How to Become a Data Analyst: Your Ultimate Guide in 2022 (2022)

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All businesses rely on data to make decisions. For example, a coffee shop might review sales from different periods to dictate holiday hours of operation. Similarly, a software company might compare labor costs with profits to decide on staffing for the upcoming year.

No matter the industry, data analysis and analytics are vital components of strategic planning. If you enjoy using data (or clues, evidence, or information) to solve problems and make informed predictions and decisions, you might enjoy a career as a data analyst. And the truth is, becoming a data analyst doesn’t require the same schooling as many other tech careers. Data analyst requirements don’t even always include direct certification, though it's certainly helpful.

This guide will teach you about the data analyst role, and how to become a data analyst with or without a degree in 2022.

Before we get into the steps on how to be a data analyst, let’s look at a data analyst’s specific job duties.

How to Become a Data Analyst: Your Ultimate Guide in 2022 (1)

At-a-Glance: How To Become a Data Analyst

If you’re wondering how to become a data analyst, these steps can help you along the way:

  1. Start your education in the fundamentals of data analytics and any required skills and knowledge.
  2. Work on your technical skills.
  3. Start gathering experience by working on real-data projects.
  4. Start developing your portfolio over time.
  5. Learn how to visualize and present your findings (and practice!).
  6. Start looking for work, even if it’s an entry-level data analytics position.

And finally, you can also look into earning an advanced degree or a professional certification, as both can help to advance your budding career.

What is Data Analytics?

Data analytics, or data analysis, involves analyzing or scrutinizing data to draw meaningful and actionable insights. You can draw these insights from just about any data set, including market trends, market research, various statistics, and financial history.

Data analysis is kind of like a puzzle, where data (puzzle pieces) are put together to show you the big picture in the end. However, data analysis is slightly more complicated than that. Data analytics involves cleaning data, analyzing it, and then visualizing it into more understandable information.

What is a Data Analyst?

A data analyst is a professional responsible for gathering, cleaning, and analyzing data. Analysts also use data visualization tools to present this information in a more understandable way. Data analysts usually have strong backgrounds in statistics, mathematics, and even computer science.

The demand for data analysts only continues to grow, thanks to businesses shifting toward data-driven strategies.

How Long Does It Take To Become a Data Analyst?

There’s no single answer. The truth is, the time you take can be different from how others. Consider these key factors:

  • Your starting point: Are you starting completely from scratch, or do you have some relevant skills and knowledge to help you pick things up faster? If you’re starting completely from scratch, it may naturally take you much longer to become confident enough in your skills to look for data analyst jobs. Starting from scratch also means you’ll probably have to spend more time learning certain things that others already know, further slowing down your progress.
  • Your background: Data analytics requires a knowledge and understanding of mathematics and statistics. If you do not have a background in either, you can still become a data analyst! However, you may take a little longer to pick up concepts, techniques, and lessons that others may pick up more easily.
  • Your choice of data analyst education: You have a few options available as you start learning the skills you need for a data analyst career. You can learn entirely on your own through self-study or by enrolling in online courses and bootcamps. Alternatively, you can attend college and earn a degree.

The factors above impact how long it will take you to become a data analyst. It could take as short as a few months, especially if you already have a relevant background and starting point. However, if you’re starting from scratch and going to college to earn a degree in this field, it could take you a few years.

What Do Data Analysts Do?

Companies collect data on every business activity and expense, be it operational costs, sales data, inventory and supply data, and more. But once they have that data, they need to transform it into information that can inform decisions.

Data analysts turn data from customers, products, performance, and costs into valuable insights that guide strategic decision-making. For example, they can help businesses plan for product development and predict which audiences they’ll find the most success with.

Alt: Data analyst role chart

Roles and Responsibilities of a Data Analyst

Here’s a breakdown of a data analyst’s duties:

  1. Collect Data: Analysts collect data from surveys, web analytics, and buying datasets. However, analysts don’t have to collect all of the data themselves — sometimes, businesses have it available for them.
  2. Identify Data Errors: Analysts correct any data errors, including duplicates. They also identify outliers and anomalies. Furthermore, analysts ensure consistency to maintain accuracy in interpretations, using tools like Microsoft Excel, business intelligence tools, or programming languages.
  3. Create Models: Data analysts are also often responsible for data modeling, where they design database structures and determine how to store and compare data categories.
  4. Interpretation: Data analysts then find trends and patterns in processed data to transform it into valuable insights.
  5. Contextualize: Analysts will then find connections with interpreted data in the greater context of a business, including the overall market and industry trends on local and international levels.
  6. Presentation: Last but not the least, analysts must then communicate data findings and interpretations to stakeholders, clients, management, and colleagues using graphs, charts, and other tools.

