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Our Guide to the Best Data Analytics Bootcamps

It’s hard to open the Wall Street Journal, Financial Times, or Economist without reading about how important data-driven decision-making — and the people gathering the data and making and supporting those decisions — is for the modern economy.

No surprise, then, that data analytics is a booming field, expected by Technavio to grow by almost 14% between 2021 and 2026 as advanced data technologies like machine learning proliferate — even as the US Bureau of Labor Statistics (BLS) expects the United States’ economy to grow by only around 2% a year between 2020 and 2030.

global data analytics market size chart

And yet, the US data analytics job market may actually be growing at a faster rate than the global data analytics economy. The BLS foresees 23% growth in the “Operations Research Analyst” — or data analyst — market over the next decade.

Those landing these jobs can expect above-average compensation: the BLS pegs the median salary for someone in one of these roles at $82,360, well above the $58,260 that they estimate the average American earned in 2021.

Increasingly, those interested in breaking into data analytics are turning to data analytics bootcamps for intensive upskilling sprints at a fraction of the cost of a traditional degree. But is a data analytics bootcamp right for you? In this article, we dive into data analytics and how exactly a bootcamp can help prepare you to be a data analyst, business intelligence analyst, or any number of other data-centric professions. We’ll also give you our picks for the data analytics bootcamps that will give you the best chance of landing one of these jobs.

What is data analytics?

At first glance, data analytics might seem to be a fancy word for data analysis — but while data analysis plays a prominent role in data analytics, there’s more to it than that. According to industry-leading IT mag CIO, data analytics is 

“a discipline focused on extracting insights from data. It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems.”

There are four main types of data analytics that are important to keep in mind: descriptive, diagnostic, prescriptive, and predictive.

Descriptive data analytics

Descriptive data analytics involve the analysis of historical data to answer the question, “What happened?” In retail, companies employ descriptive data analytics for tasks like tracking inventory and sales.

Diagnostic data analytics

Diagnostic data analytics involves the analysis of data to answer the question, “Why did this happen?” In marketing, diagnostic data analytics can be employed to help understand why some campaigns are more successful than others.

Prescriptive data analytics

Prescriptive data analytics involve analyzing data to answer the question, “What should we do next?” In logistics, prescriptive data analytics are becoming more and more important as companies like Amazon seek to deliver goods to millions of customers as efficiently as possible.

Predictive data analytics

Predictive data analytics involve the analysis of data to answer the question, “What might happen in the future?” In manufacturing, companies frequently employ predictive data analytics to predict future buying patterns and manage their supply chains accordingly.

Data Analytics vs Data Science

There’s often confusion between data analytics and data science given the considerable overlap between the two. While there is no set of definitions that can catch all of the nuance of the relationship between the two — especially because job titles and data operations are rarely uniform or consistent between businesses and industries — for our purposes we will differentiate the two as follows:

Data analytics concerns the tools and techniques surrounding the process of data analysis. Often, but not always, data analytics professionals will use existing tools and techniques on existing databases.

Data science, on the other hand, concerns the development and deployment of new tools and techniques and new ways to gather data for analysis. The analytics involved in data science will thus likely be more advanced and will frequently involve machine learning. Oftentimes, data analysts and other data analytics professionals will employ methods and tools developed by data scientists.

If you are interested in data science, dive deeper with our articles on: 

What kinds of jobs are out there for someone interested in data analytics?

We’ve covered the different types of data analytics, but who are the people getting it done? For the most part, jobs in data analytics will have the title “data analyst,” but there are some notable exceptions, including “business intelligence analyst,” “data engineer,” and “marketing analyst,” to name a few. Below, we’ll give an overview of each of these roles and provide some real-world examples.

Data Analyst

Data analyst is an extremely broad title — so the day-to-days of two data analysts at different companies might look very different. Typically, however, a data analyst gathers data and readies it for analysis, and then analyzes it to yield business insights that the analyst will communicate to relevant stakeholders, often through data visualizations.

According to, in the US the average data analyst salary is $81,719.

Business Intelligence Analyst

There’s a fair amount of overlap between a data analyst and a business intelligence analyst. Indeed, sometimes it just comes down to what title a company gives a position. But what sets a BI analyst apart? According to Indeed, a business intelligence analyst — or, sometimes just “business analyst” — is more narrowly focused than a data analyst on the metrics, sometimes called “key performance indicators” (KPIs), that can be used to evaluate a company’s performance. By definition, business intelligence focuses on the past, while the umbrella field of data analytics can include areas like prescriptive and predictive analytics that focus on future activity.

According to, in the US the average business intelligence analyst salary is $84,803.

Data Engineer

A far easier distinction to make is that between a data analyst and data engineer. While a data analyst is primarily focused on data analysis, with data collection, preparation, and storage being more ancillary to this analysis, a data engineer focuses squarely on efficiently transforming raw data for this analysis. In a sense, a data engineer is a specialist in one aspect of data analytics.

