For many, getting a job as a data analyst is their first foray into data science, and it’s one of the fastest-growing jobs in the industry.
Around 2.5 quintillion bytes of data are generated every day, and data analysts’ chief responsibility is analyzing this data, making sense of it, and ultimately, facilitating data-driven decisions.
There are numerous career opportunities for data analysts today. They help organizations determine everything from where to place ads to garner the most impressions to deciding which products to sell in which markets. Consequently, analysts have the ability to make a big impact with their work, while also gaining specialized skills.
Here, we’ll discuss the data analyst career path: what data analysts do, how to prepare for your first data analyst job, and what long-term career options are available to you.
A data analyst probes an organization’s data by identifying trends, making forecasts, and extracting information to help stakeholders understand the organization’s performance and its external environment.
Data analysts work with different types of structured data—unlike data scientists, who handle unstructured data—such as:
Another difference: Data scientists build algorithms and machine learning models that enable organizations to collect, interpret, and act upon their data in meaningful ways.
Analysts use a range of tools and skills, including computer programming, data visualization, mathematics, and statistics. The data analyst’s role is fourfold:
For example, a data analyst might help an organization determine which customers are at the highest risk of churn and suggest personalized offers. Or an analyst might analyze the risks and returns of entering a new market.
Analysts are also responsible for identifying new sources of data and methods to improve data collection, analysis, and reporting. Data analysts synthesize their findings into reports, dashboards, and data visualizations and present them to upper management.
According to the US Bureau of Labor Statistics, jobs for operations and research analysts (which includes data analysts) will grow by 25% over the next decade. The demand for these professionals is highest in industries such as IT, healthcare, finance, insurance, and professional services.
However, organizations are struggling to find talent. NewVantage Partners recently reported that 98.6 percent of executives indicate that their organization aspires to a data-driven culture, while only 32.4 percent report having success.
Data analysts work across a range of industries as generalists or in specialized roles. Some specializations require domain knowledge, such as web analytics, market research, and operations analytics.
Landing your first data analytics role means showing hiring managers that you possess not only the right technical skillset, but that you understand how to apply analytical techniques to solve tough business problems.
The right education, working on projects independently or through internships, and knowing what to expect during the interview process are key to landing your first analyst job.
Must-have skills include a deep understanding of math and statistics, as well as business sense, some SQL coding, and the ability to make predictions based on data trends.
You also need to be proficient in industry-standard tools like:
To become an entry-level data analyst, you need an educational background in statistics, computer science, or IT. Four-year degree programs generally focus on the theoretical background rather than practical skills but may provide a more well-rounded education.
Some options include:
Data Analytics Degree Programs
A limited number of universities have begun to offer a bachelor’s in data analytics, but if your institution doesn’t offer one, you may have to earn a degree in a tangential field where you’ll learn some, but not all, of the skills required to become a professional data analyst.
If you pursue a CS degree, take computer science classes that emphasize database management and project management.
Bootcamps, on the other hand, offer a pared-down curriculum emphasizing job readiness. Ranging from a few weeks to several months, bootcamps provide the opportunity to create client-facing projects and emerge with a portfolio of work to show potential employers.
Some even offer mentorship and career coaching services to expedite job placements. Bootcamps tend to have their finger on the latest industry-standard tools, so the curriculum may be more up-to-date than a traditional four-year degree program.
Bottom line, hiring managers are looking for candidates who can demonstrate mastery of the necessary skills, and nontraditional education is becoming increasingly acceptable.
Beyond your educational background, there are other ways you can enhance your resume and increase your chances of landing a data analyst interview:
Projects help demonstrate that you have the skills needed to succeed on the job, and show you are a self-starter and that you can execute the entire data analytics process with minimal supervision.
Choose projects that entail various stages of data analysis:
As you build your portfolio, showcase your projects and describe your thought process every step of the way. Hiring managers want to understand how you think and solve problems.
You should showcase:
Internships are another great way to amass portfolio projects. Experience working with a real-world client and communicating your findings shows you are not only schooled in data analysis techniques, but that you have the soft skills needed to push your findings through and create real change.
Informational interviews remain one of the surest ways to build contacts in an industry you’d like to break into. Remember, you’re not asking for a job. The point of an informational interview is to show a genuine interest in someone’s unique career path and expertise.
With interviews and networking opportunities, think about what technical and soft skills you can emphasize or acquire that will make you more attractive to employers including:
As you build your resume, it’s important to prevent burn-out. See some tips on that in our guide: What’s the Work-Life Balance Like in Data Science?
Data analyst interviews typically include a broad range of behavioral and technical questions during the interview process, which is fairly standardized.. For most large tech companies, you can expect:
Data analysts are highly effective as individual contributors (ICs), and many are happy to climb the data analyst career ladder, from junior analyst, to senior analyst.
Beyond these roles, you may wish for more responsibility down the line. Typically, data analysts have a choice of four routes for career progression:
The managerial path looks like this:
Many senior analysts are happy to stay in that position, but an option is to progress to data manager or even chief data officer (if your organization has one). To attain a managerial role, you’ll need to build your leadership and project management skills alongside your data skills.
Some organizations require a master’s degree in data analytics or business administration for management positions. Bear in mind that data managers are mostly found within larger organizations, whereas smaller businesses may not have a large enough data team to warrant a managerial position.
Data managers may oversee all types of data professionals, such as data scientists, data engineers, business analysts, database administrators, and statisticians, so it may be helpful to have experience in one or more of these domain areas.
If people management doesn’t excite you, you can move laterally to a different industry, such as becoming a financial analyst, business intelligence analyst or even transferring your skills to a career in data journalism.
Some roles allow you to build domain expertise in a specific business function or industry. For example, risk analysts and financial analysts work in the financial sector helping clients make investment decisions, whereas operations analysts can work in virtually any industry. They specialize in business process improvement.
An analytics consultant is a data expert who works for clients in various industries, either as a freelance contractor or an employee of a consulting firm.
You’ll need a strong track record as a data analyst in order to become a consultant. Companies may hire consultants to solve a specific business problem, e.g. high customer churn, or to transform the company’s existing analytics program.
Many data scientists start as data analysts, then transition after learning advanced mathematics, programming and machine learning.
While data analysts make business recommendations based on data insights, data scientists build algorithms and machine learning models to implement those recommendations, which is what makes these two roles complementary. For example, say a data analyst finds that customers who purchase item X tend to buy item Y. A data scientist could build a recommendation algorithm that surfaces item Y for every customer who purchases item X.
Here’s what you’ll need to learn to become a data scientist:
Salaries for data analyst roles vary greatly by location and industry. For example, analysts in San Francisco, New York and Boston tend to command average salaries of $150,000+, while industries like IT, management, finance and insurance also equate to a pay bump.
Here’s a look at average salaries for the U.S.: