How to Become a Data Analyst in 2022

Published on 22 October 2021

Reading time 9 minutes

Today, there are almost five billion active internet users and close to two billion websites. At any given time, the average person is surrounded by 26 smart objects

In short, we’re generating a lot of data. So it’s hardly surprising that search interest in the topic “data analysis” has been climbing steadily over the past five years:




All of which means that data analysts are in high demand right now –– which in turn means lots of incentives for becoming a data analyst.


According to Indeed, the average salary for a data analyst in the US is $65,786, plus a $2,500 yearly cash bonus. Not only that, but data analysts can expect a range of benefits, including:

  • Health savings account
  • Computer assistance
  • Gym membership

Sounds good? Read on to learn how to become a data analyst in 2022 (and beyond):


1. Learn the Fundamentals of Data Analysis


Your first step on the road toward becoming a data analyst is to immerse yourself in the world of data.


After all, if this is going to become a lifelong career, you want to know that you actually enjoy it.


Start by digging into the theory. That means researching the various types of data analysis –– namely descriptive, diagnostic, predictive, and prescriptive. And get to grips with the wide range of analytics techniques, such as:

  • Regression analysis
  • Factor analysis
  • Cohort analysis
  • Cluster analysis
  • Time-series analysis

You’ve got a few options for how to do this:

  • Do all the reading and learning yourself
  • Try to get some sort of on-the-job training (like earning a secondment to your organization’s data analytics team)
  • Enrolling in a third-party training course (like this one from Wild Code School)

Realistically, the third option is the most realistic for the average aspiring data analyst. Most people simply don’t have the patience to learn everything under their own steam. And relatively few work for organizations that both have an existing data analytics team and offer secondments.


What’s more, signing up for a course gives you the chance to earn a formal certification or accreditation.


At the very least, those qualifications will help you stand out over other candidates. And in some circumstances, an employer might not even bother reading your whole resume –– let alone invite you for an interview –– if you don’t have a specific qualification.


2. Familiarize Yourself With Analytics Platforms & Tools


By this point, you should have a decent understanding of the basics of data analytics.


Now, it’s time to start focusing on the specific hard skills that employers look for.


Part of this should involve getting to know the various analytics platforms and tools you’ll be working with day in, day out. After all, you’re going to need to know your way around an analytics platform if you’re going to make it as a data analyst.


Unsurprisingly, there are a whole lot of different platforms out there. It’s unlikely you’ll be able to build up expert-grade knowledge of each and every one of them, so don’t bother even trying.


Instead, concentrate on learning the practicalities of the biggest and most popular analytics tools.


Gartner’s Magic Quadrant for Analytics and Business Intelligence Platforms is a good starting point:

Focus on those platforms on the right –– the likes of Microsoft, Tableau, Qlik, and Thoughtspot. Those are the ones you’re most likely to encounter in the wild. If you’ve got some level of experience with those platforms, or perhaps a professional certification in one of them, you’ll be well placed to land a job.


3. Hone Relevant Soft Skills


Data analytics is a highly technical role, but that doesn’t mean you can afford to overlook your soft skills. These are often the characteristics and traits that help potential employers choose between one candidate and another.


So which soft skills are key to the data analyst role?


Well, according to Indeed, some of the most important are:


Critical Thinking


Data analysts are often asked to collect and analyze data for a specific purpose. That means they need to understand which data to collect in the first place, then figure out how to process it in order to glean the necessary information. Doing that requires excellent critical thinking skills.


Public Speaking


You might not realize it, but an important part of the data analysis role involves presenting data and the findings of your analysis. Often, this requires the ability to discuss complex themes in simple, non-technical terms.


Communication


Data analysts don’t work in isolation. As well as working closely with other members of the data analysis team, you might find yourself collaborating with the IT department, UX designers, data scientists, and even non-technical colleagues. Excellent communication skills are required to work effectively with such a diverse group.


Problem-Solving


Chances are you’ll occasionally encounter technical issues during the analysis stage. It’s up to you to find solutions.


