Popular Career Paths of a Data Analyst
Do you have mathematical acuity, technical knowledge, and (maybe too much) curiosity? If so, a data analyst career path might be the perfect professional route for you.
Data analysts are sleuth-minded problem-solvers. Armed with an organized toolbox of statistical, analytical, and programming skills, these information-oriented professionals gather. And analyze large quantities of data in the hopes of identifying trends or patterns that indicate problems or opportunities.
Once the data has been wrangled, cleaned, and organized, the data-driven insights they reveal are used to provide solutions to business challenges. Create market forecasts, and help company leaders make better business decisions. Though the details of their individual roles vary between jobs and industries, all data analysts tend to be well-versed in databases, reporting technologies, statistics, and, of course, data analysis.
For those who enjoy solving puzzles and teasing insights from overwhelmingly large data sets, the thrill of the job itself might be reason enough to choose a data analyst career path. But the career prospects make that path even more worthy of exploration.
Aspiring data analysts have reason to be optimistic in their job search. According to statistics published by the Bureau of Labor and Statistics (BLS), the employment of data analysts is on track to grow 26 percent between 2018 and 2028.
This number is more than 20 percent higher than the average 5 percent growth rate anticipated over the same period for all other occupations. Moreover, researchers for BLS write, “As technology advances and companies seek efficiency and cost savings, demand for operations research analysis should continue to grow.”
They aren’t wrong. Big Data and the professionals who can successfully analyze it have become increasingly important to businesses in all industries over the past several years.
By harnessing the power of analytics, companies can uncover previously unseen patterns in the market, develop a greater understanding of their consumer base, and make well-informed strategic decisions. Reports from Statista indicate that the total amount of data created globally will reach an incredible 175 zettabytes by the close of 2025.
Data analysts are clearly in demand. But are their experiences at work as top-notch? Research indicates yes; according to data gathered and assessed by PayScale, data analysts have an average job satisfaction score of 3.76 stars out of 5 and are “highly satisfied with their jobs.”
If you like solving problems, love statistics, and have an interest in stepping into a highly regarded and demanded professional role, you might want to consider becoming a data analyst. But how? What will it take? In this article, we’ll answer those questions and provide a few insights on how you can embark on your data analytics career path.
1. Acquire the Skills and Training
Every job requires a specific set of skills in order to be successful, and a data analyst’s role is no exception. Regardless of which industry or specialization you eventually choose, every aspiring data analyst needs to have the same foundational skills and competencies.
There are plenty of ways to do so, and your choice will likely depend on your financial and professional situation. Those fresh out of high school might enroll in a four-year undergraduate program in math or computer science. Meanwhile, those who are already in the workforce or have other external obligations may opt for a lower-cost, faster-paced boot camp program. Still others may decide to garner skills through flexible online courses or tutorials. Regardless of the path you choose, it will be important to acquire an understanding of the following tools of the trade.
While Excel might not be the flashiest software in your computer’s Applications folder, it is a workhorse tool for data analysts. This data management tool is accessible and presents an easy means of organizing and analyzing data with simple table data processing, querying, sorting, and filtering features.
Excel also maintains PivotTables, which can distill data from a more extensive database or spreadsheet into an easy-to-understand summary table. Every data analyst needs to have a baseline understanding of how to use Excel’s data management features.
Statistics lie at the heart of data analysis. Without its theoretical backing, data analysts would lack the tools and knowledge necessary to turn a flood of raw data into a channel of useful information; they would not be able to parse data for patterns or quantify uncertainty in their datasets.
Statistics is also at the root of statistical modeling, a practice that allows analysts to create a mathematical representation of observed data and distill conclusions from evident patterns.
Python is one of the fastest-growing and most popular programming languages available to data analysts today. According to data analyzed and published by StackOverflow, a question-and-answer forum for programmers and data informatics professionals, the number of discussions relating to Python has grown 27 percent year-over-year. This is significantly more than other, comparably useful languages.
Python is often pigeonholed as a language for developers, rather than data analysts. As a result, aspiring data analysts frequently try to learn Python from a development perspective.
This is a mistake. Because data analysts in the workforce typically use Python for building models and accessing, cleaning, and visualizing data, a programmer’s perspective will be of little use for an analyst-in-training. Rather than focusing on a programming track, data analysts should dedicate themselves to learning Python’s data libraries and how to use it for data management.
Given that the core of a data analyst’s role is to collect, clean, and analyze data, it’s somewhat of a no-brainer that they also need to understand databases. Information technology professionals use databases to store and organize their data. If a data analyst doesn’t know how to interact with one, they can’t access the information they need to perform their other responsibilities.
That said, some data workers have more contact with databases than others — database administrators, for example, focus on designing, installing, monitoring, and configuring company databases. An ordinary data analyst wouldn’t have as much of a focus on database management; however, it is nevertheless essential that they have basic knowledge of data management systems.
At the very least, data analysts need to have a foundational understanding of SQL and NoSQL. SQL, or Structured Query Language, is recognized as the standard means of querying and manipulating data in relational databases. SQL excels in reading and updating single-row databases quickly, but it struggles with large datasets.
For that reason, aspiring analysts should also familiarize themselves with NoSQL, a language that communicates with databases that store data in non-relational ways. The latter is better suited to large datasets and is thus frequently used by companies who process massive quantities of data. Both languages are important to know.
