How to Become a Data Analyst Without a Degree

Follow these step-by-step instructions to become a data analyst without a college degree or any prior expertise.

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The bottom line
  • According to our research, there are currently almost 20,000 open no-degree data analyst positions worldwide. This amounts to 57.13% of the total number of positions.
  • A formal degree is not strictly required to become a data analyst, although knowledge of related fields such as computer science, mathematics, and statistics is crucial.
  • The most important thing for data analyst job seekers is to have a strong set of relevant skills and a portfolio that demonstrates their abilities.
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In recent years, data analysts have become some of the most in-demand professionals in the workforce. Corporations, small businesses, healthcare organizations, and more all rely on data to make informed business decisions, and the need for professionals who can make sense of it all has never been greater.

Unsurprisingly, the high demand also means very competitive wages. With a median hourly wage of $38 and a yearly salary of $75,966, data analysts are some of the highest-paid professionals out there. Plus, consider that depending on the company and industry, the wages can be much higher. For example, data analysts working in Meta earn an average of $129,132 per year: nearly double that of the median.

But, can you become a data analyst without a degree in computer science, mathematics, or statistics? Or, without any degree or experience at all?

In this article, I’ll answer just that, by looking at what exactly a data analyst does, what skills are needed on the job, and most importantly, how to build a career as a successful data analyst without any formal education.

In this article, you’ll learn:

What does a data analyst do?

A data analyst is a professional who helps companies make data-driven decisions through processes such as data mining, organization, visualization, and statistical analysis. Data analysts typically have a strong background in mathematics and statistics, and they use this knowledge to find patterns and trends in data sets.

These trends and patterns can then be used for everything from marketing campaigns, product development, and scientific research, all the way to detecting fraudulent behavior and improving business operations.

First and foremost, any aspiring data analyst has to be mathematically inclined. A solid understanding of mathematics and statistics is key to being able to analyze data effectively. So, if you find working with numbers, graphs, and tables to be a challenge, a career as a data analyst is probably not for you.

Ultimately, data analysts help organizations to make better decisions by extracting meaning from big data. A data analyst’s job is multifaceted and successful data analysts have to be able to wear many different hats. Depending on the role, data professionals may be expected to understand advanced modeling, build data visualizations, write SQL queries, or even work with machine learning algorithms.

Do you need a degree to become a data analyst?

No, you do not need a degree to become a data analyst. More than half of open data analyst jobs in the world do not require a degree from applicants, and there are tens of thousands of available jobs for data analysts without a degree.

As is usually the case, however, there are some noteworthy differences between the countries. For example, in the United States, more than half of open data analyst positions do require a degree, while in the United Kingdom only a small minority of jobs have this requirement.

Here’s a chart that highlights the differences between countries:

Job outlook for data analysts without a degree. Data: LinkedIn Job Search (2022).
Job outlook for data analysts without a degree. Data: LinkedIn Job Search (2022).

And, here are the exact figures:

  • Worldwide, there are 19,936 no-degree data analyst jobs available out of a total of 34,896. Thus, 57.13% of data analyst jobs worldwide do not require a degree.
  • In the United States, there are 8,982 no-degree data analyst jobs available out of a total of 19,179. Thus, 46.83% of data analyst jobs in the United States do not require a degree.
  • In the United Kingdom, there are 2,231 no-degree data analyst jobs available out of a total of 2,972. Thus, 75.07% of data analyst jobs in the United Kingdom do not require a degree.
  • In the European Union, there are 4,317 no-degree data analyst jobs available out of a total of 6,245. Thus, 69.13% of data analyst jobs in the European Union do not require a degree.
  • In Australia, there are 405 no-degree data analyst jobs available out of a total of 556. Thus, 72.84% of data analyst jobs in Australia do not require a degree.
  • In Canada, there are 577 no-degree data analyst jobs available out of a total of 993. Thus, 58.11% of data analyst jobs in Canada do not require a degree.
  • In India, there are 1,268 no-degree data analyst jobs available out of a total of 1,997. Thus, 63.50% of data analyst jobs in India do not require a degree.

In a nutshell, analyzing the job market shows that, in general, a degree is not required to become a data analyst. There are many countries and regions where the majority of jobs do not require a degree, and this is especially true for the United Kingdom, European Union, Australia, and India.

Above all else, what raises your chances of finding a high-paying data analyst job is your skillset. That is why let’s now take a look at the skills that data analysts need.

What skills does a data analyst need?

A good data analyst is required to have a combination of some specific soft and hard skills. To give you an idea of what you will need to learn, let’s review some of the skills that data analysts should have in their arsenal.

