What Does A Data Analyst Do?

The title “data analyst” might seem nebulous, but the job description is relatively straightforward. A data analyst collects, cleans, and breaks down raw data sets to solve problems. Careers in operations research are booming, with a predicted 25% increase in job opportunities come 2030. Keep reading to learn what a data analyst does and how you can take the first steps toward becoming one.

Data analysts rely on logic and math to fix real-world problems. They do this by gathering data, organizing it, studying it, and then making decisions based on their observations. Data analysts are found in many sectors, such as:

• Law enforcement

• government

• military

• medicine

• finance

Data analysis is happening all around us. In small ways, we’re analyzing data without realizing it. For example, when we open the fridge and observe that the milk is running low, we come up with the solution to buy more. Data analysts solve problems similarly but on a broader and more complicated level.

Think about how marketing teams determine their target demographic or how companies study ad campaigns to gauge the most successful ads. Companies rely on data analysts to make a cohesive puzzle out of scattered data. Only then can companies see potential problems and create plans to fix them.

Which customers are more likely to shop at certain stores? What behavioral traits can lead to criminal activity? Whether or not we realize it, those types of questions are being solved every day by data analysts. As a result, they serve an integral function in society, and with the proper training, you can too.

Data is everywhere, and companies are collecting data every day. Yet, without data analysts, that data would remain unpacked and unstudied. 

Analyzing data happens in a repeatable and straightforward process. These are the steps a data analyst follows to complete a project.

• Define the problem. An analyst’s first task is understanding the client’s issue, like why a previously successful advertisement is suddenly doing poorly. Once the problem is known, an analyst creates an action plan which is then relayed to the team.

• Collect data. Now it’s time to gather information. Data analysts use CRM systems to sort through customer data, such as subscriptions or preferences. ETL pipelines are vital for extracting, transforming, and loading this data from one database to another, preparing it for analysis.

• Clean data. Data can be messy and inconsistent, with duplicate information, anomalies, and sometimes incomplete information. Quality assurance is necessary for creating clean, workable data.

• Analyze data. Once the data is collected and cleaned, it’s time for the analysis. The six types of data analysis are descriptive, exploratory, inferential, predictive, causal, and mechanistic. 

• Create reports. Finally, with the data sorted and studied, the analyst must interpret the information and present it to the team. 

The work of a data analyst leans heavily on specific skills, such as familiarity with statistical tools and programming languages. You can attain these skills through hard work and patience. Bootcamps are an excellent resource for acquiring these technical skills on your own time.

Now that you understand data analysis, you’re probably curious about what data analysts typically do daily. These will vary depending on the analyst’s specific job, but generally, their tasks include:

• Data mining to extract viable information for study

• Troubleshooting databases and coding errors

• Creating reports on observations and predicted trends

• Collaborating with programmers and developers

• Compiling presentable data for relevant team members and stakeholders 

• Using statistical tools to analyze and interpret patterns

• Overseeing the design of data systems

• Modeling data for statistical analysis and testing

These tasks might sound complicated, but they can be as intuitive as breathing for an analyst who’s worked years in the field. Data analyst bootcamps are effective at arming people with the skills required to be able to perform these daily tasks.

Just like hammers, saws, and screwdrivers perform different functions, the tools of a data analyst depend on the job at hand, as well as company requirements. For example, some analysis tools are made for data visualization, while others are used for analyzing results. Here are some of the most common tools and programming languages data analysts use.

• SQL

• Microsoft Excel

• Python

• Matlab

• SAS

• Git

• Tableau

• SPSS 

Skill-based hiring is at an all-time high, with many companies overlooking educational backgrounds and college degrees. With the predicted 25,600 new job opportunities in operations research–including data analysts–people are wondering how to get equipped for this booming career path.

Given the highly technical nature of data analysis, specific hard skills are must-haves if you’re considering a job in the field.

• Knowledge of relevant programming languages. Python and R are two common open-source languages every data analyst would know. Python helps build and visualize data structures. R is an excellent tool for statistical modeling and analysis.

• Familiarity with database tools. This may come as a surprise, given its seeming simplicity, but Microsoft Excel is a crucial tool for data analysis. Excel’s ability to compile formulas and stack data in tables makes it pivotal for analytics. SQL is another mainstay for data analysts. This Structured Query Language is used for modifying databases and information retrieval.

• Understanding of data visualization. Data sets are often massive and packed with potentially millions of data points. Presenting all that data in a manageable way requires the aid of visualization tools. Google Charts and Tableau are among the most popular. These tools can organize data into charts or graphs like bite-sized pieces of information that viewers can easily absorb.

• Background in statistical mathematics. Data analytics is a numbers-heavy field that relies heavily on statistical analysis. Statistics is key to studying data patterns and creating solutions for the client’s problem.

If any of these technical skills seem foreign to you, don’t worry. If you have the patience and are willing to invest a few months, bootcamps can equip you with these foundational skills used by every data analyst. 

In addition, these soft skills are necessary to succeed as a data analyst.

• Leadership. Data analysts are expected to steer the conversation when it comes to making informed decisions for the good of the company. A data analyst can’t be too timid to speak up when need be.

• Communication. Data is no good if it just sits in the analyst’s brain. It needs to be shared with company stakeholders, as well as team members, such as programmers and developers. Communicating the problem and the potential solution effectively is key to success as a data analyst. 

• Problem-solving. People come to data analysts with issues that need solving. Analysts need to have a clear understanding of the problem and the ability to think outside the box for solutions. This requires critical thinking and sharp intuition. 

• Relevant industry knowledge. Data analysts are hired across a variety of fields. Knowing the ins and outs of the industry–whether that be health care, military, or finance–gives the analyst the wherewithal to handle problems appropriately and with an informed background of industry news and trends.

The annual base pay for a data analyst, according to Glassdoor, is $63,566. This doesn’t include the additional compensation for yearly bonuses, commissions, stock options, and profit sharing. With those incentives, the recorded annual salary sits at $95,717. 

Job openings for data analysts continue to grow. The World Economic Forum puts data analysts in the number one position of increasing demand across all industries. 

Data isn’t going away. The next decade will bring about a wealth of new jobs in data analytics. Take advantage of the industry growth and start learning the necessary skills. The University of South Florida’s Data Analytics Bootcamp can teach you the fundamentals of those hard skills, like knowledge of SQL, Python, and Tableau. In addition, industry experts offer 1:1 mentorship and weekly meetings. Learn on your own time and graduate with a well-rounded portfolio ready to be shared with future employers. 

Become a data analyst with USF’s bootcamp, and prove your readiness to join this lucrative career industry.