How To Become a Data Analyst Without a Degree

Whether you hold a college degree or not, transitioning into data analytics is within reach. This post will guide you through the essential skills needed, what employers look for, and how you can prove your analytical prowess without a traditional educational background.

From identifying your knowledge gaps to building a compelling portfolio, we’ll provide you with actionable strategies to make your mark as a data analyst. Dive in to discover how you can unlock a rewarding new career path in data analytics.

Can You Land a Data Analyst Job Without a Degree?

Absolutely! Many data analyst roles value skills over specific degrees. Liberal arts graduates, known for their critical thinking and communication abilities, often excel in these positions. Showcasing your data skills through a strong portfolio and certifications can help you effectively compete in the job market regardless of educational background.

How To Become a Data Analyst Without a Degree

If you don’t have a degree in data analytics, you’ll need to find other ways to prove your skills. So, you’ll need a strong foundation in the technical, practical, and soft skills data analysts use in their day-to-day job responsibilities.

Identify Your Knowledge Gaps

Before diving into data analytics, assess where your current skills stand and identify what you need to learn. Start by researching the foundational skills required in data analytics, such as statistical analysis, programming in Python or R, and data visualization techniques. Evaluate your proficiency in these areas and pinpoint any deficiencies. This initial step will help you tailor your learning path effectively, focusing your efforts on acquiring the necessary skills and knowledge to bridge these gaps, thus streamlining your journey toward becoming a skilled data analyst without a formal degree.

Consider Taking A Course

Opting for an online course is an excellent alternative if formal education isn’t feasible. Industry professionals often lead these courses and include career support. They cover essential skills like data visualization, data management, and technical competencies required in the field. Many courses also provide certifications in data analytics, accelerating your entry into the industry and offering a fast track to becoming a data analyst.

Acquire the Required Data Analytics Skills

To become a data analyst, you’ll need to learn the data science tools that data analysts use, have a thorough understanding of the mathematical principles underlying data analysis, have a command of the soft skills needed to solve problems, and be able to communicate your findings. Skilled data analysts can manipulate and clean large data sets and perform statistical analysis to glean meaningful data insights.

Learn Data Analytics Techniques and Fundamentals

There are four primary types of data analytics. Each has a different purpose, but all are commonly used for business insights. To be a successful data analyst, you’ll need to be able to understand and implement each of these data science methods when relevant:  

• Descriptive Analysis: Answers “What happened?” by using current and historical data to highlight trends and patterns. Examples include financial statement analysis, demand trends, survey results, and tracking progress towards goals.
• Predictive Analysis: Answers “What might happen in the future?” by using historical data to forecast outcomes, utilizing machine learning and statistical algorithms. Examples include merchandise planning, predicting equipment failures, and consumer behavior predictions.
• Diagnostic Analysis: Answers “Why did this happen?” by identifying root causes of phenomena. Examples include investigating lost revenue causes, analyzing risk factors for data breaches, and evaluating marketing campaign success.
• Prescriptive Analysis: Answers “What should be done?” by applying techniques like simulation, neural networks, and machine learning to suggest actions. Examples include making investment decisions, content recommendations, fraud detection, and product development scheduling.

Work on Real-Time Projects and Case Studies

The best way to learn data analysis is by doing it. Start by analyzing case studies in fields such as business, government, and healthcare. By examining successful case studies, you can see the role data analytics plays in determining advanced metrics and steering growth.

After you’ve studied and understood some existing case studies, start working on live projects. Sites such as Kaggle, GitHub, and Our World in Data provide open-source datasets that you can use to build your own projects. Working on real-life problems will give you experience in executing data analytics projects from beginning to end.

Get Certified

Getting relevant certifications in data analytics is one of the best methods of proving your abilities, particularly if you don’t have a degree. Many companies—such as Cloudera, SAS, and Microsoft—offer certifications on the tools data analysts use. You can improve your chances of getting a job in data analytics with the following certifications:

• SAS Certified Data Scientist
• Cloudera Certified Associate: Spark and Hadoop Developer Certification
• Microsoft Certified Azure Data Scientist Associate

Online courses such as Springboard’s Data Analytics Career Track are a valuable way to document your data analysis skills. With its up-to-date curriculum, a bootcamp can offer you a step-by-step process for acquiring all of the relevant skills you need. There are also courses in data science to explore that can help you upskill as a data analyst.

Create a Strong Portfolio

If you don’t have a college degree and want to land a job in data analytics, your portfolio is the most important aspect of your application. Data analysts with a portfolio gain a natural edge. Employers want to see that you have the necessary skills and knowledge and are capable of applying them. They also want to see that you can extract meaning from raw data and use that information to create actionable insights and solutions. If you know what industry you’re interested in working in, include projects that are relevant to that field.

Your data analyst portfolio should include examples of all phases of the data analysis life cycle and demonstrate your ability to use tools, including:

• Scraping data
• Cleaning data
• Performing all four types of analysis
• Creating data visualizations
• Writing a story to convey your findings
• Collaborating with others
• Using tools such as SQL, Tableau, R, etc.

