How to Become a Data Analyst


Featuring Insights From Matt Brems & Vish Srivastava

Read: 4 Minutes

So, you want to be a data analyst? GA instructors Matt Brems and Vish Srivastava are data experts with deep experience across a wide range of industries. If anyone can start you on the path to your dream job of data analyst, it’s these two. Read on for advice and guidance on how to get there, what it takes and where to look for the jobs of the future.

Tell us about your experience in data — what brought you to the field?

Matt Brems: I was attracted to statistics and data science because I felt that too many decisions were made based on a gut feeling instead of data. In my experience, that usually meant that the loudest person in the room would make the decisions. Using statistics and data science to analyze evidence helps us to make better, more informed decisions that are less susceptible to bias.

Vish Srivastava: As a product manager, I’m faced with a deluge of data and need to make sense of what matters and how to use data to inform decision-making around the product. Quickly visualizing data and creating dashboards that get widely distributed are both critical skills to be an effective product manager.

What qualifications do you need to be considered for a data analyst job?

MB: Jobs that involve data analysis have many different titles. Some companies call these “data analysts,” other terms include “business intelligence analyst,” “marketing analyst,” “data scientist,” and others. Since different companies will have different names for the job, it’s not surprising that the qualifications for a data analyst (or similar) role will vary wildly from company to company.

The most common qualification is to know SQL, or Structured Query Language.

Most data analyst roles will expect some experience with data visualization. Having some background in statistics — even one or two courses at the college level or having online certifications — is often expected.

What traits make a data analyst successful?

VS: Simplicity, integrity, empathy, and patience. Simplicity because it can be very easy to go crazy and complicate an issue when you are in the weeds. Data analysis must always resist this urge and instead create clarity and complicity for stakeholders.

Integrity, because data analysts make a lot of crucial decisions like which outlier to remove and which insights to show vs. which ones to leave behind. You can easily tell a story that isn’t really true if you wanted to, and data analysts must hold themselves accountable to the truth. Mark Twain wasn’t kidding when he said that “there are lies, damned lies, and statistics.”

Empathy, because data analysts must always look at deliverables from the perspective of an audience. Will this be helpful to them, is it immediately legible, what questions will they have that you can preemptively address?

And lastly, patience. Data analysis involves a thankless job that people don’t really talk about — data cleaning!

How does someone start a career in data analytics

MB: I would start by searching data analyst roles at companies in which you’re interested. If you have most of the qualifications listed, go ahead and start applying! If you feel like you lack most of the qualifications, start exploring resources and courses that can help you close that gap. The three most important skills to know are likely to be SQL, statistics, and experience with a data visualization tool. There are lots of tutorials and courses available to teach you each of these.

How long does it take to become a data analyst?

MB: Data analysts are, in many cases, entry-level roles. New graduates from bootcamps or from college or university can often be accepted to data analyst roles. Some roles may require up to a few years of experience.

What’s the next big disruption? Where should candidates look for the jobs of the future?

VS: Every sector has been transformed by data, and this will continue to happen. But I think a very interesting one to watch is healthcare. Data in healthcare is trapped in various silos, like hospital systems (e.g., EHRs), insurance companies (e.g., claims data), clinical data (e.g., lab tests), and even personal health and fitness data (e.g., Apple Watch). This fragmentation of data, along with various pieces of regulation that govern how and when data is shared and used, means there is so much value that is currently untapped. As the industry moves to more interoperability and hopefully does so in a way that respects patient privacy and patient safety, we will see new opportunities quickly emerge.

Matt Brems teaches our Data Science Immersive, a bootcamp where students become fully-fledged data scientists in 12 weeks. He runs the consultancy BetaVector, where he solves data problems with Fortune 500 companies and startups alike. 

Vish Srivastava teaches our Data Analytics course. He has led multidisciplinary teams across many different tech sectors. He is currently a product leader at Evidation Health and is obsessed with building products to make the world a better place.