For their specialization, data engineers can demand higher compensation. According to, in the US the average data engineer salary is $112,555.

Marketing Analyst

Though the “marketing analyst” title can sometimes refer to professionals who focus on larger market trends, it will generally refer to a data analyst whose work contributes directly to a company’s marketing efforts. This often means analyzing how campaigns perform in particular markets or in particular media (i.e. print, app, web, etc.) using sources like Google Analytics.

According to, in the US the average marketing analyst salary is $57,282.

Why a data analytics bootcamp?

If your goal is to get hired as a data analyst, business intelligence analyst, data engineer, marketing analyst, or one of the many other jobs out there in data analytics, a data analytics bootcamp has four chief advantages over a traditional degree program:

Shorter: The duration of a data analytics bootcamp is measured in months, not the years used to measure the length of most degree programs. This doesn’t mean that bootcamps are light on content: a lot of learning can be packed into those months.

More flexible: Because they are entirely online and often offer part-time options, bootcamps are perfect for those with existing professional or personal obligations who need some flexibility in how and when they study.

More affordable: Being shorter and online allows bootcamp providers to offer quality education at a fraction of the cost of a traditional degree program. Many providers also offer financing or income-share plans where you pay back a fraction of your income once you get a job. Some even offer a job guarantee, where you get your money back if you don’t land a job within a certain timeframe.

More practical: We aren’t saying that traditional degree programs don’t offer practical training — far from it — but with a shorter duration, data analytics bootcamps need to get right down to it, ensuring that every lesson provides technical skills and experience directly applicable on the job. Many boot camps also offer students an opportunity to put what they’ve learned into practice through a capstone project. But perhaps most importantly, data analytics bootcamps are solely focused on helping students land an entry-level job, and offer career services to help students do so.

Who are data analytics bootcamps for?

Seeing the chief advantages of bootcamps, you can start to see who a data analytics bootcamp might be right for, namely those with limited time and limited money who are looking to make a quick but lasting change in their career.

Boot camp attendees might have backgrounds in STEM, information technology (IT), or even the social sciences or humanities. They might already have a bachelor’s degree in a different field or might be experiencing post-secondary education for the first time.

Some programming experience or experience with statistics will typically help a student get more out of a data analytics bootcamp, but this is not essential. In fact, some programs even offer self-paced pre-course modules in these areas to help those with less experience hit the ground running.

Bootcamps vs. Certificate Programs

With so many options out there, it’s easy to get confused about the difference between data analytics certificate programs and data analytics bootcamps. The way we see it, all bootcamps are certificate programs — students who successfully complete one are awarded a certificate — but not all certificate programs are bootcamps. Bootcamps distinguish themselves by the relative length, rigor, and intensity of their curricula, plus their focus on preparation for an entry-level job and the career services they offer in service of this.

What will you learn in a data analytics bootcamp?

What exactly is contained in these curricula? A data analytics bootcamp attendee can expect instruction in the following areas:

Programming and other technical skills and tools, including SQL, Python, R, Tableau, SAS Enterprise Miner, and Excel

Statistical analysis, including linear regression and predictive modeling

Key data analytics processes, including data collection, data cleaning, data mining, and data visualization

Advanced concepts, including machine learning, data ethics, big data, and introductions to data science 

Soft skills, including project management and decision-making

Many data analytics boot camps also offer extensive careers-guidance services, such as 1:1 mentoring, resume and portfolio help, employer information systems, and job leads.

What to look for in a data analytics bootcamp?

If you’ve decided that a data analytics bootcamp might be the best next step for your career, how do you know which will be right for you? Obviously, you want to ensure that the schedule and cost of the bootcamp works for your life and budget. Additionally, we suggest that you look for the following: 


Data analytics bootcamps are almost only online — but there are a lot of different kinds of online study these days. While they will generally be more expensive, we recommend choosing a bootcamp that provides as much live instruction as possible. The more facetime you can get with a professor and your fellow students, the more opportunities you will have to clarify aspects of the instruction you are unsure about, gain valuable feedback, and stay excited and engaged.


Most reputable bootcamps will list their faculty on their websites. As you’re researching programs, be sure to pay attention to who exactly would be teaching you. What are their qualifications? How many years of experience do they have? Are they actually employed by the educational provider offering the bootcamp, or some third party? Will you have a chance to engage directly with them, or will you just be watching recordings?

Student Outcome

Though information on student outcomes is not always readily available, it is an invaluable resource in determining if a bootcamp is worth the investment. As you’re researching, see if you can find out whether those who took the bootcamp in the past ended up getting jobs in the field, and if so, what the success rate was. If you can find this information, you might instead try to find testimonials from prior students, but keep in mind that if these testimonials were solicited by the educational provider, they might not be terribly valuable as to the overall experience of the program.


A bootcamp curriculum should be specific and jam-packed with practical, technical skills that you’ll be able to use on the job. At the same time, be wary of a data analytics course that promises to teach you dozens of different kinds of software and programming languages.