Data Analyst Qualifications

Many data analysts pursue professional certification to gain knowledge of the role and find job opportunities. This might look like a four-year bachelor's degree in math, science, computer science, business, or statistics. Or, it might look like a professional certificate program that allows more flexibility in study. Both offer perks like networking and a more structured education.

But, professional certification and degrees aren’t necessary to become a data analyst.

Data Analyst Skills

Data analysts have easily transferable skills. Meaning that to be a successful data analyst, you don’t necessarily need a degree. However, the following qualifications and knowledge will certainly help you on your path to becoming a data analyst:

  1. Programming Languages: Data analysts use statistical programming languages to present, analyze, and interpret data. Usually, data analyst job descriptions will identify the programming languages they prefer candidates to have. But, you’re safe if you have Python or R knowledge, as they’re the most common.
  2. Data Tools: Data analysts use various software and tools to do their job. Google Sheets and Excel are common in all industries, while SQL is a more advanced tool for larger amounts of data.
  3. Visualization and Presentation Skills: Data analysts must make complex data digestible. Good communication skills are essential for the role, and visualization and presentation tools like Tableau and Jupyter Notebook also help.
  4. Statistics: Most data analysts have an extensive understanding of math and statistics, which help identify data errors and interpret data more effectively.

Being a data analyst means learning, knowing, and using tools at your disposal to find actionable insights from massive amounts of data. These tools make a data analyst’s life much easier and more efficient.

But what tools are these, exactly? We’ve made a list of a few below, including but not limited to:

  1. Business Intelligence Tools: Business intelligence tools are some of the most popular data analysis tools. As the name might suggest, BI tools are used most often in business analytics for analyzing and monitoring data and extracting key insights. These tools also often help analysts report their important findings. BI tools come in various styles and types, so there is something for almost any skill level.
  2. Programming Languages: Thanks to the continued evolution of data analytics and data science, the days of using only spreadsheet programs for data handling are long gone. Many data analysts learn various programming languages used in some data analysis and data visualization tools. Additionally, it may be necessary to know certain programming languages to collaborate with data scientists, engineers, developers, and other team members. Some of the more popular languages are R, Python and its frameworks, and SQL.
  3. Spreadsheet Programs: Spreadsheet programs, with the most popular being the industry-standard Microsoft Excel, are some of the oldest traditional tools used in data analytics. The best thing about these programs is that they are much more accessible to people of all skill levels, whether they are data analysts or not. Excel, for example, doesn’t require months or years of training and even comes with a few built-in data visualization tools like charts and graphs.
  4. Data Cleansing Tools: Data cleansing tools are responsible for helping data analysts prepare massive amounts of data for analysis while minimizing or even eliminating errors, inconsistencies, and data duplication. Tools like these help data analysts perform more accurately and efficiently.
  5. Statistical Analysis Tools: These tools use statistical analysis techniques for exploring, manipulating, and analyzing data for the purposes of extracting and generating actionable insights. Statistical analysis tools tend to be a bit more technical, requiring a bit more learning.
  6. SQL Consoles: These consoles allow users to write and execute SQL statements. SQL is a programming language used to query and manage data inside relational databases. It’s great at handling structured data and is often used in business situations and other data scenarios.
  7. Industry-Specific Analytics Tools: Some data analytics tools are industry specific, meaning they’re only available within particular industries. If you’re looking for a data analyst job in a specific industry, consider researching industry-specific tools you need to learn to get a head start.
  8. Data Modeling Tools: Data modeling tools help analysts create models to design systems and structure databases. These tools often look like wireframing tools because they use similar shapes, arrows, and graphics to depict how data will flow and connect.
  9. Data Visualization Tools or Platforms: Data visualization tools create visual depictions or representations of raw data. They can help turn data into information that’s easily understood, digested, and remembered — even by laypersons.
  10. Automation Tools: As we’ve discussed, data analytics tools make analysts’ lives easier and more efficient. Automation tools are similar — they help analysts perform data analysis tasks while maintaining little to no human input or interaction in the process. Automation can free analysts up to do other tasks in the meantime, further increasing productivity.

These are just some of the many tools used in data analysis. There’s no doubt that in the near future, there will be more things for analysts to learn and use. And as the tools get more complex, it’s easy to understand the growing emphasis on computer science for data analyst roles.

How to Be a Data Analyst: A Step-by-Step Guide

We’ve discussed the requirements for data analyst jobs in great detail. Now, let’s take a look at the steps to become a data analyst — starting from the beginning!

