How to Choose a Data Science Bootcamp (+ 5 to Consider)

Data science bootcamps are intensive and immersive courses that teach advanced data science skills to individuals pursuing a career in the field. Offering a crash course in everything from Python, SQL, data visualization, Hadoop, and more, data science bootcamps can equip course takers with a deep knowledge of fundamental concepts and advanced techniques.

But how do you know if a data science bootcamp is right for you? And, what should you consider when assessing the many different bootcamps out there?

In this article, you'll learn about the difference between a data science bootcamp and a degree, how to pick the right boot camp for you, and explore five of the most popular data science bootcamps.

Read more: Data Science Major: What You Need to Know Before Declaring

Data science bootcamp vs. data science degree 

Data science bootcamps and degree programs both prepare people for careers in the field. But, if you already have an undergraduate degree in an unrelated field, then a bootcamp could be a great way to gain the skills you need to join the career.

Many people who enroll in a bootcamp hold an undergraduate degree in an unrelated field and want to get formally certified in data science so that they can pursue a career in the field. You can also earn a data science degree, but there are considerable differences between the two. These differences include:

How to choose the right data science bootcamp for you

The right bootcamp is the one that helps you reach your career goals, build the appropriate skills you need to land a job, fits your current budget, and works with your personal time frame. This section will walk you through each step, so you can pick the right bootcamp for you.

1. Outline your career goals. 

While many data science bootcamps cover similar material, they each also have their own focus that can make a difference when you are pursuing a specialized career in the field.

To choose the right bootcamp for yourself, then, you need to identify and outline your career goals so that you can match a program to your own professional interests. Some questions that you might ask yourself include:

• Where do you want to be in five years?

• Are you seeking an entry-level position or an upper-level position?

• Are you already employed and want a promotion, or are you starting your career?

• What skills does your career goal require?

By answering these questions, you'll gain greater clarity on the kind of program you can use to reach your career goals.

2. Research job requirements. 

Once you've outlined your career goals, you should now research the skills and qualifications you will need to obtain to perform the job. Many data science jobs require you to possess a skill set specific to that particular position, which may differ somewhat from those that you already possess. A few of the most common technical and workplace skills you may find in the field of data science include:

• Communication 

• Teamwork 

• Creativity 

• Perseverance 

• Problem-solving 

• Knowledge of programming languages, such as Python or R

• Databases 

• Machine learning 

• Data visualization 

• Big data frameworks 

Research postings for desirable jobs and read the descriptions to get a good idea of what skills you’ll need before applying. 

Data science is a broad field that includes artificial intelligence, cybersecurity, machine learning, and more. As a result, there are many careers that you can pursue in the field. Some of the most popular data science careers include: 

- Data engineer

- Statistician 

- Data analyst 

- Machine learning engineer 

- Data scientist 

3. Assess your current skills. 

You’ll find the most success with data science bootcamps if you already have some core foundational data science knowledge. Bootcamp instructors move fast, and you’ll likely be completing projects that require some background knowledge. Classes are usually limited to mastering key high-level skills and building your career toolbox. 

There won’t be much time for reviewing basic concepts, so assess your skills to know what type of bootcamp would be the best fit for you based on your skills. If you need to focus more on the basics, look for a data science bootcamp for beginners or consider taking an online course to either brush-up or expand your current skills, such as the University of Michigan's Python for Everybody Specialization. 

4. Research programs. 

Most data science bootcamp programs last three to six months and the cost will vary by location and institution.

When researching programs, consider what class structure works best for your schedule, what skills you need to learn based on your career goals, and the integrity of the institution or organization offering the bootcamp. Make sure to check for any prerequisites before applying for a program as well.

5. Consider structure and location.

One of the key questions you will face when comparing different data science bootcamps is whether you want an online, in-person, or hybrid program. Each of these different educational approaches have their own benefits, depending on your goals, available resources, and personal circumstances.

In-person classes

Typically, in-person bootcamps provide more structure in a hands-on environment with an instructor ready to help as you need.

In-person classes can also be a networking opportunity and chance to build people skills like teamwork and collaboration. However, if you want to enroll in a bootcamp that isn’t local or have a busy schedule, this option may not be flexible enough for you.