These are the skills that data analysts should possess:

  • Data visualization. The idea behind data visualization is quite simple. It is about making large chunks of data easily readable to anyone by visualizing them in the form of graphs, charts, etc. There is various software used for this, but Tableau is still the top dog in the industry. So, if you are planning on becoming a data analyst, you will eventually need to start learning Tableau. Luckily, the software is relatively intuitive and easy to learn, even for complete beginners.
  • SQL. A data analyst will have to deal with large amounts of data stored in databases quite frequently. In order to extract this data, one must know how to use Structured Query Language or SQL. SQL is a powerful programming language used for communicating large amounts of data in databases. Luckily, this language is not that difficult to learn for beginners. There are various online courses and tutorials that can help you get started with SQL in no time.
  • Python and/or R. While SQL will be your tool for communicating with databases, Python and R are commonly used for other data-related tasks such as manipulating, cleaning, and analyzing data. These two programming languages are the most popular ones used by data scientists and analysts. Python is a versatile language that can be used for everything from web development to scientific computing, while R was specifically designed for statistical computing. Both of the aforementioned languages are commonly used in data analysis, so it is essential to learn at least one of them if you want to have a career in this field.
  • Excel. Excel is another essential tool for data analysts. It is a spreadsheet application that allows analysts to easily manipulate and analyze data. Especially when it comes to smaller pools of data. Excel is a staple software in businesses all over the world for a reason, and understanding how to effectively use Excel is essential for any data analyst.
  • Data wrangling. This is the process of cleaning and organizing data so that it can be easily analyzed. Data wrangling is a very important skill for data analysts as it can be nearly impossible to work with messy data. Data wrangling can be done using various software, but since we already mentioned Excel and Tableau, then you should know that both of these software can be used for data wrangling.
  • Data warehousing. Data warehousing is the process of storing large amounts of data in an easily accessible format. This is important for data analysts as they will often have to deal with large amounts of data that need to be stored. Some of the most commonly used data management platforms are Oracle, Microsoft SQL Server, and MySQL.
  • Statistics. Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It is an essential skill for data analysts as they will often have to work with large amounts of data that need to be analyzed.
  • Mathematics. A good understanding of mathematics is also essential for data analysts. This is because a lot of the work of data analysts revolves around numbers and calculations.
  • Communication skills. A big part of the job of a data analyst is communicating complex information about data to people who do not have a background in data. This means that in addition to a plethora of technical skills, data analysts also need to have strong communication skills to be effective in their jobs.
  • Analytical skills. Data analysts need to have strong analytical skills to be effective in their profession. This means that they must be able to identify patterns and trends in data, and then use this information to make accurate predictions.
  • Problem-solving skills. Data analysts often have to deal with complex problems that need to be solved. An ability to think on your feet and come up with creative solutions to problems is essential for data analysts.
  • Presentation skills. As a data analyst, your job often involves taking unstructured data, cleaning it up, and then presenting it to relevant stakeholders. These stakeholders often have different levels of understanding when it comes to data, and data analysts need to be able to tailor their presentations to the audience they are presenting.

Depending on your data analyst career path, you may also need to learn a number of other skills, but the aforementioned skills are often the most essential ones.

So, now that we’ve gone over some of the necessary skills for data analysts, let’s take a look at a few potential places of employment for data analysts who do not have a degree.

Places of employment for data analysts without a degree

There are a number of places where data analysts who don’t have a degree can find employment. To give you some concrete examples, have a look at some of the organizations that rely on the services of data analysts:

  • Technology companies. Naturally, tech companies are one of the biggest employers of data analysts. Companies like Google, Meta, and Amazon all have a seemingly insatiable appetite for data analysts, and they are constantly hiring to get an edge over their competitors. Data analyst salaries in tech companies are superb, and I recommend keeping an eye out for job openings in these companies. Best of all, tech companies often don’t care whether or not you have a degree or any type of formal education. All they care about is the skills you possess.
  • Consulting firms. Data analysts who work in consulting firms help their clients make informed decisions about their businesses. This can involve anything from providing data-driven insights to helping clients develop new strategies based on data.
  • Investment firms. Data analysts who work in investment firms use their skills to analyze financial data and find ways to invest the firm’s money in a way that will make the most profit.
  • Marketing firms. Data analysts who work in marketing firms use their skills to help the firm sell the products and services of the client of the marketing company. This is done by analyzing large pools of customer data and using it to develop marketing strategies that are targeted at the right audience.
  • Manufacturing companies. Data analysts who work in manufacturing companies use their skills to help the company improve its production process. This can involve anything from analyzing data about production efficiency to using data for improving the safety of the manufacturing process.
  • Healthcare. Data analysts who work in healthcare generally use their skills to help the organization improve its patient care. This can be done in a number of ways – from analyzing patterns in patient data to helping develop new treatments and procedures based on data.
  • Governmental organizations. Nowadays, nearly every major governmental organization in the world employs data analysts and scientists, and for good reason. Data analysts who work in governmental organizations use their skills to help the government make better decisions and improve its services. This can involve anything from analyzing data about crime rates to helping develop new social welfare programs.
  • Banks. Analysts who work in banks utilize their talents to assist the bank in making a profit. They accomplish this in a variety of ways, including analyzing the risks involved with customers and assisting the bank in developing new goods and services through data-driven decision-making. However, for degreeless applicants, I don’t often recommend banks as the first choice as they tend to be more risk-averse and prefer to see applicants with a degree from a well-known university.

