How to Become a Data Analyst (Step-By-Step) in 2022
Since data is such an essential aspect of business intelligence, data analysts are very much in demand — and this trend is only increasing! The job is key for many types of projects, such as analyzing market trends or collecting data for political polls.
If you want to learn to become a data analyst, you’re in the right place. We’ve laid out 5 steps for you to start your journey.
Step 1: Know What’s Right for You
The data industry is divided into several disciplines. Each one, whether it’s data science or data analysis, for example, has its own specificities, but they do sometimes overlap. Additionally, there are occasions when professionals within the same field use a different programming language.
Although this sounds confusing, we’re here to break it down for you. After all, we want you to know which field you’re best suited for, so you don’t waste your time or effort.
Ensuring data analysis is for you
If you’ve started researching data, you’ll know that understanding the difference between a data analyst role and other data-related jobs can be a little bit confusing. We’ve got a few resources that can definitely help clarify things:
- Article: Data Engineer, Data Analyst, Data Scientist — What’s the Difference?
- Article: Business Analyst vs. Data Analyst: Which One is Right for You? (2022)
- Roundtable video: Understanding Different Data Roles
- Video: How To Start Your Career In Data: Find Your Role, Learn Skills, Build A Portfolio
In the end, if you’re more interested in a data scientist career, here are a few tips to help you become a data scientist. Alternatively, if a data analyst career seems to make more sense for you, continue reading — we’ve got you covered.
Choosing the right programming language: Python vs. R
Before picking the programming language you want to learn, familiarize yourself with the key skills for data analysts:
Python and R are two programming languages that data analysts use that produce the same end result, albeit in different ways. Where R is more functional, Python is more object-oriented. In the end, both programs demand about the same amount of work for each task, so there’s no significant time saved by learning one over the other. The resources below can help you better understand the differences between Python and R.
- R vs Python for Data Analysis — An Objective Comparison
- R vs Python | Which is Better for Data Analysis?
Step 2: Learn the Fundamentals
As a data analyst, you’ll be the go-to person for finding the hidden meaning behind the numbers. You’ll investigate why, for example, customers prefer one product over another. You might even discover which types of consumers buy certain products, connecting seemingly random data points to create a clear pattern of purchasing behavior.
Where to start
Here are a few places where you can learn to become a data analyst:
- Dataquest — Dataquest was created because there was no easy way to learn Python. In addition to offering paths consisting of all the courses you need to become a data analyst, Dataquest also offers basic Python courses that cover the fundamentals.
- Humble Bundle — if you’re not familiar with Humble Bundle, it’s an online video game storefront that sells bundles of video games, books, and software. The site always offers at least one Python or data-focused bundle. Available now, the Essential Knowledge Computers and Tech bundle from MIT Press includes 24 books about data science, deep learning, AI machine learning, and more.
- YouTube — there are a surprising number of data-focused YouTubers who work in the data industry and provide insightful knowledge for newcomers. Whether you want to learn more about a field or teach yourself by creating your own video curriculum, YouTube has no shortage of options.
Step 3: Build Projects
In my political science data course at University, theoretical class was the typical scenario where material was very technical, and a lot of it went over my head. In practice class, however, we used databases, which held polling information on everything from a person’s age to their political affiliation to their income — it was a lot more fun and much more effective.
Applying the skills you learn throughout a course with practice exercises and projects is the best way to get them to stick. Practice is a tried-and-true method of learning how to resolve complicated tasks and ensuring that you can handle them while on the job.
You can start one of the two data analyst paths on Dataquest that will challenge you with more than 20 projects, such as building a mobile app for lottery addiction, a spam filter with Naive Bayes, or even strategies to win at Jeopardy.
Find projects that interest you
While you’re learning to become a data analyst, you can do other projects in your spare time that interest you. I find that working on something for myself to feed my curiosity is a huge motivational tool.
Projects show that you have initiative, which is a virtue employers highly value. Moreover, it will help you determine how much you enjoy this field. If you find yourself working on personal data projects for “fun,” then you’re in the right place.
- 7 Free Resources To Download Datasets
- 55 Fun (and Unique) Python Project Ideas for Beginners in 2022
When it comes to working on personal projects, you can make them about anything that interests you. As long as you have the data, your imagination is the only limit.
Look for inspiration elsewhere
You can also find inspiration for projects from books or other media. For example, Super Graphic, by Tim Leong, is a great example of using data for something fun. This book focuses on superhero comics, and it’s made of infographics that explain random data points about superheroes.