1. Start Your Education in the Fundamentals of Data Analytics

If you’re completely new to data analytics, you can start by learning the fundamentals. Foundational knowledge is vital, as having this education can help you more easily pick up new concepts, tools, and methods that may develop over time. A foundational education can also give you a broader look at data analytics, helping you determine whether this field is right for you.

A bachelor’s degree might have been required for even entry-level positions back in the day. Most positions today still require bachelor’s degrees. But as technology advances, more companies are starting to remove this requirement, which means you can find work no matter your education — as long as you can prove you have the knowledge and skills needed.

2. Work on Your Technical Skills

Data analysis requires various skills depending on the job and the industry you enter. However, most jobs will require a specific set of technical skills, which you should start building and working on as soon as you can. Regardless of how you’re learning about data analytics, there are vital skills you cannot skip.

You can start improving your knowledge and practicing your skills in:

  • Statistics
  • Data cleaning and preparation
  • Python and its data analytics frameworks, such as Matplotlib, Scikit-learn, and more
  • R (programming language)
  • SQL (programming language)

It may also be beneficial to start learning some of the most popular data analytics and visualization tools.

3. Start Gathering Experience by Working on Real-Data Projects

One of the best ways to learn something and really understand it is through experience. Working on real-data projects can help you get some hands-on experience while also teaching you how to work with data in real-world situations. You can join projects or design your own by accessing some of the public data sets available for free and creating your project around them.

One resource that offers real data sets is NOOA (National Oceanic and Atmospheric Administration). Even NASA has open data sets available.

If you aren’t completely sure what you’re doing or how to start working on a project, you can look for guided projects (often available in online courses).

4. Develop Your Portfolio Over Time

One of the requirements for data analyst roles is a portfolio. More often than not, companies will ask you for yours when you apply, because portfolios help to show your understanding of the field while displaying your skills. A strong portfolio can give you an edge over other applicants.

Start gathering your best work as soon as possible. It’ll give you more to choose from when you finally put your portfolio together. You can use services like GitHub to house your projects.

But it’s not enough to just put together a portfolio — you should also choose projects that demonstrate essential data analytics skills, such as scraping data from various sources or cleaning and normalizing raw data. You should also demonstrate your ability visualize data into accessible, engaging formats like colorful graphs and charts.

You can even include group projects you worked on, as they may help to demonstrate your capability for collaboration.

5. Learn How to Visualize and Present Your Findings (and Practice!)

Data analytics isn’t quite complete without visualization. Data visualization allows you to present your findings clearly and understandably, even to those less technically knowledgeable. There is a wide variety of data visualization tools that you can learn, including plenty of free options like Tableau Public.

It’s also vital to learn how to present your findings. If you’re not great at public speaking or handling yourself at meetings, a little practice might benefit you.

6. Start Looking for Work!

Once you’ve built up some experience and a strong portfolio, the next step on your path is to start looking for work. You may need to start with some entry-level positions until you get to a point where you can start advancing and looking for more senior positions. You can also start preparing for job interviews to give yourself a better chance of success. These data analyst interview questions can help.

Advance Your Career: Continuing Education and Certifications

Finally, once you’ve secured a job, you can continue your education. Learn programming languages and frameworks if you don’t know them already. You can also learn more about data analytics tools, seek professional certifications, or consider an advanced degree.

More skills, certifications, and advanced degrees are helpful to advancing your career. Additionally, they may help you make an eventual shift into a career as a data scientist.

How to Become a Data Analyst with No Experience or Degree

Not everyone can afford a degree program or professional certification. If you lack the experience and skills needed to become a data analyst but can’t afford the time or money needed for schooling, here’s how you can become a data analyst. Follow these steps to gain the necessary knowledge to become a data analyst.

  1. Start with Self-Study

The internet has a wealth of knowledge you can access for free. If you have the discipline and motivation, consider learning data analytics skills on your own. Some skills you’ll want to study and learn include Python, R, data visualization, Statistics, and SQL. You might consider starting with a Python tutorial!

  1. Try Out Data Analytics Projects

It’s time to apply your knowledge with hands-on projects. After gathering some technical skills, you might consider one of the following 10 Best Online Data Analytics Courses or a data analyst certification. Make sure your chosen course or certification allows you to design data projects and present your findings.

Another way to work on projects is to seek out free datasets, such as from public repositories, to make your own interpretations to practice with.

  1. Create a Portfolio

Once you have the experience of a few projects under your belt, you can put them together into a portfolio. Your portfolio demonstrates abilities that employers want to see before hiring you for a data analyst role.

