Data analytics bootcamps are a useful way for people in a variety of industries to improve their ability to leverage data. They are also helpful for those who want to begin a career in the data analytics field, either as young newcomers to the job market or as experienced professionals looking to pivot from a different career.
Data analytics bootcamps teach technical skills and soft skills, that are most in demand in the field, to help bootcamp completers become excellent candidates for data analytics jobs.
We’ve compiled this data analytics bootcamp guide to help you sort through your options and find a bootcamp that fits your present needs and future goals. At the bottom of this page, you will find a comprehensive list of data analytics bootcamps offered in the U.S. Give it a read and get ready to embark on your data journey!
You may have noticed that our site has another guide for data science bootcamps. If data analytics bootcamps teach you all about working with data, then what are data science bootcamps for? Let’s go over the similarities and differences between data analytics and data science.
As you can guess from their names, both fields work with large amounts of data. Both data science and data analytics use technology to best manage, store, and analyze their data. Skills used across both areas include Python, SQL, statistics, and analytical thinking.
The differences here are harder to define than the similarities because organizations and individuals all have their own ways of differentiating between a data analyst and a data scientist. Some organizations may call all of their data professionals “data analysts,” while others may call them all “data scientists.” Sometimes data scientists are the more advanced members of a team, while data analysts are slightly more junior. At other organizations, it may be the opposite! Don’t take our comparison here as the final word on the matter. Even those working in the field admit that the distinctions are blurry.
The way most bootcamps differentiate data analytics and data science is that data analytics involves performing more granular analysis on datasets, while data science involves using advanced techniques to manipulate databases and develop models. Under this distinction, data analytics might involve using existing datasets to answer a question using statistics and querying. Data science, on the other hand, might involve extracting data from many sources to build a data set or developing predictive models using data and coding.
Sometimes data analytics and data science work hand-in-hand. Let’s say there is a business question that needs to be answered. A data scientist might develop models that a data analyst can then employ. A data scientist may also develop a system for compiling the necessary data, and a data analyst can then manipulate and query the data.
Data analytics and data science share many key skills. Other skills are used more in one field than the other. The chart below outlines the differences in skill sets for these two fields.
Data analytics bootcamps aim to cover all of the skills students need to successfully leverage and manipulate large amounts of data in a professional environment. Not all bootcamps cover the same topics, so you’ll want to compare different programs and choose one that covers the skills that you want to learn. Below are topics that many data analytics bootcamps cover.
Python is a coding language that helps manipulate large amounts of data and perform advanced mathematical calculations. It also has non-data applications in software and web development.
SQL is another language used in data analytics. The acronym stands for Structured Query Language. This language is used to write queries that retrieve information from a database.
Power Bi is a Microsoft product used for data visualization (graphs, charts, images, etc.) and analytics.
Tableau is also a visualization and analytics tool. Check out our comparison between Tableau and Power Bi on our Resource page.
Dashboarding is the act of creating dashboards, which are complex data visualizations that display current data and trends about a business or project in a variety of formats.
Geomapping is a specific type of data visualization used for location data. Geomapping produces images in which data is laid out spatially in a way that corresponds to real-life geography.
Matplotlib is a Python library that contains pre-written code and helpful tools that Python users can use to create data visualizations.
Data analytics employs statistics principles for making inferences from large amounts of data. These principles include sampling, averages, probabilities, statistical significance, medians, minimums, maximums, distribution models, and more.
Regression analysis is a statistical measurement that is used to measure the effects of one or more independent variables on a dependent variable. Data analytics use this skill when they ask questions like “What recent factors have impacted business performance this month?”
Data analytics bootcamps vary in duration, curriculum, and level of career support. While most curriculums cover a standard set of key data analytics skills, there may be certain subjects only taught by some programs. For this reason, it is important to review the curriculum before you sign up for a bootcamp.
Most data analytics bootcamps are project-based, though some may also include elements like quizzes and class discussions.
There is a wide range of companies that offer these programs. Some are fully bootcamp oriented and may offer coding bootcamps as well, while others may be based out of universities.
Most bootcamps require students to fill out an application to be admitted to a program. However, it is quite rare that students are flat-out turned away. If administrators determine that a student is not yet ready for an intensive bootcamp experience, they may simply recommend that students start with a beginner prep course or that they reapply when their schedule is less full.
An initial application may ask students questions about any past experience in data analytics but this is often just informative for the bootcamp instructors and not used to make an admissions decision. Applications may also ask students about career goals, commitment to learning, and education level.