These are just some of the places where data analysts can find employment. Data analysts are valued in a variety of very different industries because nearly all successful modern businesses rely on data to make decisions.

Simply put – data is everything. If you do have a proven portfolio and the skills that businesses are looking for, then you can probably find a job as a data analyst – even if you do not have a degree.

Now, let’s take a look at the steps you need to take in order to become a data analyst without a degree.

Steps to becoming a data analyst without a degree

1. Learn the basics of statistics and SQL.

Statistics are the bread and butter of every data analyst, so it is important that you have a strong understanding of the basics. Not only should you be able to understand and use basic statistical concepts such as mean, median, mode, and standard deviation, but you should also be familiar with more advanced concepts such as regression analysis and time series analysis.

As for SQL, it is the most commonly used programming language for working with databases. Learning SQL will allow you to extract data from databases for analysis and it is a skill that is highly valued by employers. While many people overestimate the importance of programming languages, the reality is that being able to write basic SQL queries is often all you need for most entry-level data analyst jobs. Start learning it early, and it will serve you well throughout your career.

There are a number of ways to familiarize yourself with these topics, and personally, I’m a big advocate for self-directed online learning. There are plenty of resources online that you can use to learn about them and if you are not sure where to start, my suggestion would be to use a reliable online course.

2. Enroll in an online data analytics course or bootcamp.

If you go down the route of taking an online course, you may be able to skip the first step altogether. After all, any respectable online data analytics course will always cover SQL and statistics.

While not free, an online course can often fast-track your learning process and help you develop a comprehensive understanding of data analysis. If you are serious about becoming a data analyst without a degree, I would highly recommend that you consider enrolling in an online course. While there are many successful data analysts who are entirely self-taught without any external help, there are also many untold stories of people who give up on their data career after watching three YouTube videos and becoming overwhelmed by the sheer volume of information.

As for what online course to choose, I recommend choosing a course that offers:

  • Practical hands-on projects. Working with hands-on projects will give you valuable real-life experience and any projects you complete during the course can be added to your portfolio of work.
  • Certificates of completion. Without a degree, you need to look for any opportunities to boost your CV. From my experience, a course certificate of completion from an accredited organization is one of the most effective ways of doing so.
  • One-on-one mentorship. Having a mentor ready to help you when you get in trouble is a fantastic way to stay on track and avoid getting overwhelmed. However, courses and bootcamps that offer this feature often tend to be pricier than the alternatives.

Now, taking all these factors into consideration, I recommend taking a look at the following online data analytics courses and bootcamps:

3. Practice with your own data analysis projects.

One of the best ways to familiarize yourself with the world of data analysis is by working on projects of your own. This will give you a chance to put into practice the concepts that you have learned and also to develop your own data analysis workflow.

Here are some simple data analysis projects to consider:

  • Collect and analyze data about your daily commute (e.g., distance, time, mode of transportation, weather conditions, etc.).
  • Analyze data from your favorite sporting team (e.g., player performance, game results, league standings, etc.).
  • Collect data about the books you read (e.g., number of pages, time to read, genre, author, etc.) and analyze it to find trends.
  • Analyze data from your social media usage (e.g., time spent per day, most used platforms, content posted, etc.).
  • Collect data about your spending habits (e.g., the amount spent per day, category of purchase, etc.) and analyze it to find trends.

These are just a few ideas to get you started. The important thing here is that you find a data set that interests you and that you can use to practice your data analysis skills. For more ideas, I recommend checking out this blog post on Career Foundry that features a few interesting project ideas for data analysis students.

Remember that this is purely for practice – you do not need to work on these projects with the end goal of boosting your resume. In fact, you will master the key concepts of data analysis much faster if you also manage to have fun with your projects. Plus, it’s the best way to become comfortable with complex topics such as querying languages, analytics techniques, and analytics software with the least amount of resistance.