Hadoop is an open-source data framework that empowers analysts to distribute data storage and process large-scale data sets across disparate clusters of computers and users. Given its distribution-friendly structure, it is often a tool of choice for large teams that need to collectively process a significant amount of data. While not all data analysts will end up working with Hadoop, many will. In any case, it’s undoubtedly a useful skill to have on your CV.
2. Get an Entry-Level Position
First, let’s break down the typical responsibilities that a professional might have in the early days along a data analyst career path.
An entry-level data analyst is detail-oriented. Their work revolves around collecting, organizing, cleaning, and analyzing data. They identify patterns and trends in complex datasets, then distill insights from those findings.
New data analysts also help to develop and manage data collection and storage systems, as well as collaborate with their team members and non-technical business personnel to identify information gaps and define opportunities for process improvements.
But what does it take to get hired as an entry-level data analyst?
Don’t worry; while achieving an entry-level role might seem intimidating from the outset, landing a job isn’t always difficult — provided, of course, that you have the requisite skills, relevant experience, and a positive attitude.
Generally speaking, employers expect their incoming candidates to have a thorough understanding of statistical principles, a firm grasp on data management, and demonstrated competency in the tools and skills mentioned above. Employers usually look for some indication of formal coursework, such as a four-year degree in a field relating to analytics or an intensive data analytics boot camp program.
If you are primarily self-taught, you can compensate for your lack of formal education by assembling a portfolio that demonstrates your skill set and capabilities as adeptly as any resume. Even so, it may be worth considering enrolling in an intensive boot camp program to improve your resume, brush up on your self-taught skills, and gain access to career guidance.
3. Progress to Senior Analyst
Like an entry-level analyst, a senior data analyst spends the majority of their workday dealing with information. They collect, organize, and analyze data to provide actionable insights that will allow business leaders to make well-optimized strategic decisions. There are a lot of responsibilities that overlap with those of lower-level analysts — and one significant difference.
Senior analysts are leaders. Rather than keeping their noses to the digital grindstone, professionals this far along the data analyst career path are responsible for more interpersonal work. They guide their teams through projects, provide instructions and feedback, and offer mentorship to their juniors.
Senior analysts are also responsible for maintaining constructive communication with upper-level leaders and those in other departments. Their efforts ensure that lower-level data analysts always understand their direction and the task at hand.
Senior analysts can climb into an upper-level position in a variety of ways. They might be promoted in-house after distinguishing themselves in a junior role, or they might get the job after proving themselves at a different company. Generally speaking, though, companies usually require senior-level data analysts to have between three and five years of experience in data management and analysis; some companies may even require candidates to have a master’s degree.
No matter where you are in your career journey, you should be keeping opportunities for continued education in mind. You may find that an additional certification or advanced degree makes a difference in the interview room.
4. Potential Director Roles
Data analytics directors are big-picture leaders. They are responsible for overseeing the efforts of a company’s analytics team. They coordinate projects, provide guidance to team leaders, develop initiatives, and measure department achievement. Inter-department collaboration is a crucial facet of a director’s job; they must be able to communicate with executive leaders and other department leaders so that they can identify information needs and prioritize initiatives for the analytics team.
All this said, a data analytics director’s role isn’t laser-focused on interpersonal work. As with the positions mentioned earlier, director-level analysts assess complex data to support business development and distill opportunities and inefficiencies from data patterns. No matter how senior their role, these top-level analytics professionals will perform the same set of core responsibilities: data analysis, visualization, reporting, and presentation.
Becoming a director will take time. If you distinguish yourself early in your data analyst career, you may be able to achieve such a high-level role in under a decade — but for most people, the climb will probably take longer. If you plan to establish yourself as a director, you may want to consider pursuing a master’s degree in business or analytics to better prepare yourself for higher-level responsibilities.
Industries You May Find Yourself Working In
For all the time that data analysts spend immersed in technology, they aren’t confined to the tech sector. These information wranglers can find jobs in almost every industry — especially given that nearly all have become reliant on the insights data analysis provides.
Findings published in Research and Market’s 2020 report on Global Big Data indicate that across-the-world spending on big data analytics topped $180 billion in 2019 alone. That same year, the percentage of firms investing more than $50 million in big data and AI rose from 27 percent in 2018 to over a third (33.9 percent) in 2019. A full 90 percent of surveyed business organizations say that data and analytics will be crucial to their digital transformation by the close of 2020.
Today, data analysts are employed across a host of industries. Their skills underpin operations in fields as diverse as healthcare, finance, hospitality, and gaming. Data analysts often play a pivotal role in industries that a layperson might never expect to employ them in the first place. Let’s highlight a few.
Though often associated more with its pastoral roots, modern farming is heavily reliant on analytics. The industry has slim margins, and its leaders depend on knowing how factors such as crop prices, livestock health, and pesticide amounts will impact their expected costs and losses.
Predictive analytics empower farmers to eliminate waste and manage their expenses. Consider how much farmers gain from using data to determine feed quantities; purchasing supplies based on data cuts down on wasted resources while ensuring that all animals remain healthy.
Data science is fueling innovation in the energy sector. Analysts have given business leaders in the industry the ability to reduce drilling and exploration costs, improve efficiency and efficacy, and even avoid power outages by identifying problematic patterns before they impact consumers.
Sure, travel might not be the first industry you think of in relation to data analysis, but think about it — how useful would it be to predict when people will travel, which locations will see an uptick in popularity, and which rates will turn the most profit in a season? The travel industry runs on analytics as much as any healthcare research lab or tech company.