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

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. Practise presenting your findings.

6. Get an entry-level data analyst job.

7. Gain certifications.

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

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

You can find data analytics jobs in various industries, and there’s more than one path toward securing your first job in this high-demand field. Whether you’re just starting 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’re new to data analysis, you’ll want to start by developing foundational knowledge. A broad overview of data analytics can help you decide whether this career is a good fit whilst equipping you with job-ready skills. 

In most cases, the path to becoming a data analyst is through an undergraduate or postgraduate degree in a related discipline, such as computer science, information management, maths, statistics, economics, finance, or business information systems. Many companies seek candidates with this background because it demonstrates solid foundational knowledge in relevant quantitative skills. 

A postgraduate degree can help gain hands-on experience relevant to real working environments. Depending on your desired role, you can also focus your postgraduate education on the area you would like to build expertise. For example, a Master in Data Science and a Master in Business Analytics are valuable degrees for data analysts, but the curriculum's focus differs. 

However, suppose you have a degree in an unrelated field or do not have a degree. In that case, building the needed skills through alternative methods such as certificate programmes or university courses is possible. If you choose an alternative path, it is essential to gain relevant Professional Certificates to demonstrate you have built the skills needed to perform the job well.

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 programme, professional certificate, or on your own, these are essential skills you’ll likely need to get hired.

• Statistics

• R, Python, and JavaScript programming

• SQL (Structured Query Language)

• Data visualisation (Tableau, QlikView, Power BI)

• Data cleaning and preparation

• Microsoft Excel

• Dashboarding

• Relational Database

• Machine Learning

Look at job listings for roles you’d like to apply for and focus your learning on the specific programming languages or visualisation tools listed as requirements.

In addition to these technical skills, hiring managers also look for workplace skills, like communication skills—you may be asked to present your findings to those without as much technical knowledge—problem-solving ability, creativity, and domain knowledge in the industry you’d like to work in.

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 programmes or courses with hands-on projects using real data sets. You can also find a variety of free public data sets you can use to design your projects. 

Get started with open source data and resources from GitHub India, 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 practise.

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.

4. Develop a portfolio of your work.

As you play around with data sets online or complete hands-on assignments in your classes, 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 normalise raw data

• Visualise your findings through graphs, charts, maps, and other visualisations

• Draw actionable insights from data

Consider including one if you’ve worked on any group projects throughout your learning. This shows that you’re able to work as part of a team.

If you’re unsure what to include in your portfolio (or need some inspiration for project ideas), browse other people’s portfolios to see what they’ve chosen to include.

Tip: Sign up for a GitHub account and post 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.

5. Practise 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 can tell a story with the data, you can help your organisation make data-driven decisions. 

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

Data-driven decision-making (sometimes abbreviated to DDDM) is 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 organisations are as data-driven as possible. According to the 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].

As you complete projects for your portfolio, practise presenting your findings. Think about what message you want to convey and what visuals you’ll use to support your message. Practise speaking slowly and making eye contact. Practise in front of the mirror or with 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 experience working with data and presenting your findings, it’s time to polish your resume and apply for entry-level data analyst jobs. Don’t be afraid to apply for positions you don’t feel 100 per cent qualified for. Your skills, portfolio, and enthusiasm for a role often matter more than if you check every bullet item in the qualifications list.

Your pay will likely increase as you gain more experience through education or job experience. For example, data analysts in India make an average annual salary of ₹4L to ₹9L, whilst senior data analysts make between ₹7L to ₹18L per year, according to Glassdoor as of June 2023 [2].

7. Gain certifications.

Earning a certification can help you stand out to employers. Certification will demonstrate that you have the necessary skills to work on the job. 

Some options for highly recognised certifications include:

• Associate Certified Analytics Professional

• Open Certified Data Scientist

• Google Certified Professional Data Engineer

• Microsoft Certified Azure Data Scientist Associate

• Microsoft Certified Data Analyst Associate

• SAS Certified Advanced Analytics Professional Using SAS 9

• SAS Certified Big Data Professional Using SAS 9

• Cloudera Certified Associate Data Analyst

Depending on your intended job responsibilities, specific certifications may be more suited to the skills you want to demonstrate. Looking at open job postings can help you see what skills employers most value. For example, if a job posting you are interested in mentions working with SAS, gaining the SAS Certified Advanced Analytics Professional certificate may be helpful.

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

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.

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 you have the skills to do the job. If you don’t have a degree, focus on taking relevant coursework to build your skill set and make your portfolio shine with your best work. Earning a Professional Certificate from an accredited institution can help show employers that you have the training required for the position, especially if you do not have a degree. 

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 on Coursera.

Learn how to clean and organise data with SQL and R, visualise with Tableau, and complete a case study for your portfolio—no prior experience or degree is required. 

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, develop your experience by working with data and building your portfolio. Many degree programmes, certificate courses, and online classes include hands-on projects with real data sets that can give you real project experience. You can also find free data sets on the internet (or scrape your own) to gain experience collecting, cleaning, analysing, and visualising real data.

Take your first steps

As data applications in many industries continue to expand, the need for professionals who can work with this data is rising. Whether you have a background in data or are entering a new career, showcasing your data analytics skills through a Professional Certification like the Google Data Analytics Professional Certificate on Coursera can help you build your resume and land your first entry-level position in this field.

Frequently asked questions (FAQ)