GitHub is a great place to showcase your work. You can check out other professional portfolios for ideas, broaden your network, and even find job opportunities.

  1. Apply for Internships and Jobs

Tidy up your resume and polish your portfolio to apply for entry-level data analyst positions. You might consider reaching out to the network you’ve built through your projects, courses, and self-study. Job boards like Indeed are also great places to find potential work opportunities.

The time it takes to become a data analyst is up to you. It can take anywhere from a few months to a few years, depending on your level of commitment, hours per week, educational path, and networking capabilities.

Job Outlook after Becoming a Data Analyst

Are data analysts in demand? Definitely — data analysts share a promising job outlook. The market is expected to grow by at least 20% in the next ten years. For example, market research analysts have a predicted job growth of 22%, 25% for operations research analysts, and 33% for statisticians. Even at 22%, this growth rate is several times the national average.

Alt: Data analyst job outlook graph

Just look at this Google Trends graph for the term “data analyst” going back to 2004. As you can see, there has been a steady growth starting from around 2011. The big jump you see near the end results from Google’s improved data gathering techniques. Nevertheless, the interest is clear — and the demand for data analysts continues to grow.

However, getting your foot in the door as a data analyst is just the beginning. As more and more companies adopt data-driven strategies and invest in data professionals and big data projects, data analyst roles continue to grow more complex. There is a constant stream of new strategies, tools, and advancements in the field, which means you’lll have to continue learning new things during your career.

As a data analyst, your primary responsibilities will include actually working with massive amounts of data to retrieve some actionable insights. However, as technology evolves and tools get more complex, so does the analyst role.

More and more analysts find themselves having to learn how to use predictive analytics and data modeling to extract insights and data visualization to display them.

Although you may enter a position as a data analyst, your responsibilities may start to shift over time. As you continue learning new tools and techniques, you might slowly become more of a data scientist.

What Salaries Can You Expect as a Data Analyst?

According to Glassdoor, salaries for Data Analysts sat at an average of around $71,884 per year in the United States.

And according to the United States Bureau of Labor Statistics, data analyst salaries range anywhere between $65,000 - $86,000 in the US. The salary range covers various types of data analysts, and certain types can expect an even more positive job outlook.

Recommended Data Analyst Courses

If you’ve made it this far and you’re pretty sure you want to become a data analyst, one of the best places to start is by signing up for an online course. Online courses are often more inexpensive than the alternatives while also providing plenty more flexibility to help you fit your learning around your existing schedule.

Hackr.io has compiled a list of the best data analytics courses to help you get started.

There are many great courses to choose from, each at varying skill levels and starting points. Here are some of our top recommendations:

Conclusion

Wondering how to get into data analytics? Take your pick.

The answer to how to become a data analyst isn’t the same for everyone. You might go the degree route for more structure or opt for self-study and networking to save on costs and time commitment.

Of course, you can also take the direction of online courses, which are more structured than full self-study. Regardless, we hope this guide has helped you figure out your best path to becoming a data analyst!

Looking to expand your data analytics knowledge and qualifications?

Check Out Data Analyst Certifications.

Frequently Asked Questions

1. Can I Become a Data Analyst in 3 Months?

The time it takes to become a data analyst depends on your starting point, time commitment each week, and your chosen educational path. You can become a data analyst in three months, but if you’re starting from scratch and don’t have an existing background of relevant skills, it may take you (much) longer.

2. Is it Hard to Become a Data Analyst?

Not if you have the background in mathematics and computer science data analyst jobs require. If you already have a solid background and some relevant experience, switching can be relatively easy. On the other hand, learning from scratch can be an uphill climb.

3. Does Becoming a Data Analyst Require Coding?

A career as a data analyst may require some coding and programming knowledge. Some data analysts are experienced and proficient programmers, while others may do their work with analytics software or spreadsheet software like Microsoft Excel. It’s best to learn programming to ensure a better future career, but as of now, there may still be job postings that do not require coding experience.

4. Do Data Analysts Work from Home?

Data analysts often work from home, especially since remote work is becoming more popular.

5. What Should I Learn to Become a Data Analyst?

Although data analysts often benefit from having a STEM or computer-science bachelor’s degree, a degree isn’t absolutely necessary.

Even a course can be enough, especially as many companies will look for skilled employees more than credentials on paper. You should also learn some of the best and most popular business intelligence software available, alongside some programming languages and other data analysis and visualization tools.

6. Are Data Analysts In Demand?

Absolutely — more companies are taking a data-driven approach to business. Just look at this 2017 Forbes article discussing how IBM predicted a demand increase for data professionals. Many of those predictions came true, and the demand only continues to grow.

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