How to Become a Data Analyst [2024 Roadmap]

According to the US Bureau of Labor Statistics, employment of data professionals is projected to jump 25% by the year 2030, meaning data analytics has one of the highest growth rates in the country. 

This comprehensive guide is your roadmap to a rewarding career in data analytics. We meticulously unpack the essentials—from initiating your journey to mastering the specific skills employers seek. Data analytics offers the promise of high earning potential and positions you at the forefront of decision-making processes, empowering companies to navigate the complexities of the modern business landscape.

Buckle up and get ready to unlock the power of data!

Is It Hard To Become a Data Analyst?

Becoming a data analyst isn’t hard per se, though it does require specific technical skills that might be more challenging for some than others. Additionally, because of continuing advancements in the field, data analysis is a career path that requires ongoing education. 

How To Become a Data Analyst

Follow these steps to land your first job as a data analyst:

Understand the Fundamentals

The first step of your journey is ensuring you fully grasp the fundamentals. You want to ensure you understand the key principles of data analytics, the different types of data analysis, and the tools data analysts use.

Consider a Course

One of the most efficient and comprehensive approaches to acquiring the necessary skills for a data analyst role is to enroll in an online analytics bootcamp. Through this focused learning path, you’ll gain in-depth knowledge and have the opportunity to receive guidance from mentors who are currently practicing in the field.

Meet the Educational Requirements

Data analysts spend a lot of time working with numbers. Because of this, many employers look for candidates with bachelor’s degrees in mathematics, statistics, computer science, or a related field. 

However, you don’t necessarily need a formal degree to land a data analyst job. In fact, companies often hire candidates who have completed an online course in data analysis or a data analytics bootcamp, or can demonstrate the requisite technical knowledge and skills. Look for the requirements listed in the descriptions of jobs that interest you.

Learn an Essential Programming Language (or Multiple)

Not all data analysts need to know how to code (though most data scientists do). In fact, at the start of your career, a cursory familiarity with the more popular querying languages should suffice. However, if you want to become the best (most employable) data analyst you can be, consider learning one or more of the essential programming languages (i.e., Python, R, SQL). 

Get Familiar With Data Analytics Tools

Data analysts use a wide variety of tools and applications to collect, manipulate, and analyze data. You should familiarize yourself with the most used analysis tools, including:

• Tableau. A popular data visualization tool used to perform data analysis, manipulation, and visualization
• Microsoft Excel. A well-known spreadsheet application commonly used by data analysts   
• Microsoft Power BI. A free data visualization and business insight tool ideal for collaboration
• Google Analytics. A web-based analytics tool used to track website traffic
• Jupyter Notebook. An open-sourced web application that enables exploratory analysis using “big data” tools (e.g., Python, R, and Scala)

Develop Your Data Analyst Skillset

Having the right skillset is paramount to your becoming a data analyst. Make sure you’ve developed the following hard skills and soft skills:

Technical Skills

• Statistical analysis and math skills. Data analysts work with numbers. A lot. Data analysts should have strong math skills and be comfortable analyzing data sets.
• Programming and querying languages. In order to process data and make it understandable for others, analysts use various programming and querying languages, such as Python and R. As a data analyst, you probably won’t have to build many programs, but being familiar with the commonly used languages and understanding how they function is essential.  
• Microsoft Excel. MS Excel is a go-to tool for data analysts. It’s widely available, easy to use, and has a ton of useful features.
• Data visualization. If the insights derived from your analysis are incomprehensible, what is their value? A good data analyst can produce meaningful insights and present those findings visually in their most understandable form.
• Machine learning. One of the unofficial differences between data analysts and data scientists is that data scientists use machine learning to design new processes for data modeling. As a data analyst, you likely won’t have to do that. However, in order to work with data scientists effectively, you will need to be familiar with these processes and understand how they work.

Soft Skills

• Analytical thinking. Data analysis involves collecting raw data and refining it into meaningful information. Analysts analyze the data to uncover more profound truth and value once the data has been refined. This can only be done through analytical thinking.
• Problem-solving skills. Well-developed problem-solving skills help data analysts recognize the nature of the problem before them, the best data analysis tools and techniques to solve the problem, and the solution to the problem itself.
• Presentation skills. Data analysts must effectively present their insights in a way that everyone—mainly non-technical stakeholders—can understand. Strong presentation skills will enable you to convey your findings and predictions in a compelling manner that your audience will care about.
• Communication and interpersonal skills. Data analysts rarely work in a bubble. In fact, as an analyst, you’ll likely interact with a variety of different people, including colleagues and coworkers, managers, and subject matter experts. Having strong communication and interpersonal skills will help facilitate collaboration among different parties.

