How To Become A Data Analyst Without A Degree

How To Become A Data Analyst Without A Degree


Given the rapid growth of data and its increased relevance over the past decade, there are a lot of reasons why many are looking to break into the profession. With its tech-adjacent status and relative stability in the workforce, the data industry offers a host of opportunities for both degree and non-degree holders.

However, data analytics, while one of data’s most accessible fields, can still be tough to break into without a relevant degree. This article delves into how to become a data analyst without a degree, offering insights into essential skills and strategies to enhance your prospects of securing a role in this field.

What Does A Data Analyst Do?

Data jobs are often difficult to distinguish from each other. More often than not, different positions can have overlapping responsibilities, with differences described only in industry jargon. To keep it simple here, data analysts can be described as the detectives of the data world, working with numbers and patterns to extract meaningful insights.

The role revolves around collecting, processing, and making sense of vast amounts of data to help businesses make informed decisions. Using statistical tools, programming languages, and data visualization techniques, data analysts interpret complex data sets to translate them into actionable information.

During an average day, a data analyst might have to:

  • Query and pre-process data, ensuring its accuracy and relevance to specific business questions.
  • Use statistical methods and tools to uncover patterns and trends within the data.
  • Create visualizations, such as charts or graphs, to represent data findings comprehensively.
  • Collaborate with various business teams to understand their data needs and provide actionable insights.
  • Communicate findings and share recommendations with both technical and non-technical stakeholders.

Data Analysts Versus Other Data Positions

The key distinction between data analysts and other data professions is the scope of work. For instance, data scientists design complex algorithms and predictive models, while data engineers focus more on building infrastructure for data generation, storage, and retrieval. Data analysts, on the other hand, analyze existing data to identify trends, analyze the results of decisions or measures, and produce actionable reports. Regardless of the nuanced differences, the ultimate goal of a data analyst is to provide clarity and drive decision-making through accurate, data-driven insights.

What Should I Learn To Become A Data Analyst?

learn data analyst

Data analytics is a broad field and– as a relatively new position– the required skill set can vary from company to company. Nevertheless, some of the fundamental skills you’ll need for every data analytics position include:

  • Statistics: You should be well-versed in the measures of central tendency, regression, hypothesis testing, probability, correlation, and standard deviation.
  • Programming: Most data analysts use Python for a variety of tasks, such as data extraction, automation, and processing. At a minimum, learning about fundamentals in operations, flow control, iterative structures, and data structures will be helpful.
  • Python Libraries: Utilizing the constructs implemented within important computing libraries such as Pandas, NumPy, and Seaborn can be quite helpful.
  • Data Visualization Tools: Tools like PowerBI, Tableau, and Seaborn are industry-standard visualization tools that you should definitely check out for data presentation and analysis.
  • Excel: Excel is one of the most important software in the business world, and many small to medium-sized companies heavily rely on it for data processing. Functions, pivot tables, and other features are essential tools for data analysts.
  • SQL: Especially in medium to large-sized organizations, data analysts are responsible for pulling and retrieving data from large databases. Learning SQL can help attract more job offers and allow you to branch out to different data fields in the future.
  • Domain Expertise: Familiarize yourself with the industries you’re interested in, whether it’s finance, healthcare, e-commerce, etc. Each industry has its own set of data nuances and key performance indicators.

Resources for Learning Data Analytics

If you’re looking for a comprehensive set of data analytics learning materials, Interview Query offers various resources that cover everything from statistics to domain expertise. Our data analytics learning path includes in-depth lessons on A/B testing, product metrics, business case, and other topics.

For more practice, try out the SQL and Python learning paths, as well as more interview questions, takehomes, and challenges that can help you hone your skills.

Compensating for the Lack of a Degree

Let’s face it: applying for a data analytics position without having a degree is tough. Not only are you competing with many others in the same boat, but you’re also facing competition from industry-native applicants and career shifters with degrees.

The job market is hard enough as is, so it’s important to market your experience and skill sets in other ways to distinguish yourself from competing candidates. Some ways you can do this include:

Building a Portfolio

If there’s anything more important than a degree, it’s your skills, which can be showcased through a strong portfolio. Think about it from a recruiter’s perspective– why should they hire you, a non-degree holder, over someone with a degree? What do you have to show for your skills outside of your resume?

Your portfolio can include personal projects, freelance work, or even contributions to open-source projects. Make sure to incorporate detailed data visualizations, thorough analyses, and clear narratives to highlight not only your technical skills, but also your ability to communicate complex ideas.


More often than not, it’s not just about what you know, but who you know. Cultivating relationships within the industry can open new doors that may be otherwise closed based on qualifications. Attending industry conferences or participating in online forums/communities can help connect you to professionals who can provide referrals, mentorship, or even jobs.



While not a direct substitute for a degree, certifications can serve as validation of your skills in a particular area. Consider certifications like Microsoft’s Data Analyst Associate certification, or those offered by platforms like Coursera or Udemy in Python, R, SQL, or specific tools like Tableau.

Check out Cousera’s Data Analytics certificate to further boost your skills. Sign up to enroll for a 7-day free trial here.


Soft Skills

Technical expertise is vital, but soft skills are equally important. Throughout the interview process, emphasizing your problem-solving capabilities, teamwork, adaptability, and communication skills can set you apart. Being able to understand business needs, collaborate across teams, and present findings to non-technical stakeholders are all important parts of any data position.

Job-Hunting Strategies

Tailoring your applications to match specific job postings is especially important if you’re coming from a non-data analyst background. You can customize your resume for each position by highlighting different relevant projects and experiences.

Additionally, filtering your job search by company may maximize your chances of securing a role. The hiring process for startups or smaller-sized companies is generally less rigid compared to larger organizations.

Continuous Learning

It’s important to stay updated with the latest trends, tools, and techniques. Attending workshops/webinars and reading articles will help advance your knowledge and skill set, which will only work in your favor if you don’t have a formal degree.

Other Resources

If you’re having trouble finding ideas for your data science portfolio, here is an exhaustive list of data analytics projects sorted by difficulty, data analytics case study and how to start a data analytics project. You may also find it helpful to study from a list of SQL questions and excel data analyst questions that data analysts often encounter in their interviews.

Check out our video on how to get a data job in 2023:

How to get a data job in 2023

Additionally, if you want to chat with industry experts, join Interview Query’s active Slack community (see Community tab above).