you'll need to learn and courses you can take to pursue this career.

2. Build your technical skills.

Getting a job in data analysis typically requires having a set of specific technical skills. Whether you鈥檙e learning through a degree program, professional certificate, or on your own, these are some essential skills you鈥檒l likely need to get hired.

• Statistics

• R or Python programming

• SQL (Structured Query Language)

• Data visualization

• Data cleaning and preparation

Take a look at some job listings for roles you鈥檇 like to apply for, and focus your learning on the specific programming languages or visualization tools listed as requirements.

In addition to these hard skills, hiring managers also look for workplace skills, like solid communication skills鈥攜ou may be asked to present your findings to those without as much technical knowledge鈥攑roblem-solving ability, and domain knowledge in the industry you鈥檇 like to work.

Learn more: 7 In-Demand Data Analyst Skills to Get Hired

3. Work on projects with real data.

The best way to learn how to find value in data is to work with it in real-world settings. Look for degree programs or courses that include hands-on projects using real data sets. You can also find a variety of free public data sets you can use to design your own projects.

Dig into climate data from the , delve deeper into the news with data from , or come up with solutions to looming challenges on Earth and beyond with open data. These are just a few examples of the data out there. Pick a topic you鈥檙e interested in and find some data to practice on.

Read more: 5 Data Analytics Projects for Beginners

4. Develop a portfolio of your work.

As you play around with data sets on the internet or complete hands-on assignments in your classes, be sure to save your best work for your portfolio. A portfolio demonstrates your skills to hiring managers. A strong portfolio can go a long way toward getting the job.

As you start to curate work for your portfolio, choose projects that demonstrate your ability to:

• Scrape data from different sources

• Clean and normalize raw data

• Visualize your findings through graphs, charts, maps, and other visualizations

• Draw actionable insights from data

If you鈥檝e worked on any group projects through the course of your learning, consider including one of those as well. This shows that you鈥檙e able to work as part of a team.

If you鈥檙e not sure what to include in your portfolio (or need some inspiration for project ideas), spend some time browsing through other people鈥檚 portfolios to see what they鈥檝e chosen to include.

Read more: How to Build a Data Analyst Portfolio: Tips for Success

5. Get an entry-level data analyst job.

After gaining some experience working with data and presenting your findings, it鈥檚 time to polish your resume and begin applying for entry-level data analyst jobs. Don鈥檛 be afraid to apply for positions that feel like a stretch. Your skills, portfolio, and enthusiasm for a role can often matter more than if you check every bullet item in the qualifications list.

If you鈥檙e still in school, ask your university鈥檚 career services office about any internship opportunities. With an internship, you can start gaining real-world experience for your resume and apply what you鈥檙e learning on the job.

There鈥檚 more than one path toward a career as a data analyst. Whether you've just graduated from school or are looking to switch careers, the first step is often assessing what transferable skills you have and building the new skills you鈥檒l need in this new role.

6. Consider certification or an advanced degree.

As you move through your career as a data analyst, consider how you鈥檇 like to advance and what other qualifications can help you get there. Certifications, like the Certified Analytics Professional or Cloudera Certified Associate Data Analyst, may help qualify you for more advanced positions at higher pay grades.

If you鈥檙e considering advancing into a role as a data scientist, you may need to earn a master鈥檚 degree in data science or a related field. Advanced degrees are not always required, but having one can open up more opportunities.

Data analyst career advancement

Being a data analyst can also open doors to other careers. Many who start as data analysts go on to work as data scientists. Like analysts, data scientists use statistics, math, and computer science to analyze data. A scientist, however, might use advanced techniques to build models and other tools to provide insights into future trends. Further your data analytics career with the Google Advanced Data Analytics Professional Certificate.

Build your data analyst skills today

Data analysts use their knowledge to help others make more informed decisions. Build the skills you need for this in-demand and fast-growing career through one of these educational programs on 糖心vlog官网观看:

To prepare for an entry-level data analyst role, enroll in the Google Data Analytics Professional Certificate. In this beginner-level program, you'll learn how to clean, organize, analyze, visualize, and present data from data professionals at Google.

To launch your career as a Power BI analyst, take the Microsoft Power BI Data Analyst Professional Certificate. In as little as five months, you'll learn to use Power BI to connect to data sources, explore the visualization and reporting capabilities of Power BI, and prepare Excel data for analysis in Power BI.

For data analysis and visualization skills, explore the Microsoft Data Visualization Professional Certificate. Learn how to create data visualizations, build data models, and prepare and transform datasets.

Frequently asked questions (FAQ)