One Gantt chart in the book breaks down how long different superheroes stayed dead before (miraculously) coming back to life. Another horizontal graph visualizes whether Superman or the Flash is the fastest man alive — spoiler: it’s the Flash. Super Graphic Star Wars is the same, except with charts about the happenings in a galaxy far, far away.
Earlier in 2022, it seemed like everyone was playing Wordle, a game that gives you 6 tries to solve the 5-letter word of the day. If you played it, you likely had a proprietary method to solve the word, and everyone else did, too. But one data reporter from The Why Axis made a data-based chart that found the five most common letters used in Wordle: E, R, A, O, and T. This fun and clever chart took away some of the mystery of playing the game, but it did help many with their daily Wordle game.
- Super Graphic and Super Graphic Star Wars — two books that use data in a fun way that anyone can enjoy (not just data analysts).
- Dataviz books Everyone should read — a comprehensive list of books you should read if you want to learn how to make eye-catching infographics.
- 12 Best Infographic Tools for 2022 (Full Comparison Guide) — tools to streamline the making of infographics.
- A simple, spoiler-free analysis of letter frequency in Wordle — learn more about how a reporter learned the frequency of letter use in Wordle.
- Universal Paper Clips — this simple game is barely a game compared to modern standards, but it requires an analytical mind to beat it. As a paper clip manufacturer, you have to take into account the price of each paper clip, public demand, the cost of materials, and marketing. It’s very difficult to put down.
Step 4: Creating a Strong Portfolio
Rather than imagining what your job will look like and what’ll you face as a data analyst, a project-based course can give you that insight before you even set foot into your future workplace. Your portfolio will show, not just tell, future employers what you’re capable of doing. A portfolio is especially important for an entry-level data analyst, since you likely won’t have the work experience.
Here are a few resources that will help you build your portfolio:
- Video: How To Build A Data Project Portfolio And Stand Out To Employers (With Examples)
- Video: 3 Real-World Data Projects for Your Portfolio
- Article: How to Build a Killer Data Science Portfolio
When competing with other data analysts for the same job, a strong portfolio can be the difference between an email with an offer letter and one without. No one knows this better than Miguel Couto, a data analyst at a big online streaming company, who landed a job after taking the Data Analyst Path.
“The projects on Dataquest allowed me to start building a GitHub portfolio,” Miguel said. “Dataquest actually makes you think and apply your skills.” While not every project in the course will make it into your portfolio, you’ll have the tools to make your own projects outside of the course.
These personal projects are great for any portfolio, showing you have initiative and the experience to back it up. For Miguel, GitHub was the perfect place to show his work, since he could show off his projects and the documentation and code behind each project.
Step 5: Get out There
Hosting your portfolio on a site is a great start, but that can lack personality. While employers and peers will see your work, they might miss out on what makes you different. You might write a blog about data analytics, for example.
Writing isn’t the only medium where you can express yourself professionally. While social media platforms seem like the least likely place to discuss data analytics, you’d be surprised by what you’ll find. One TikTok user began creating Microsoft Excel content on the platform, which she turned into a career by teaching people how to use spreadsheet software.
Reddit is another great example. There are communities for data science and data analysis, as well as other data-centric fields. This is a great place to post an accomplishment, ask a question, or meet other people in the same boat. Discord, a messaging platform similar to Slack, is another place where you can meet people and talk about everything data analytics.
The thing about interacting in a niche community is that there is a high chance you could meet somebody who could help your career. You might find out about a job or meet someone who could introduce you to their employer. But on a personal level, it’s just a great way to make friends.
Taking the First Step to Becoming a Data Analyst
We tend to psych ourselves out by thinking something is too difficult, too expensive, or simply too time-consuming. But you’ve already taken that first step. Now that you have the basic outline for how to become a data analyst, it turns out it’s neither too hard, too expensive, nor too time-consuming. And you don’t even need a certification . . .
A former Dataquest learner now working for Fractal was “impressed” with how the Data Analyst path taught complicated subjects like math. “Only the most important concepts were taught, and they were broken down so well that I think anyone who has basic arithmetic skills could start learning data analysis and statistics.”
The Dataquest Data Analyst Path is affordable, challenging, and flexible. Whether you’re holding down a full-time job or figuring out your next steps, the path is yours to walk at your own pace. You can finish it in as few as five months, with a time commitment of 10 hours per week, or you can spread it out over a year. In the end, it’s finishing the path (not how long it took!) that matters most.