Participate in Competitions

Participating in competitions will give you experience conducting real-world data analysis and also give you the opportunity to connect with your peers. Competitions, such as those hosted by Kaggle, give you a chance to grow your skills by participating in exciting projects. You can also create your own competitions for your community, which is a great chance to demonstrate leadership skills and enhance your resume.

Network

Use your network to land your next job. Keep all of your professional social profiles updated with your skills and certifications. Let your friends, family, and professional connections know that you’re looking for a job. Connect with others. Using networking to tap into the hidden job market is more effective than blindly answering ads, although you should do both.

You’re more likely to get seriously considered for a data science job if you’ve been personally recommended, so don’t be afraid to reach out to people you know.

Data Skills To Know

Technical

• Programming skills. The two most commonly used programming languages for data analysis and data science are R and Python. They each have their strengths and weaknesses, and endless debates exist about which is best for working with data. If you don’t want to choose a side, learn both to prepare for anything. You’ll need these data skills to become a data analyst and work in the data market.
• SQL. SQL stands for Structured Query Language. It’s used to maintain, query, and manipulate data stored in large databases.
• Data cleaning and sorting. Data analysts spot trends and patterns in data, which they then use to gather insights and make predictions. However, your predictions are only as good as your data. So, data analysts must ensure their data is accurate, timely, and relevant. Cleaning and sorting data eliminates duplicate, inaccurate, and irrelevant data, so you’re not making decisions based on wrong information.
• Data visualization. Data visualization allows you to share your findings with a broader audience in an easy-to-understand format. Not everyone can derive meaning from looking at a CSV table, but almost everyone can understand data when it’s displayed in intuitive graphs and other visualizations. If you want to become a data analyst, you’ll need this skill.
• Data warehousing. A data warehouse is where you store and manage your data. It needs to be stored securely but in a manner that’s easy to access and easy to retrieve. Not all data analysts become specialists in this field, but some data professionals focus exclusively on maintaining data warehouses.
• Advanced Excel. Many data analysts work with Excel if they’re doing preliminary work or working with smaller data sets. You’ll need to know to use its advanced functions to extract all of the information you can.
• Matlab. Matlab is a high-level programming language and platform that uses computations and algorithms to analyze large amounts of data and provide a visual representation of it. Most data analysts have a working knowledge of this language.

Practical

• Statistics. Statistics is the core of what data analysts and data professionals do. It’s a field of mathematics that’s concerned with collecting, analyzing, interpreting, and presenting data. Understanding statistics will help you differentiate between good and bad data and help you make reasonable conclusions.
• Mathematics. As a data analyst, you’ll also need to know other types of math that are used to build and deploy models for data analysis, including linear algebra, calculus, and discrete mathematics

Soft

• Communication and storytelling. Data analysts start by trying to understand the problem that they’re trying to solve, which involves communicating with non-technical stakeholders. And once you’ve completed your analysis, you’ll be expected to share your findings by crafting a compelling narrative. Data analysts need good presentation skills.
• Creative thinking. Many people don’t think of data analysis or data science as a creative field. But every step of data analysis—from innovative models to discovering new data collection techniques—benefits from original ideas. Data analysts have many opportunities to express their creativity.
• Analytical mindset. An analytical mindset allows you to analyze information, solve complicated problems, and identify patterns as a data analyst.

Tools You Should Be Familiar With

• Tableau. Tableau is a very powerful data analysis tool. It lets you create a dashboard with interactive maps and stories for exploring and analyzing data. Tableau’s intuitive UI allows users to answer questions and make connections with the data. Relationships and patterns are easy to explore. Tableau has a range of offerings, including mobile, web, and desktop applications.
• SAS. SAS stands for Statistical Analytics Software. It’s a tool for analyzing statistical data. You can automate the process of running SQL queries with macros that allow you to retrieve, analyze, and issue reports on statistical data. SAS is a proprietary software, whereas R and Python are both open source.
• Power BI. Microsoft’s Power BI is another data analysis and visualization tool that provides powerful business insights. It lets you visualize your data and share it across your organization. You can even embed it into your website or app. Its versatility comes from its individual components to create customizable solutions.
• Apache Spark. Apache Spark is a fast, flexible, developer-friendly, open-source platform that does large-scale data processing and machine learning. It can also distribute data processing across multiple computers to help facilitate machine learning tasks that require more computing power. It’s used by major companies in all industries, including banks, telecommunication companies, and major tech companies like Apple, Facebook, IBM, and Microsoft.

How Much Can You Make as a Data Analyst?

Data analysts’ salaries vary based on education, experience, local, and skills. According to data from Payscale, here are some typical ranges:

Tips from Experts

Entry-Level Data Analytics Jobs Worth Considering

Here are the types of jobs you’ll be qualified to do after you learn basic data analysis skills:

FAQs About Becoming a Data Analyst

Since you’re here…
Switching to a career in data analytics is possible, no matter your background. We’ve helped over 10,000 students make it happen. Check out our free data analytics curriculum to gauge your interest, or go all-in with our Data Analytics Bootcamp.