When you consider the massive amount of data produced globally these days (90 zettabytes in 2019 and 2020 alone, according to market research outfit IDC), it’s easy to understand why the US Bureau of Labor Statistics (BLS) forecasts 23% growth in data analytics jobs over the next decade. What’s more difficult is figuring out how to capitalize on this growth and break into data analytics. For someone coming from another field like information technology (IT) or healthcare — or just someone in high school who’s interested in working with data — the path to landing a data analyst job can seem anything but sure. How do you gain the skills and expertise needed to analyze data, and, once you have the skills and expertise, how do you convince a hiring manager that you’re the right person for the job?
With this step-by-step guide, we’re hoping to make the path to a data analyst career a little clearer. In it, we’ll cover the skills required to work as a data analyst, the educational options that can get you there, ways to gain experience while on the job hunt, and how to improve your chances of getting an interview and landing an offer.
What does a data analyst do?
To begin, let’s dive deeper into what exactly a data analyst does. While exact responsibilities can differ depending on industry, seniority, and the type and size of the company where an analyst is employed, in most cases a data analyst is responsible for collecting and preparing data for analysis and then analyzing it, interpreting the results, and communicating findings to relevant stakeholders.
Within data analytics, this analysis usually serves one of four goals. Descriptive analytics leverages data analysis to better understand what has happened in the past. Diagnostic data analytics leverages data analysis to better understand why past events have occurred. Predictive data analytics leverages data analysis to determine what is likely to happen in the future. Finally, prescriptive data analytics leverages data analysis to recommend, determine, or even automate future actions.
In addition to a booming job market, many are attracted to data analytics because positions in the field offer above-average salaries. While the average American made $58,260 in 2021, data analysts make $81,719 on average.
How to become a data analyst
Step One: Get educated.
The first step towards landing a data analyst position is education. To succeed in data analytics, analysts need training in an interdisciplinary set of skills, including:
Computer science skills, such as fluency in programming languages such as SQL, R, and Python
Statistical analysis skills, including regression, optimization, factor analysis, cohort analysis, cluster analysis, and time-series analysis
Data management skills, including data cleansing, data mining, and data visualization
Advanced skills, such as machine learning modeling for big data sets
Business and industry expertise
Though a data analyst’s skill-set can be intimidating to newcomers, the good news is that there are educational options that can help individuals of any background get up and running. Below, we’ll introduce the most common ones.
Data Analytics Bachelor’s Programs
Aspiring data analysts with the time and money to pursue a four-year degree
The curriculum for a data analytics bachelor’s degree will cover all the skills noted above, though instruction for advanced areas may be fairly minimal.
Data analytics bachelor’s degrees offer a time-tested path to a data analyst job for those without existing experience.
Tuition for bachelor’s degree programs varies widely, especially between tuition at elite private universities and in-state tuition at public universities.
Generally, undergraduates can expect to spend between $10,000 and $60,000 on tuition per year before living expenses.
To learn more about data analytics bachelor’s degrees, check out our guide to the best data analytics undergraduate colleges and universities.
Data Analytics Bootcamps
Those looking to get into data analytics who want a faster, less expensive, more flexible alternative to traditional degrees
Compared to a bachelor’s program, students in a data analytics bootcamp can expect to cover much of the same material, though in much less time and thus in much less detail.
Often, fundamental skills in mathematics and/or programming are taught through self-directed preliminary courses.
Bootcamps generally offer one-to-one mentoring or other career services, plus independent projects that students can use to build their data analytics portfolios.
As we mentioned above, bootcamps are shorter, cheaper, and more flexible than traditional degree programs, while still providing a feasible path to a career as a data analyst.
The average bootcamp cost $11,727 in 2020.
Bootcamps can offer installment plans, financing, or income-share plans where you pay back a fraction of your income once you get a job.
Other bootcamps offer money-back job-guarantees.
To learn more about bootcamps and find out which are our favorites, check out our data analytics bootcamp guide.
Data Analytics Certificates, Certificates, and Other Short Courses
Those looking to get into data analytics who already have some relevant skills and/or don’t want to commit to a bootcamp.
Data analytics certificate programs and other short courses either give a broad overview of data analytics without diving too much into the details or focus on only one aspect or skill, such as coding with Python or data visualization.
Certification programs offer students the chance to pass an exam proving their mastery of a skill.
Certificate programs and other short courses offer more targeted, flexible, and tailored instruction than bootcamps. For those with existing experience working with data, they can offer a more efficient option to become a viable candidate for a data analyst position..
Certificates and certifications can be displayed on LinkedIn or a resume to communicate your expertise to recruiters.
Certificates and short courses are typically less expensive than bootcamps, ranging from free to a couple thousand dollars.
Online learning providers like DataCamp instead offer training for a flat monthly fee.
To learn more about data analytics certificate programs, certifications, and other short courses, check out our guide.
Data Analytics and Data Science Apprenticeships
Data analytics and data science apprenticeships are available to individuals from a variety of backgrounds looking to transition into data-centric fields, from those holding GEDs and associate’s degrees with little formal experience to professionals working in a different capacity. If you’re interested in data science, we’ll discuss opportunities at the end of this guide. If you want to know more about the difference between data science and data analytics, check out our explainer.
Apprenticeships combine instruction in data analytics and data science basics with on-the-job training.
Students seek out apprenticeships for a low-risk, high ROI opportunity to study data analytics and data science, with the added benefit of gaining work experience through being embedded on a team at a real company.
Apprenticeships are unique in offering training for free, or even with a modest salary.
Head to our guide for the latest data analytics and data science apprenticeship opportunities.
