Here are five steps to consider if you’re interested in pursuing a career in data science:

A data analyst collects, processes and performs statistical analyses on large datasets. They discover how data can be used to answer questions and solve problems. With new and advanced computers and a trend toward greater technological integration, the field of data analysis continues to evolve. The development of the relational database gave a new breath to data analysis, which allowed professionals in the field to use SQL (pronounced “sequel” or “s-q-l”) to retrieve data from databases.

Most jobs in data analytics involve gathering and cleaning data to uncover trends and business insights. The day-to-day data analyst job duties vary depending on the industry, company or the type of data analytics you consider your specialty. Data analysts may be responsible for creating dashboards, as well as designing and maintaining relationship databases and systems for different departments throughout their organization using business intelligence software, Tableau and programming.

Most data analysts work with IT teams, management staff or data scientists to determine organizational goals. They mine and clean data from primary and secondary sources, then analyze and interpret results using standard statistical tools and techniques. In most cases, they pinpoint trends, correlations and patterns in complex data sets and identify new opportunities for process improvement. Data analysts must also create reports on their findings and communicate next steps to key stakeholders.

The day-to-day for a data analyst depends on where they work and what tools they work with. Some data analysts don’t use programming languages and prefer statistical software and Excel. Depending on the problems they are trying to solve, some analysts perform regression analysis or create data visualizations. Experienced data analysts are sometimes considered “junior data scientists” or “data scientists in training.” In some cases, a data analyst/scientist could be writing queries or addressing standard requests in the morning and building custom solutions or experimenting with relational databases, Hadoop and NoSQL in the afternoon.

“A big part of my job is creating player projections for Fantasy Baseball. These power the default rankings in our draft rooms and inform my preseason and in-season rankings of players. Our readers and customers of our Fantasy product rely on the accuracy of these projections, so it’s important to have a sound statistical basis for making them. During the season, we have a high degree of interaction with our audience, as a large part of our responsibility is to respond to questions about player value and performance. Statistical analysis informs these recommendations, whether they are made through social media platforms, written and video content or podcasts”

–Al Melchior, a Fantasy Sports Data Analyst for CBSSports.com

Here are some other important tools data analysts use on the job:

Today’s data analysts should be prepared for change as the field is becoming more complex. Experienced analysts use modeling and predictive analytics techniques to generate useful insights and actions. They then have to explain what they’ve discovered to colleagues and stakeholders who may not have expertise in this domain.

Market research analyst positions are expected to grow 13% from 2022 to 2032 and management analyst positions are expected to grow 10% during the same time period. This is much faster than the average job growth rate for all occupations, according to the U.S. Bureau of Labor Statistics (BLS). Data analysts can find opportunities in a wide array of industries, including finance, healthcare, information, manufacturing, professional services and retail. The expansion of technology corresponds with the need for analyst positions in such sectors. We are collecting data at every turn, and its organization and use in predictive analysis can contribute to the improvement of society as a whole.

Salary frequently hinges on job responsibilities, along with various other factors. A senior data analyst with the skills of a data scientist may have the potential for a higher-than-average earning potential.

If you’re interested in a career change or want to deepen your understanding of data analytics, you may want to consider a boot camp. Tech boot camps are fast-paced training in specific programming languages such as Python, R or SQL that are offered in a variety of formats. Online coding boot camps offer intensive learning experiences that mimic the real world, with students learning how to create projects from scratch.

If you’re interested in a career in analytics, enrolling in a data analytics boot camp may help prepare you for a new job opportunity. Data analytics boot camps typically cover statistical analysis, analyzing data to uncover insights and using business intelligence software such as Tableau and other tools commonly used by data analysts.

Another option is a data science boot camp. Data science boot camps typically cover more advanced analytical concepts as well as machine learning, natural language processing and neural networks. If you’re unsure of which boot camp to enroll in, consider your career goals and what you want to achieve in your existing or new role.

Boot camps can last a week to a few months depending on whether you opt for part- or full-time enrollment. You can choose an option that works with your schedule and learning goals. Some boot camps offer prep courses and workshops to help ensure students’ success.

Last updated September 2023.

This page includes information from O*NET OnLine by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. O*NET® is a trademark of USDOL/ETA.