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

Data science bootcamps are short, intense, immersive courses that teach advanced data science skills to individuals pursuing a career in the field [1]. 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 boot camp is right for you? And, what should you consider when assessing the many boot camps out there?

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

Data science boot camp vs. data science degree 

Data science boot camps and degree programs prepare people for careers in the field. But if you already have an undergraduate degree in an unrelated field, a boot camp could be a great way to gain the skills you need to enter the career. 

Many people who enrol in a boot camp hold an undergraduate degree in an unrelated field and want to get formally certified in data science so they can pursue a career in the field. You can also earn a data science degree, but there are considerable differences between them. [2, 3] These differences include: 

How to choose the right data science boot camp for you

The right boot camp is the one that helps you reach your career goals, helps you 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 boot camp for you. 

1. Outline your career goals. 

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

To choose the right bootcamp for yourself you need to identify and outline your career goals so you can match a program to your professional interests. Some questions 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 just starting your career? 

• What skills does your career goal require?

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

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

- Data engineer

- Statistician 

- Data analyst 

- Machine learning engineer 

- Data scientist

2. Research job requirements. 

Once you've outlined your career goals, you should research the skills and qualifications you’ll need 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 you already possess. A few of the most common technical and workplace skills you may find in 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. 

3. Assess your current skills. 

You’ll succeed most with data science boot camps if you already have some core foundational data science knowledge. Bootcamp instructors move fast, and you’ll likely complete projects requiring some background knowledge. Classes are usually limited to mastering key high-level skills and building your career toolbox. 

There won’t be much time to review basic concepts, so assess your skills to know what type of bootcamp would best fit you, based on your skills. If you need to focus more on the basics, look for a data science boot camp 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 boot camp programs last three to six months, and the cost varies 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 boot camp. Make sure to also check for any prerequisites before applying for a program.

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 has its own benefits, depending on your goals, available resources, and personal circumstances. 

In-person classes

Typically, in-person boot camps provide more structure in a hands-on environment, with an instructor ready to help as needed.

In-person classes can also be a networking opportunity and a chance to build people skills like teamwork and collaboration. However, if you want to enrol in a boot camp 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. You can complete most online bootcamps anywhere there’s an internet connection and at your own pace. 

Some online boot camps may have an instructor on call when you need help, while others may be more self-directed and independent. Online courses may not provide as many opportunities for networking and team-building as in-person programs.

Hybrid courses

Hybrid courses offer the pros of online and in-person courses. With a hybrid-style data science boot camp, 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 boot camps 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 coursework: 

• Python programming 

• Machine learning

• Coding

• Statistics 

• A/B testing

• Intermediate excel 

• Linear regression

• Databases (MySQL, MongoDB, etc.) 

Don’t expect to take 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 CA$975 to CA$16,450, depending on the school or organization [2].

Although you can’t use the Canadian Student Financial Assistance Program for boot camps, you may find financial aid opportunities available through the boot camp providers themselves.

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 boot camp from a reputable institution or university. According to the University of Kansas, characteristics of a quality program include [4]:

• Real-time access to instructors 

• Career planning services 

• Financing options 

• Portfolio building 

• Flexible scheduling

Those requirements might sound like a lot to consider, but you can find free online resources that rank data science boot camps. 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 boot camps that they could share with you. 

9. Decide if a data science boot camp is right for you.

There are many benefits to data science boot camps, 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 boot camps:

Shows determination

Many employers see individuals who earn boot camp certifications as highly qualified, hard-working candidates. About 39 per cent of developers believe boot camp-trained candidates are just as qualified as peers who went to college, according to HackerRank’s 2020 Developer Skills Report. Another 33 per cent say those who have completed boot camps are more qualified than college graduates [5]. 

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. You may find success with school financing options or staggered payment plans. 

Efficient but intensive

If you want to get into data science fast, boot camps can be your fast track in the door. Though the entire program runs for just a few months, expect to be immersed in data science during that time. This can be a problem if you already hold a full-time job.

Career-focused

When you enrol in a data science boot camp, you’ll learn with 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 aligns with your career goals, it’s time to apply. 

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

Get started today

Whether you decide to pursue a boot camp or get a degree, you might consider taking a flexible online data science program to prepare for your future career. Coursera offers specializations and professional certificates from top industry leaders like IBM that can introduce you to the critical skills you need to know to get started in the field.