Data Analytics Master’s Degree Programs
Individuals who already hold bachelor’s degrees and can show aptitude or existing ability in programming and applied mathematics.
Data analytics master’s curricula combine courses in programming and data management with instruction in statistical analysis and modeling.
Master’s-level study provides more advanced data analytics training and can unlock higher salaries than bachelor’s programs, certificate programs, or bootcamps.
Increasingly, there are full-time and part-time online options that offer students the flexibility to continue working or caring for a loved one while they work towards an advanced degree.
According to educationdata.org, the average master’s of science degree in the US costs $61,200, but the cost of the degree can range from $30,000 to $120,000 once fees are factored in.
Our guide to data analytics master’s programs has lots of useful information for those interested in graduate study in data science.
Step Two: Get experienced.
Are you experienced? It’s not just a question for Hendrix anymore: recruiters and hiring managers hiring for data analyst roles want job candidates to have real-world experience in data analytics, even for entry-level positions. How to get job-ready experience before you land a job? Here are several ways:
Internships can provide something that classroom instruction can’t: a view into operations at a real company. Many students pursuing bachelor’s or master’s degrees complete one or more internships during their summers off. Sometimes, degree programs will have local industry connections through which they can secure internship placements for their students. Internship openings are also often advertised online, either on company career pages or job boards like LinkedIn and Indeed.
Independent or Collaborative Projects
A good complement to a resume filled with educational achievements and real-world experiences is a portfolio of independent or collaborative projects that can give recruiters and hiring managers an idea of your work. Many even choose to build personal websites to show off their interesting approaches to analysis, new machine learning models, or beautiful data visualizations.
Many educational programs, whether a traditional degree or a bootcamp or short course, lead students through the process of designing and executing an independent project. These days, aspiring data analysts and data scientists are also collaborating with others from around the world on open-source projects like those offered by Spotify.
Freelancing and Pro Bono Work
Freelancing is another great way to build experience, but if you’re brand new to the field, it can be difficult to land your first gig. If this is the case, pro bono work through a social-good outfit like DataKind, Catchafire, or Statistics Without Borders offers a workaround that you can feel good about. While you’ll likely have to work for free, you can usually showcase any work product in your portfolio.
Join a Data Science Club or Organization
Most colleges and universities have student-led clubs for those interested in data analytics, as well as events such as hackathons. All of these can be a great way to meet people in the field and learn more about what’s out there.
Step Three: Get polished.
After getting some education and building your experience, the next step is to ensure you’re showcasing what you have to offer a company in the best way possible. In addition to a strong portfolio, you want to have a resume that can persuasively tell the story of where you’ve been and lay the groundwork for where you’re going.
But not just one resume: for each job you apply to, you’d be playing a specific role as part of a team. Accordingly, you’ll want to tweak your resume to play up those parts of your background that are most relevant. You should also practice telling your story in a way that emphasizes what you have to offer so that when you land an interview you’ll be ready to shine.
For more, check out our data analytics and data science resume and interview tips.
Step Four: Get busy.
If you’ve developed your skills and expertise and figured out how to demonstrate this to an interviewer, the next step is to land an interview. It’s many people’s first instinct to paper the world with their resume. There’s no doubt that you’ll have to apply widely, but for each job you apply to, you want to maximize your chance of getting an interview, so you should also leverage your professional network to develop some leads and potentially even get some referrals. Oftentimes, a person on the inside is all you need to make sure your resume gets read and you get an interview.
But landing an interview is just the start: you’ll likely have to pass several different interview rounds before you get a job offer. This will typically involve speaking to potential colleagues at varying levels of seniority. Often, you’ll also have to complete and present a take-home project so that they can assess your skills and better evaluate your fit.
Step Five: Profit!
While you might not get the first job you apply for, with enough diligent effort and patience, you’ll eventually succeed in getting an offer. Of course, your first data analyst role won’t be your last. For more on what to expect from a data analytics career path — promotions, educational requirements, and compensation — check out our guide on that.
In this guide, we’ve focused on how to become a data analyst: the skills you need, the educational opportunities out there, and how you can turn these into a job offer. But for someone with computer science, statistical analysis, and data management skills, there are other interesting options out there. To close, we’ll go over a few and suggest some ways you can learn more.
A business analyst’s day-to-day might closely resemble a data analyst’s — in fact, the titles can frequently be synonymous — but at large companies, a business analyst is usually required to possess a higher degree of business acumen. Often, their work will directly contribute to decisions about how to meet business goals such as increasing profits, decreasing costs, entering a new market, or agreeing to a merger or acquisition.
Business intelligence analyst
Another career with considerable overlap with a data analyst is business intelligence analyst. Like a business analyst, a business intelligence analyst requires a considerable amount of business acumen. While a business analyst focuses on analytics that drive business actions, however, a business intelligence analyst is more squarely focused on producing intelligence: reports, dashboards, and related processes and tools.
Like these other roles, a data scientist focuses on collecting, preparing, analyzing, and interpreting data, but usually they will also work on designing, developing, and deploying new processes for doing so. Demand for data scientists has grown in demand as the availability and utility of big data sets has grown, as well as the capabilities of machine learning. While data analysts might have some training in machine learning, data scientists will spend significant amounts of time with it, even writing their own machine learning algorithms and models. To develop these skills, aspiring data scientists usually need to take specific courses in machine learning, deep learning, and big data.
Understandably given their higher impact, the average data scientist salary in the US is higher: Salary.com puts it at $139,631. To learn more about educational and career possibilities in data science, check out the following articles: