How to Become a Data Analyst Without a Degree?

Data analysts are in high demand and will continue to be for the foreseeable future—it’s an excellent time to consider jumping into this field!

And if you’re considering becoming a data analyst, you’re probably wondering how to get started and have questions like, “Do I need a degree?”.

If you don’t have a degree (or have no desire to spend the money getting one), don’t worry; we’ll explore the skills you’ll need to start your new career as a data analyst without going to a formal school.

How Can I Become a Data Analyst With No Degree?

Without a data analytics degree, you’ll have to show future employers that you have the necessary skills needed to succeed in the role. Here’s how to do it:

Develop the Necessary Technical Skills

Strengthen your programming skills. As a data analyst, you’ll need a solid understanding of programming—especially the most common languages in the field: Python and R. You can learn either/or, but we recommend having at least a basic understanding of both languages.

Learn how to clean and sort data. A data analyst’s insights and predictions are only as good as their data—it’s crucial to know how to clean data to get rid of inaccurate, duplicate, or irrelevant information to come to better conclusions.

Jump into SQL. Structured Query Language (SQL) is the lifeblood of a data analyst. You’ll need it to maintain, query, and manipulate the data you rely on to do your job.

Want to get a jumpstart on your data analyst career? Reach out to our data science mentors.

Develop the Necessary Practical Skills

Although data analysts strongly focus on programming, you’ll still need to understand how statistics and mathematics affect your analysis.

Statistics are at the core of every data analyst’s job—it’s a mathematical field that focuses on collecting, analyzing, and interpreting data. You need to understand statistics to differentiate between relevant and irrelevant data to reach reasonable conclusions.

Mathematics, particularly calculus, linear algebra, and discrete mathematics, is crucial for data analysts to build and deploy efficient analytical models.

Develop the Necessary Soft Skills

Many roles in the tech field don’t require communicating with those in nontechnical roles—however, this is different for a data analyst. If you go into the field, you’ll frequently share information with people that have no knowledge about what you do.

Having the right soft skills is crucial if you want to get in with a large company as a data analyst, as you’ll regularly speak with key stakeholders, SMEs, and members of the corporate office. The three most important include:

Creative thinking: You might not think of data analysts as being creatives, but every step of analyzing data benefits from original ideas (creating innovative models, uncovering new ways to collect data, etc.).

Communication: The first thing a data analyst does is try to understand what problem they need to solve, which often involves communicating with stakeholders outside of the tech field. Then, you’ll need to share your findings in a way that makes sense to everyone.

Problem-solving: You also need an analytical mindset to solve complicated problems, analyze loads of information, and identify patterns in your data.

Learn the Fundamentals of Data Analytics

To be successful as a data analyst, you need to understand how to implement different data analysis techniques. There are four primary types:

Descriptive Analysis

With descriptive analysis, you use data to determine what happened by analyzing historical and current data. This step is often the first one data analysts take when analyzing information. It’s also the most common type of data analysis.

Data analysts typically use descriptive analysis to look into:

Financial statements
Survey results
Progress towards goals
Demand trends
Engagement results

Diagnostic Analysis

While descriptive analysis looks at what happened, diagnostic analysis looks at why it happened. Data analysts use this technique to uncover the cause of anything. Diagnostic analysis is helpful for organizations to understand why their strategies are doing well (or not).

You can use diagnostic analysis to:

Find the cause of dropping revenue.
Determine which marketing campaigns perform best
Understand the risk factors for data breaches

Predictive Analysis

We’ve covered the how and why, but what about looking into the future? Data analysts use predictive analysis to attempt to determine future outcomes based on historical data and trends. It’s arguably the most complicated type of data analytics and incorporates techniques like machine learning and statistical algorithms.

Data analysts commonly use predictive analysis to predict:

Future resource needs
Consumer behavior
Merchandise planning
Population trends

Prescriptive Analysis

Finally, we have prescriptive analysis, which answers the question of what to do next after analyzing the data. This analysis incorporates techniques, including heuristics, neural networks, simulations, event processing, and machine learning.

Prescriptive analysis helps determine the following:

The best way to invest
How to detect fraud
Content recommendations

Get Certified With a Coding Bootcamp

While you don’t need a full-blown college degree to become a successful data analyst (even without previous experience in the field), you need a formal, structured approach to learning the skills you’ll need.

The best way to get that education is with a project-based course—like our coding bootcamp.

You’ll graduate from the course with more than the knowledge you’ll need to become a data analyst; you’ll also get a certification and leave with a strong portfolio that shows future employers your skill level.

Interested in becoming a data analyst without getting a degree? Check our coding bootcamp’s available slots here!

Speaking of portfolios:

Create a Solid Portfolio

There’s something better than a college degree when you want to become a data analyst—a solid portfolio that proves to potential employers you can do the work; it’s the most crucial piece of your application.

Your future employers want to see that you not only know about the data analytics process, but they want to see that you can apply them, too.

Your portfolio should include pieces from each phase of the data analysis cycle and show your ability to use additional resources. For example, your portfolio might include the following: