How to Become a Data Analyst (with or Without a Degree)

Written by Coursera • Updated on

If you enjoy working with numbers and solving puzzles, a career as a data analyst could be a good fit.

A female data analyst sits at her desk in front of a laptop computer, looking at the camera

Data analysts gather, clean, and study data to help guide business decisions. If you’re considering a career in this in-demand field, here's one path to getting started:

  1. Get a foundational education.

  2. Build your technical skills.

  3. Work on projects with real data.

  4. Develop a portfolio of your work.

  5. Practice presenting your findings.

  6. Get an entry-level data analyst job.

  7. Consider certification or an advanced degree.

Let's take a closer look at each of those seven steps.

How do I become a data analyst? A step-by-step guide

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

Learn more: What Does a Data Analyst Do? A Career Guide

1. Get a foundational education.

If you’re new to the world of data analysis, you’ll 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’s degree. While many positions still do require a degree, that’s beginning to change. While you can develop foundational knowledge and enhance your resume with a degree in math, computer science, or another related field, you can also learn what you need through alternative programs, like professional certificate programs, bootcamps, or self-study courses.

2. Build your technical skills.

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

Take a look at some job listings for roles you’d 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—you may be asked to present your findings to those without as much technical knowledge—problem solving ability, and domain knowledge in the industry you’d like to work.

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 National Centers for Environmental Information, delve deeper into the news with data from BuzzFeed, or come up with solutions to looming challenges on Earth and beyond with NASA open data. These are just a few examples of the data out there. Pick a topic you’re interested in and find some data to practice on.

Tip: For more inspiration, check out Coursera’s library of data analysis Guided Projects—a series of guided, hands-on experiences you can complete in under two hours.

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

If you’re not sure what to include in your portfolio (or need some inspiration for project ideas), spend some time browsing through other people’s portfolios to see what they’ve chosen to include.

Tip: Sign up for a GitHub account and start posting your projects and code to the site. It’s an excellent spot to network with a community of data analysts, show off your work, and possibly catch the eye of recruiters.

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5. Practice presenting your findings.

It can be easy to focus only on the technical aspects of data analysis, but don’t neglect your communication skills. A significant element of working as a data analyst is presenting your findings to decision makers and other stakeholders in the company. When you’re able to tell a story with the data, you can help your organization make data-driven decisions. 

What is data-driven decision-making (DDDM)?

Data-driven decision-making, sometimes abbreviated to DDDM), can be defined as the process of making strategic business decisions based on facts, data, and metrics instead of intuition, emotion, or observation.

This might sound obvious, but in practice, not all organizations are as data-driven as they could be. According to global management consulting firm McKinsey Global Institute, data-driven companies are better at acquiring new customers, maintaining customer loyalty, and achieving above-average profitability [1].

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As you complete projects for your portfolio, practice presenting your findings. Think about what message you want to convey and what visuals you’ll use to support your message. Practice speaking slowly and making eye contact. Practice in front of the mirror or your classmates. Try recording yourself as you present so you can watch it back and look for areas to improve.

6. Get an entry-level data analyst job.

After gaining some experience working with data and presenting your findings, it’s time to polish your resume and begin applying for entry-level data analysts jobs. Don’t be afraid to apply for positions you don’t feel 100-percent qualified for. 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’re still in school, ask your university’s career services office about any internship opportunities. With an internship, you can start gaining real world experience for your resume and apply what you’re learning on the job.

7. Consider certification or an advanced degree.

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

Tip: Consider pursuing your data science degree online from an accredited university so you can continue working (and earning a paycheck) as you learn.

The University of Michigan School of Information’s online Master of Applied Data Science (MADS) degree is designed for aspiring data scientists to learn and apply skills through hands-on projects. You’ll learn how to use data to improve outcomes and achieve ambitious goals.

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

Learn more: Data Analyst vs. Data Scientist: What’s the Difference?

In this video, practicing data professionals offer their best advice for aspiring data analysts.

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Data professionals give advice to aspiring data analysts.

How to become a data analyst without a degree

A degree isn’t always necessary to get hired as a data analyst. Data analysts are in demand, and employers want to know that you have the skills to do the job. If you don’t have a degree, focus on making your portfolio shine with your best work. 

Get started with Coursera

If you’re looking to build job-ready data analyst skills without spending the time or money on a degree, consider the Google Data Analytics Professional Certificate through Coursera.

Learn how to clean and organize data with SQL and R, visualize with Tableau, and complete a case study for your portfolio—no prior experience or degree required. Upon completion, you can start applying for entry-level jobs directly with Google and more than 130 other US employers.

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How to become a data analyst without experience

Often employers will want you to have experience working with data before taking a role as a data analyst. Luckily, you don’t have to wait to get hired to start gaining experience. Data is all around us. 

If you’re switching to data analysis from another field, start to develop your experience by working with data. Many degree programs, certificate courses, and online classes include hands-on projects with real data sets. You can also find free data sets on the internet (or scrape your own) to gain experience collecting, cleaning, analyzing, and visualizing real data.

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professional certificate

Google Data Analytics

This is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required.

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Average time: 6 month(s)

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Spreadsheet, Data Cleansing, Data Analysis, Data Visualization (DataViz), SQL, Questioning, Decision-Making, Problem Solving, Metadata, Data Collection, Data Ethics, Sample Size Determination, Data Integrity, Data Calculations, Data Aggregation, Tableau Software, Presentation, R Programming, R Markdown, Rstudio, Job portfolio, case study

Frequently asked questions (FAQ)

Article sources

1

McKinsey & Company. "Five facts: How customer analytics boosts corporate performance, https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance." Access March 15, 2022.

Written by Coursera • Updated on

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