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Valentina Perezalonso
Valentina Perezalonso
Content Marketing Manager
01 Mar, 2022 · 8 min read

Should you become a data analyst?

With the rise of technology and an increase in data generation, data and analytics are becoming a core business function, so it’s no surprise that a data-led strategy is the future for any successful business. Data now shapes how businesses operate and with that comes the need for skilled people to work with data. 

This upward trend opens up new paths for those who have peeked into the data space but haven’t yet dared to jump in, as well as those who are already specialized in data analytics or data science and are looking for interesting opportunities.

If you're considering a career change and you’re interested in venturing into the expansive world of data analytics, then here’s how you can get started in this field and just a few of the reasons why you should become a data analyst.

What does a data analyst do?

A data analyst’s day-to-day work requires setting up databases and data analytics tools; collecting and examining large amounts of raw data; and filtering, cleaning, and modeling the data to then make observations and derive insights. Life as a data analyst involves thinking outside the box, being curious and communicative, and having a knack for technical matters.

Some key responsibilities of a data analyst are to spot trends and patterns, produce forecasts, question unusual behaviors, and identify errors. The driving force behind the dashboards and reports they create will always be the questions they pose — asking the right questions will help turn abstract business problems into actionable insights.

But most importantly, data analysts are storytellers. They use their data explorations as the basis for a summary of interesting facts which they then present to the respective stakeholders. However, it is crucial for analysts to present interpretations based on the data and not to tell stories that go beyond what the data actually shows.

Why you want to be a data analyst

As data and analytics become more and more important for businesses, one thing is clear: companies need data analysts. The job market for this role is booming and, perhaps even more exciting, data analysts are needed across all industries and company types, so there are plenty of opportunities to choose from.

Your next workplace could be a startup, an SMB, or a big corporation, and you could be working on either company-wide or team-specific data analytics for finance, marketing, or customer success.

The reason behind this constant need for data analysts is that their work enables the maximization of business value by better informing business decisions through a deep understanding of the company’s current state of affairs. Data analysts can also help illustrate which areas of the organization need to optimize their workflows and how, what can be done differently, and which direction the overall business should take. A data analyst then bridges the gap between business users and the data they generate.

How to start a career as a data analyst

When considering a career as a data analyst, there are two questions that people tend to ask themselves: “What degree do I need to become a data analyst?” and “How can I become a data analyst without a degree?” The real question, though, is, “What are the essential skills of a data analyst?”

Whether you have a university degree in computer science, data science, mathematics, statistics, or something completely different like business or international relations, one thing is certain: you need to be able to prove your technical and analytical skills. For some employers, having a university degree related to data analytics is non-negotiable, but there are more and more companies open to giving people who are changing career paths who have managed to gain the necessary skills a chance.

Skills you need as a data analyst

Here’s a priority list of skills to have as a data analyst:

Affinity for numbers and problem-solving

As a data analyst you’ll be working with numbers all day, thinking critically about them, and facing abstract problems that require the right strategy and questioning to be solved.

Statistical knowledge

Statistical thinking is crucial to work as a data analyst, as statistics deals with tasks such as data collection, preparation, analysis, and interpretation.

Microsoft Excel/Google Sheets

You need to start somewhere and that somewhere is the basics. Mastering Excel will equip you with the knowledge necessary to work with tables and perform mathematical calculations and statistical analysis.

Data visualization

In order to understand your interpretations, you need to translate complicated ideas into a format that is easily digestible for others. Data storytelling is key, so, to present your findings in a smoother and more engaging way, you need the support of visual aids like dashboards, reports, simple graphs and charts, or any other visual representation of your data.

Querying languages

As a data analyst, you’ll spend most of your time talking to computers instead of real people, which means that you need to speak the same language, like SQL, so that you can handle your databases and command certain data analysis tasks.

Statistical programming languages

Once the basics are sorted, you need to acquire the programming languages needed to perform more advanced analyses. These languages are Python and R.

Becoming a data analyst without a degree

You now know the requirements to become a data analyst, but if you’re taking the no-university-degree road to get there, there are a few things you need to do first:

Self-study

There are plenty of online courses and bootcamps that can equip you with the technical skills you need to get on this career path. Find the one that suits you best and start studying. And, if you can’t allocate any budget to a course right now, remember that YouTube is free and has knowledge to give.

Test your skills with projects

Once you have gathered some technical skills, it’s time to put them to the test. You can either reach for publicly available datasets and run your own analysis of them, or come up with a specific topic that interests you, find how to approach it with data, and analyze it. For example, you could analyze how well the next season of your favorite show is going to perform based on past data.

Create a portfolio and share it

Collect all the projects you have done and build a portfolio that you can easily share with prospective employers. Joining GitHub is also a great opportunity to showcase the code you have written, even if it was just something you did during a course. The idea is to prove that you can do it.

Data analyst checklist

So, should you become a data analyst?

Choosing a new career is no easy task, but if you’re still debating whether or not you should become a data analyst, then hearing from data analysts themselves might encourage your decision to become one as well.

We talked to Hugo Cheyne (former Data Analyst and current Product Lead at Y42) and Elena Alfonsi (Customer Success Data Analyst at Y42) about what it's like to be a data analyst and how they became one.

Hugo explains that he got into data analytics by chance. He had always loved numbers and strategy and tried to approach games like Yu-Gi-Oh or Pokemon with analytical thinking even from a young age, trying to figure out how to perfect his deck or his team. So, when he found himself in an interview for a role at a company he really wanted to join and was asked if he could do data analysis, he immediately blurted out an enthusiastic “yes!” before realizing that he didn’t even know how to write SQL. But he got the job, and the rest is history.

For Hugo:

"Being a data analyst is a great fit if you're good with numbers and curious about how you can use those numbers to improve the world around you. You have to spend a lot of time asking people the right questions, and then a lot of time getting the right answers to those questions.”

As for Elena, she came into the world of data from a business background. She always aimed to ​​adopt a data-assisted decision-making process and, in her work, had to constantly carry out analytical tasks. But for Elena, jumping straight into the deep end is what brought her to her career of choice today:

“I approached data-analysis as a self-learner and gained a basic understanding of it, but still mainly worked with the typical business tools like Google Sheets. When the company I was working for decided to use Y42, I had the opportunity to build a solid BI architecture to drive decisions and, by working with the tool, I quickly developed new skills.”

Hearing that being a data analyst works for others is great, but what matters most is for you to understand whether or not this job is a good fit for you. So we’ll leave you with some questions to ponder that might help you find more clarity:

  • Do you enjoy working with numbers?

  • Are statistical tasks something you excel at?

  • Do you consider yourself to be a curious person that likes to ask questions and dig deeper for the root cause of things?

  • Would you say you’re a pragmatic and logical person?

  • Do you have a knack for problem-solving?

  • Do you enjoy performing analytical work and thinking critically about it?

If you answered yes to more than one question, and, like Hugo or Elena, find yourself performing more and more analytical tasks without any SQL or Python knowledge, then implementing an all-in-one data analytics tool with a drag-and-drop interface like Y42 could help you get started in your journey towards becoming a fully-fledged data analyst yourself.

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