Many, but not all, bootcamps do require that students have completed high school or hold a G.E.D. and most are open only to students who are at least 18 years old. Many bootcamps serve international students as well as U.S. students, though some financing options may be limited to only U.S. students (citizens, permanent residents, and DACA recipients).
Bootcamps vary in their tuition costs. On the whole, bootcamps in the higher price range tend to include a lot of extra support and perks such as networking opportunities, individualized career planning, 24/7 access to facilities, and, sometimes, money-back job guarantees.
Bootcamps in the lower price range are often more bare-bones. They may have less face-to-face interaction and fewer career perks. Again, this is not to say that the quality of the material is worse. Low-cost programs can also offer comprehensive and up-to-date curriculums. The main thing to consider when choosing between higher or lower-priced programs is how much additional support you anticipate needing.
While scholarships and financial aid can help lower costs for individual students who seek them out and qualify, the tuition price for a data analytics bootcamp generally falls between $7,000 and $20,000.
Now that you have a pretty good idea of the costs and benefits of a data analytics bootcamp, you can decide if the benefits outweigh the costs. While these programs can be pricey, they can give your career a major boost and are a great option for people who want to transition into a data-centric career but have little to no background experience working with data. It can be a tough field to break into because it requires expertise with multiple technical skills. Completing a bootcamp allows students to first learn what kind of skills and responsibilities might be required of them and also to get real hands-on practice using those skills and producing work.
Completing a bootcamp is an investment in your own career. You invest time, effort, and money in order to come out of the program and find a well-paying job that will recoup your investment. Like any investment, you should think carefully before diving in. Ask yourself, is your ultimate goal to land a job working with data and leveraging your new skills? If not, a data analytics bootcamp might not be worth it for you. However, if this outcome would represent success to you, chances are a bootcamp can help you achieve this goal.
Now that you are prepped with all of the background information on data analytics bootcamps, browse the bootcamp options below to find one that fits your needs! We’ve highlighted special features like career support and community activities. If you click into a Course you can view the specific skills taught. Best of luck in your analytics journey!
People skilled in data analytics can excel in many different fields, not just in analytics. Marketing, finance, government, health administration, retail, and business all leverage data to work effectively and efficiently. After completing a data analytics bootcamp, students can apply their skills to any of these fields.
Data analytics bootcamps cover a huge range of marketable skills in a short period of time. Their curriculums are often designed with job requirements in mind, so adding these skills to your resume can make you stand out as a great choice to hiring managers. It can be difficult to make a career transition all on your own, but bootcamps offer a structured and guided path to help you succeed.
Compared to earning a bachelor’s or master’s degree, bootcamps can be completed very quickly in just a matter of months. If you want to achieve a higher level of education, but aren’t able to dedicate years to doing so, bootcamps are a good alternative.
Many bootcamp teachers come from industry roles or are at least very knowledgeable about the field. They can help you understand what kind of jobs are most in demand and get an understanding of what a career trajectory might look like.
Bootcamps cover a range of advanced skills that are on the cutting edge of business technology. They’ll also help you learn the most current versions of industry tools so that your skills are up to date and ready to help you innovate.
Choosing a bootcamp is a personal decision and depends on circumstances like your current employment status, your familial responsibilities, and your future goals. Think about whether you want to study full time or only on evenings and weekends. Consider how much career support you anticipate wanting when looking for a post-bootcamp job. Decide if you’d rather learn online or in person.
It’s also important to review the bootcamp’s curriculum to make sure that it covers any specific skills you hope to learn.
Many bootcamps offer a range of career services to help students find employment after finishing a program. This does vary across programs and, as we mentioned above, the more expensive programs often have the most robust career services. Career support may include opportunities to network with industry employers, project fairs where students can demonstrate their work, resume review, interview coaching, job hunt guidance, and goal planning.
Some bootcamps offer a money-back job guarantee. That means that their career support staff will work tirelessly to match their students with industry employers since it is just as much in their interest for students to gain employment.
Other programs offer financing options that depend on students’ post-bootcamp job attainment such as income share agreements (ISAs). In this setup, students don’t pay tuition until they have landed a job in data analytics. Then, they repay the cost of tuition through monthly cuts out of their salaries. This kind of financing also incentivizes bootcamps to quickly match students with employers.
According to the Bureau of Labor Statistics, the mean annual salary in the field of data science is $103,930 as of 2020. Analytics is a high-growth field. BLS predicted a 7% increase in jobs working with Big Data over the period of 2016 to 2026. Below, find a breakdown of the average salary you might earn for various analyst positions in the United States. Salary data is from Glassdoor.
|Job Title||Average Salary (October 2021)|
|‘Big Data’ Analyst||$81,839|
|Sr. Data Analyst||$92,001|