A successful bootcamp need only cover SQL, Python or R programming language, and Tableau to get someone up-and-running in data analytics. It’s also crucial that you come out of the bootcamp with something to show for it: a project you can use to start your portfolio. This will be an important proof-of-concept to show recruiters that you are able to analyze data and communicate your findings compellingly.


Data analytics boot camp tuition isn’t cheap: signing up for one is a big investment. It’s in your best interest to find a program that offers you some kind of guarantee that with their training you will be able to get a job offer in the field.

Careers Guidance

The job market can be tricky for even the best-qualified candidates. If a bootcamp offers careers services, such as 1:1 mentoring, resume and portfolio help, it’s a good sign that they are invested in your success.

Our picks for best data analytics bootcamps

In making our picks for the best data analytics bootcamps, we’ve taken these aspects into consideration as we’ve made holistic evaluations of the various programs out there.

It’s important to emphasize that there is no one “best” data analytics boot camp for all the aspiring data analytics professionals out there. For our picks, we’ve foregone ranking and focused instead on pointing interested individuals to a variety of programs that we think will deliver them lasting value.

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Columbia Engineering Data Analytics Bootcamp

Columbia Engineering’s Data Analytics Bootcamp offers courses in Excel, Python, databases, and machine learning, as well as a final project that students are able to collaborate on with their peers. Throughout the bootcamp, students have access to academic and career support, including a Career Engagement Network that helps students prepare their resumes and portfolios and practice interviewing.

  • Length: 24 weeks, part-time (9 hours of live classes per week, 20+ hours of homework per week)

  • Modality: Live online

  • Prerequisites: None

  • Syllabus Highlights: “Excel crash course,” “Python data analytics,” “Machine learning and other advanced topics”

  • Reported Outcomes: None reported, though positive testimonials

  • Cost: $14,745

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Georgetown University’s Data Analytics Bootcamp

Georgetown’s Data Analytics Bootcamp offers students the opportunity to learn data analysis and data visualization and apply these skills in collaborative projects. Georgetown also offers a course in critical thinking and 1:1 career services to help students find job placement after the course.

  • Length: 12 weeks, Friday evenings and Saturdays

  • Modality: Live online

  • Prerequisites: None

  • Syllabus Highlights: “Foundations of Data Analytics & Python Basics,” “Statistics,” “Data Analytics in Python & SQL,” “Data Visualization”

  • Reported Outcomes: None
    Cost: $6,995

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Berkeley Online Data Analytics Boot Camp

Cal Berkeley’s Data Analytics Boot Camp offers students the opportunity to practice skills like Excel, Python, API Interactions, and Machine Learning on projects focused around fields like finance, human resources, healthcare, and government. For those interested, Berkeley also offers a coding bootcamp.

  • Length: 24 weeks, with 9 hours of in-class time and 20+ hours of hands-on projects and practice work

  • Modality: Live online

  • Prerequisites: None

  • Syllabus Highlights: “Excel Crash Course,” “Python Data Analytics,” “Web Visualization”

  • Reported Outcomes: None reported

  • Cost: $9,995

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CareerFoundry’s Data Analytics Program

CareerFoundry’s Data Analytics Program isn’t a bootcamp by name, but we’re including it in the list because it offers opportunities to work 1:1 with an expert mentor, meaning that students will have direct visibility on what it’s like to work as a data analyst. CareerFoundry also offers an impressive 180-day guarantee: either you get a job offer as a data analyst within 180 days after finishing the course or you get your money back.

  • Length: 5 months at 30-40 hours/week or up to 8 months at 15-20 hours/week

  • Modality: Self-directed online with live 1:1 mentor sessions

  • Prerequisites: None

  • Syllabus Highlights: “Cleaning Your Data,” “Conducting a Descriptive Analysis,” “Storytelling with Data”

  • Reported Outcomes: CareerFoundry graduates have gone on to jobs at Amazon, Facebook, and Google

  • Cost: $6,210

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datacamp’s Data Analyst in SQL Career Track

While datacamp’s data analyst course of study also isn’t a bootcamp per se, it’s a novel alternative for a fraction of the cost. Students take 11 courses led by datacamp faculty and industry professionals and complete a final project. For just $25/month, students are also able to supplement this data analyst course with courses in Python, Tableau, and R.

  • Length: 39 hours of instruction

  • Modality: Self-directed online

  • Prerequisites: None

  • Syllabus Highlights: “Introduction to Statistics,” “Intermediate SQL,” “Exploratory Data Analysis in SQL”

  • Reported Outcomes: None reported
    Cost: $25/month, which includes access to other courses as well


In this guide, we’ve covered what data analytics is, what kinds of jobs are out there for someone with data analytics skills, and how data analytics bootcamps can help you get started. 

If you’re interested in data analytics but not sure a bootcamp is for you, check out our guides on data analytics courses and master’s programs to learn more about these other educational options. 

If you’re interested in a bootcamp, but drawn to the rigor of data science, check out our data science bootcamps guide (or online data science bootcamps)