Attention to Detail


The results of your analysis might be used to inform high-level decisions impacting your organization’s future direction, so it’s vital those results are based on accurate, error-free data.


Project Management


It’s not unusual for data analysts to manage projects involving a team of other analysts or IT specialists. With a lot of moving parts to keep track of, project management experience can be vital.


Writing


Communicating the results of your analysis isn’t just about public speaking. It can also involve writing up reports. Strong writing skills will ensure your key findings and explanations are understood.


Research


However experienced the data analyst, sometimes they will encounter new scenarios and challenges. To find solutions or recommend relevant systems and processes, they need to carry out effective research.


4. Get Out There & Network


Remember: it’s not just what you know, it’s also who you know.


Simply put, if you have a strong professional network full of people who’ve already established themselves as data analysts –– and, ideally, who have some sort of sway over recruitment-related decisions –– it’s going to be much easier to land a job.


Not only that, but networking can introduce you to potential mentors and peers; people you can lean on for support and advice throughout your career as a data analyst.


It might even alert you to opportunities you’d never otherwise have considered. Maybe you’ll get talking to someone about mobile healthcare, decide it sounds like an interesting career path, and wind up applying for a role at an m-health app developer. You won’t know unless you network.


Of course, to many of us, the idea of networking seems pretty dreadful; hanging out at boring events trying to strike up awkward conversations with people who don’t want to speak to you.


But the reality can be a lot different –– sometimes it can even be enjoyable!


Also, bear in mind there are several different forms of networking beyond the classic professional event setting, such as:

  • Using LinkedIn: It’s no secret that the Microsoft-owned social network plays a big role in contemporary recruitment. Indeed, over 75% of people who recently changed jobs used LinkedIn to inform their career decision. So connect with peers and thought leaders, and join data analytics groups.
  • Connecting with fellow data analytics students: There are lots of student-dedicated communities, such as Slack groups and forums. These are a great space to get support from your peers.
  • Browsing Meetup: The platform is used to organize data and tech-related events across the world, from free, informal sessions to large professional events. Find what’s happening in your area and get involved.

It can be tempting to ignore the networking stage because you think it’s just “not for you”. But everyone else is doing it, so you owe it to yourself to get out there and make some valuable connections!


5. Choose An Industry (& Learn About It)


Being a data analyst is a bit different to being a vehicle mechanic.


If you’re a vehicle mechanic, you’re (probably) going to work in the automotive industry, or maybe in logistics.


Data analysts, on the other hand, can work in pretty much any industry that requires data analysis. That’s a lot of industries.


But just because you could work in pretty much any industry, that doesn’t mean you should take the first job that comes along.


The types of data you’ll be analyzing –– and the reasons behind your analysis –– will naturally vary from one industry (and organization) to another. So before you start applying for data analyst jobs, read up on multiple industries and decide on one or two that most closely align with your skills and interests.


For instance, maybe you decide you want to be a data analyst at a marketing agency. In that case, you could boost your chances by learning about the practicalities of a career in digital marketing, and familiarize yourself with complementary subjects like:

  • Content marketing
  • Search engine optimization
  • Pay-per-click advertising

Or say you’re going for a job at a SaaS company; maybe you want to swot up on customer service-related topics like chatbots and contact center automation.


Sure, knowing about those things won’t necessarily make you a better data analyst. But it’ll definitely help you stand out during the interview stage and find your feet faster during the onboarding process.


Conclusion


There are a lot of steps in the process of becoming a data analyst.


Earning a formal qualification could take months (or years) of study, and that’s before you even start applying for jobs.


But don’t let that put you off. With Wild Code School, you could become a qualified data analyst in just five months, picking up highly desirable technical skills like Python and SQL along the way. Alternatively, a business intelligence course can give you a deep understanding of essential tools like Power BI and Tableau


Either way, they’ll dramatically improve your chances of landing a data analytics job.


About the Author


Phil is a freelance writer specializing in all things digital marketing. He worked agency-side in the UK for 10 years, before leaving when he realized he could basically do this job from anywhere. Beyond marketing, he loves travel, his cat, and wearing a vest under an open shirt. Connect with him on LinkedIn.