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

Written by Coursera • Updated on

A data science bootcamp is an intensive and immersive program designed to prepare you for a job in the field of data science. While programs can take only a few months, they'll prepare you for some of the most in-demand jobs in the US.

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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 both 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 degree, how to pick the right boot camp for you, and explore five of the most popular data science bootcamps out there.

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 [2,3]. These differences include:

Data science bootcampData science degree
CostData science boot camps cost around $8,940 to $16,000Average in-state tuition for a bachelor’s in data science is $53,100
DurationAnywhere from a few weeks to a few monthsAt least four years
Time commitmentPart-time or full-timePart-time or full-time
Skills learnedSkills for practical and applied applications, highly specific set of skillsTheories, algorithms, basics of computer science in addition to advanced concepts like machine learning, and programming (depending on if degree is a master’s or bachelor’s)
StructureOnline, in-person, or hybridIn-person is traditional (some universities may offer hybrid or online courses)
Certification typeCertificateBachelor’s degree or master’s degree

How to choose the right data science bootcamp for you

The right boot camp 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.

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 


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 

Look up job descriptions of openings in your area to get a good idea of what skills you’ll need to grow before applying. 

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. 



Python for Everybody

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Average time: 8 month(s)

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Json, Xml, Python Programming, Database (DBMS), Python Syntax And Semantics, Basic Programming Language, Computer Programming, Data Structure, Tuple, Web Scraping, Sqlite, SQL, Data Analysis, Data Visualization (DataViz)

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 [4].

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 [5]. 

While you can’t use FAFSA to apply for financial aid, as you can when earning a college degree, there are some financial aid opportunities 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. According to Course Report, characteristics of a quality program include [6]:

  • 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:

1. Shows determination

Many employers see individuals who earn bootcamp certifications as highly qualified, hard-working candidates. About 40 percent of developers believe bootcamp-trained candidates are just as qualified as peers who went to college, according to HackerRank’s 2020 Developer Skills Report. Another 33 percent say those who have completed bootcamps are more qualified than college graduates [7]. 

2. 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. 

3. 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.

4. 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.


Get started today

Whether you decide to pursue a bootcamp 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, which can introduce you to the critical skills you need to know to get started in the field.


professional certificate

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Average time: 5 month(s)

Learn at your own pace

Skills you'll build:

Data Science, Deep Learning, Machine Learning, Big Data, Data Mining, Github, Python Programming, Jupyter notebooks, Rstudio, Methodology, CRISP-DM, Data Analysis, Pandas, Numpy, Cloud Databases, Relational Database Management System (RDBMS), SQL, Predictive Modelling, Data Visualization (DataViz), Model Selection, Dashboards and Charts, dash, Matplotlib, SciPy and scikit-learn, regression, classification, Hierarchical Clustering, Jupyter Notebook, Data Science Methodology, K-Means Clustering

Article sources


UC Berkley Extension. "Are Data Science Boot Camps Worth It? A Simple Breakdown,". Accessed June 21, 2022.

Written by Coursera • Updated on

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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