Data Science vs. Data Analytics Bootcamps

When it comes to data, people often use the terms “data science” and “data analytics” interchangeably. Although these two areas are certainly related, they are actually distinct fields — and it is vital that people interested in a data-related career understand the difference. Otherwise, you might accidentally find yourself applying to jobs with the wrong set of skills.

Data bootcamps understand the distinction between these two professions, which is why some offer data science and data analytics programs separately. If you are having trouble deciding whether to apply to a data science or a data analytics bootcamp, continue reading this guide, which clearly outlines the differences between these two fields.

What is the difference between data science and data analytics? Let’s start by looking at data science. Data science involves finding the answers to problems by interpreting data and developing new ways to interpret data. If that seems broad, that’s because it is. Data scientists apply a variety of mathematical and analytical skills in their work, such as writing algorithms, building statistical models, and incorporating predictive analytics. This field may also involve advanced programming skills, including software engineering and machine learning.

Data analytics has a narrower focus than data science. Data analysts spend most of their time cleaning incomprehensible data, storing it in the correct format and database, and interpreting it with database functions and tools. People who work in data analytics can identify trends and make predictions from large datasets. This field does not involve as much programming, predictive modeling, or machine learning as data science, which is one of the main differences between data science and data analytics.

Data science relies on a variety of skills, so the curricula for data science bootcamps are diverse. These programs often start by teaching programming languages, with many bootcamps focusing on Python and SQL. Students build on these skills by learning how to use Python to build data visualizations. Bootcamp students also learn data wrangling skills, such as cleaning datasets, and how to manage and work with databases.

While many of these skills are also useful for a data analytics career, data science bootcamps also typically touch on more advanced data science topics, as well. Students learn how statistical inference can relate to data science, and how to properly use A/B tests and correlation and regression models. Data science bootcamps often incorporate machine learning, and other advanced topics might include time-series analysis, natural language processing, or machine translation.

Keep in mind: Since data science covers a broad spectrum of topics, each bootcamp offers its own unique curriculum. You may learn about all of the topics mentioned above, or just some of them. You can explore the curricula of popular bootcamps in this list of online data science bootcamps.

Data analytics bootcamps share many similarities with data science bootcamps; you might even find some of the same learning modules in both. That said, data analytics programs focus much more on databases and less on topics such as machine learning and predictive modeling.

Data analytics courses usually cover how to work with databases and analysis tools, including Microsoft Excel, MySQL, PostgreSQL, and MongoDB. Students typically become experts in SQL — a language used specifically for data analysis. Foundational statistics like modeling and forecasting are also usually a part of data analytics curricula, and many data analysis bootcamps also cover some data visualization.

Some data analytics bootcamps involve more advanced topics that you might find in data science programs, too, like machine learning. And like data science bootcamps, curricula vary from program to program.

After you develop data science skills, you can land a variety of data science jobs. Perhaps the most obvious job path is to become a data scientist. PayScale data shows these professionals earn average salaries of $96,300. These professionals typically focus on data storage, modeling, and predication.

A background in data science can prepare individuals for other jobs, too. Data engineers, who use their programming expertise to develop data management solutions, earn an average income of $92,310, according to PayScale. Data architects also handle data storage and data management tasks and bring home average salaries of $119,580.

Graduates of both data science and data analytics bootcamps often find work as data analysts. These professionals specialize in making sense of large sets of data by organizing jumbled datasets, finding trends, identifying anomalies, and making predictions for the future.

Data analysts work across many different industries, including technology and software development, finance and insurance, media, retail, manufacturing, healthcare, government, and education. Professionals in data analytics jobs earn an average annual salary of $61,110, according to PayScale data.

When debating between data science vs. data analytics, consider several factors. First, there is the practical factor of availability: Which bootcamps do you have access to? You will likely find more data science bootcamps than data analytics programs. If you’re looking for an in-person or part-time bootcamp, that may also limit your available options.

Consider your own career goals and interests. If you want to focus on organizing and evaluating data, you should consider going the data analysis route. Alternatively, if you want to learn more programming skills, use machine learning, and apply more algorithmic knowledge in your work, a career in data science might be a better fit.

Finally, think about your salary goals; data scientists tend to earn more than data analysts. If a high salary is important to you, then be sure to factor that in.

According to PayScale data, data scientists make more money on average than data analysts. Data analysts make an average income of $61,110, while data scientists earn mean salaries of $96,300. And that gap only grows larger as workers gain more experience; entry-level professionals in data analytics jobs earn about $55,760, while entry-level professionals in data science jobs earn $85,390. Experienced data analysts make an average of $71,210, compared to experienced data scientists who earn $121,900.

Yes. The foundational knowledge for data analysts and data scientists is similar; when it comes to data and database management, these two professions share many of the same skills. That said, data scientists often learn additional advanced skills like machine learning, which data analysts can learn through a targeted course or a coding bootcamp.

That depends on the specific job. Some data analyst jobs do require regular coding, but many positions don’t. Data analysts should understand how to use SQL for data manipulation, but many won’t encounter advanced coding in their day-to-day duties.