Online courses

Online programs can be a convenient way to join a program without sacrificing a comprehensive education. Capable of being completed anywhere with an internet connection, online bootcamps can often be done at your own pace. While some may have an instructor on call when you need help, others may be more self-directed and independent. However, online courses may not provide as many opportunities for networking and team-building practice as in-person programs.

Hybrid courses

Hybrid courses offer the pros of both online and in-person courses. With a hybrid-style data science bootcamp, you can experience the immersion of in-person learning with the convenience of online learning. This is an excellent option if you live near an institution but have a busy schedule or want the flexibility of online learning in addition to in-person classes. 

6. Take note of relevant topics.

Some bootcamps are specialized in a specific field of data science or focus on a particular set of skills. However, you can expect to generally see a few of these topics in the course work: 

• Python programming 

• Machine learning

• Coding

• Statistics 

• A/B testing

• Intermediate excel 

• Linear regression

• Databases (MySQL, MongoDB, etc.) 

Don’t expect to be taking notes all day in these bootcamps. Most are project-based, hands-on programs that offer you valuable skills to take into the workforce. Review the course content thoroughly to be sure it aligns with your career goals.  

7. Know the cost. 

Data science boot camps cost around $8,940 to $16,000, depending on the school or organization [1]. 

While you can’t use FAFSA to apply for financial aid, as you can when earning a college degree, some financial aid opportunities are available for some bootcamps.

These include deferred tuition, income share agreements, or school financing. Some employers may also offer a form of tuition reimbursement if you’re already employed but looking to move into a role of seniority. 

8. Research institution reputation. 

Make sure that you choose a bootcamp from a reputable institution or university. Characteristics of a quality program are likely to include:

• Alumni and student reviews

• Well-established (program offered for 3+ years)

• Published CIRR (Council on Integrity in Results Reporting) outcomes within the past year

• Variety of financing options

• Vetted lending partnerships

• Other factors like level of career support and selectivity of the application process 

Those requirements might sound like a lot to consider but there are free online resources that rank data science bootcamps. Combine those resources with your own research to track down the top programs relevant to your interests. 

It’s also worth mentioning that employers or individuals already working in the industry may have recommendations for reputable bootcamps that they could share with you. 

9. Decide if a data science bootcamp is right for you.

There are many benefits to data science bootcamps, but it’s important to know if this learning style is right for your overall career goals. Consider these common benefits and drawbacks to data science bootcamps:

• Limited financial aid. Bootcamps are typically much less expensive than earning a degree, but be aware that it’s rare to find financial aid opportunities to help you cover the cost of a bootcamp program. You may find success with school financing options or staggered payment plans. 

• Efficient but intensive. If you want to get into the field of data science fast, bootcamps can be your fast rack in the door. Though the entire program length runs for just a few months, expect to be immersed in all things data science during that time. This can be a problem if you already hold a full-time job.

• Career-focused. When you enroll in a data science bootcamp, you’ll learn alongside like-minded individuals who are likely just as career-focused as you. You may also find career services and job assistance built into these programs. 

10. Apply.

When you find a program that’s right for you and matches up with your career goals, it’s time to apply!

Depending on the program, the enrollment process may include a call with a program representative, application, and assessment prior to acceptance.

Alternative options to data science bootcamps

If you decide a data science bootcamp isn't right for your goals, you have options. The list below outlines a few similar offerings from industry leaders in technology:

• IBM Data Science Professional Certificate: This 100-percent online, self-paced program is designed to cover all the latest job-ready skills and tools. Using real-world data sets, you'll work with open-source tools and libraries, databases, and the IBM Cloud. By the end, you'll have built a portfolio of projects and earned a Professional Certificate from IBM.

I had never really thought of myself as a data-oriented person, but the way that the program was taught was easy to understand.

— Emma S., on taking the IBM Data Science Professional Certificate

•  Google Data Analytics Professional Certificate: Seventy-five percent of Google Career Certificate Graduates in the US report an improvement in their career trajectory within six months of completion. In this program, you'll gain data cleaning, data ethics, and data visualization skills while working with industry tools and platforms like SQL, Tableau, and R programming. Upon completion, you'll earn a Professional Certificate from Google for your resume.

Start learning data science today

When you're ready to advance your data science career, consider enrolling in the first course What is Data Science? of the 10-part series in IBM's Data Science Professional Certificate. All you need to get started is basic computer literacy and the willingness to learn online.