Pursue Volunteer, Open-Source, or Freelance Projects to Build Your Portfolio

Building a solid portfolio is one of the best and most surefire ways to demonstrate your analytical skillset. Pursue volunteer opportunities, find freelance work, or work on a personal project to highlight your analytical knowledge.

If you are considering showcasing a personal project in your data analyst portfolio but don’t have one readily available and are unsure where to start, consider using a publicly available data set. You can find all kinds of datasets on sites like Data.gov and Datahub.io.

Finding a dataset that covers a topic you are already interested in is helpful. But more importantly, choose data that the most proficient methods can best analyze—this will allow you to showcase your strengths to their fullest potential as an analyst or data scientist.

In addition to personal projects, you will also want to fill your portfolio with projects that demonstrate your analytical skillset and showcase your ability to tackle real-world problems as part of a team.

That’s where volunteer work and freelance projects come in. Look for opportunities that will allow you to collaborate with others while working on the different phases of the data analysis life cycle. Not only will this experience make a good impression on potential employers, but it will also help strengthen the data analytics skills you need to be successful.

Build Your Network

Good networking can help you get your foot in the door, develop valuable relationships with experienced professionals, find a mentor, and more. Consider these resources to start building your professional network today:

• LinkedIn. The well-known social networking service for professionals makes finding and connecting with data analysts and other like-minded individuals easy.
• Online Communities. Online communities aren’t just great for meeting people with similar interests. They are also a great source of support and helpful information.
• Conferences and Meet-Ups. Attending a data analyst conference or meet-up will allow you to network in person, which is a great way to make lasting impressions that could benefit you down the road.

Find a Mentor

No matter your profession, a mentor can provide guidance and feedback to help you achieve your career goals. Through them, you can learn lots of valuable insights, including how other analysts started out, the different career paths an analyst can take, as well as the pros and cons of those varying paths. As a mentee, you will benefit from your mentor’s real-life experience and gain actionable insights you can use to chart your ideal career path.

One way to find a good mentor is to use your professional network. Reach out to contacts to let them know you are looking for an experienced data analyst to become your mentor. Use professional networking platforms like LinkedIn to potential mentors and approach them about a mentorship.

Before you reach out to someone about becoming your mentor, be sure to have a specific goal in mind. Be prepared with detailed questions and answers to potential questions, as well. Your potential mentor will likely ask questions about your personal and professional life, including what your career goals are, where you would like to be, and why you want them to mentor you.

It is also a good idea to come up with a plan or structure for how you imagine your mentorship would work. You should be clear from the beginning about how much time you would like to spend with your mentor, the type of guidance you are looking for, and so on. Establishing what you are looking for early on is crucial if you want to get the most out of your mentorship as possible. 

Consider an Internship

Landing an internship as a data analyst is arguably one of the best ways to break into the industry. As an intern, you’ll work on real-world projects, further develop your analytical skills, and gain valuable insights into the world of data analytics. Internships also provide excellent networking opportunities. Internships also give aspiring analysts the kind of hands-on experience that looks great on a resume.

Polish Your Resume, Prep for Interviews, and Start Applying

Now that you’ve honed your skillset and compiled a professional portfolio, it’s time to start your application prep. Talk to your mentor: have them sign off on your portfolio and help you with interview prep. Tap into your network for job leads and begin applying for entry-level data analyst jobs.

Data Analyst Roles to Consider

How Much Can You Make as a Data Analyst?

While not the most important factor in your exploration of how to become a data analyst, being able to earn a living should be high on your list of considerations. As with most professions, salaries vary based on several factors, including geographic location, relevant work experience, etc. That being said, here are the current national averages for data analyst salaries:

• Entry-Level: Currently, the average base salary for entry-level data analyst job postings in the US is $47,669 per year.
• Mid-Level: Job opportunities for mid-level data analysts include an annual salary of $69,343 per year. 
• Senior: The average salary for a senior data analyst is around $102,820 per year.

Examples To Learn From

Curious to see how others began their career as data professionals? Check out the following videos for an inside look at how two successful analysts made the leap into the data field.

FAQs About Becoming a Data Analyst

We’ve got the answers to your most frequently asked questions:

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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.