4. Create a public data analysis portfolio.

Once you have a few data analysis projects under your belt, it is time to start creating your portfolio. This portfolio will be key in helping you find employment as a data analyst – especially if you do not have a bachelor’s degree.

Your portfolio should include:

  • A description of the data analysis projects that you have worked on, including the data sets that you used and the methods that you employed.
  • The results of your data analysis. Make sure to use your data visualization skills to present your results in an easily understandable and engaging way.
  • The code you used for each of your data analysis projects.
  • If possible, try to highlight all of the different data analysis tools and platforms that you are familiar with.

There are many ways to create a portfolio, but we recommend using a simple website builder such as WordPress or Squarespace. These platforms will allow you to create a professional-looking website with minimal effort.

Once you do have a portfolio, you will also want to share it – and your data analysis skills – with the world. There are a number of ways to do this, the following are some of the most simple ones:

  • Creating a blog and writing about your data analysis projects.
  • Creating a YouTube channel and sharing video explanations of your data analysis projects.
  • Sharing your data analysis projects on social media platforms such as Twitter, LinkedIn, and Facebook.
  • Submitting your data analysis projects to online communities such as Reddit and Hacker News.

Doing all of the above will help you gain exposure for your work, which will turn the chances of finding a job in this field in your favor.

5. Optimize your LinkedIn profile.

Your LinkedIn profile is one of the most important tools that you have at your disposal when it comes to showcasing your expertise level in a given profession. As a data analyst, you will want to make sure that your profile is optimized to show off your skills and experience in the field. Here are a few tips on how to do just that:

  • In the summary section, highlight your data analysis skills and experience.
  • In the work experience section, include data-focused projects that you have worked on.
  • Use the portfolio section for showcasing your data visualization skills.
  • In the skills section, list all of the data analysis tools and platforms that you are familiar with.
  • If you have any, do not forget to include links to your blog, YouTube channel, or any other online platforms where you share your work.

6. Apply to entry-level data analyst positions.

Once you have created your portfolio, optimized your LinkedIn profile, and also have some relevant experience, it is time to start applying for jobs. While you may not have the required experience for senior data analyst positions, there are plenty of junior data analyst jobs that will be a perfect fit for your skills. And, if you manage to pull your weight at this job, who says that you cant eventually work your way up to a senior position?

To find a job as a junior data analyst, we recommend using job search engines such as Indeed, Monster, and Glassdoor. Simply enter “junior data analyst” or “entry-level data analyst” into the search bar and you will be presented with a list of relevant job postings.

When applying for jobs, make sure to:

  • Highlight all of your relevant skills – both hard (e.g., programming) and soft (e.g., communication) – in your resume and cover letter.
  • Include all your data analysis projects in your resume and include a link to your portfolio.
  • Tailor your application to each individual job, highlighting the skills and experience that make you the perfect fit for that particular position. For example, if you want to work as a data analyst in finance, make sure to emphasize all the projects where you worked with financial data.

One more thing – do not get discouraged if you do not hear back from every company that you apply to. The job market is competitive and even the most qualified candidates will not always get the job they want.

Most of the time this has nothing to do with how qualified you are. Instead, the company might be hiring internally and the job posting was just a formality. Alternatively, the company might have already found a suitable candidate.

Whatever your reasons for not striking gold immediately, know that the demand for data analysts is huge in the modern job market. Eventually, you are bound to find a company that is looking for someone with your skills and experience.

7. Use your working experience as leverage for senior roles.

Once you do manage to land a job as an entry-level data analyst, make sure to give it your all and learn as much as you can. If you do a great job, the company might be willing to eventually promote you.

Alternatively, if you decide that this particular company is not a good fit for you, use this opportunity for learning how to work on real-life data sets and projects. All your skills will come in handy when you eventually move on to your next job as a data analyst.

Once you have gained enough working experience to start applying to senior data analysts roles, you’ve hit the jackpot. With a few years of experience under your belt, you will no longer have to rely on your portfolio and online presence to get noticed, your salary will skyrocket, and at this point, most hiring managers will no longer care about whether or not you have a master’s degree, bachelor’s degree, or no degree at all.

Of course, the path to becoming a data analyst without a degree is not an easy one and it will require a ton of work, but it is definitely doable. If you have the passion and the dedication, anything is possible!

Sander Tamm

Sander Tamm

Founder @ Degreeless. I write about online education, self-teaching, and the job market. Last year, my articles were read by over 1 million people and my writing has been featured by Neil Patel, AOL, HackerNoon, The Baltimore Sun, Independent Australia among others.