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Data Science Bootcamp vs Master’s Degree

Overview: Data Science Bootcamp vs Master's

Data science bootcamps and master’s programs are two of the most common pathways into data science careers.

Each learning program offers key advantages and trade-offs.

Bootcamps are accelerated, with most programs specifically designed to teach new skills quickly. As a result, the trade-off you often receive is a lack of depth into more niche and advanced topics.

Master’s programs, on the other hand, offer depth and domain-specific expertise, but require a greater investment of time. They’re also costlier.

Ultimately, choosing the best program comes down to where you are at in your career, your career goals, and how much time and money you want to invest. We put together this quick guide to offer some insights into helping you choose the right program.

What is a Data Science Bootcamp?

Data bootcamps are high-intensity programs with streamlined curriculums. As such, they tend to focus on a specific skill or a data science tool, like data visualization or SQL, and pack in as much hands-on experience in as little time as possible.

Most bootcamps last 6-8 weeks and provide enough practical experience to get you up and running.

There are two main types of data science bootcamps:

  • Onsite Bootcamps - These are held at training facilities or universities, and offer in-person classroom instruction. On-site bootcamps tend to be exercise-driven, as well as group and project-based, so they are very effective at simulating real-world applications of these topics. Programs in this setting tend to run a bit longer, around 12-16 weeks.
  • Virtual Bootcamps - Virtual camps tend to be high-intensity and deadline-driven. These are fast-paced (or sometimes self-paced), allowing you to finish the coursework in as little as 30 days.

You will also want to consider who is sponsoring these bootcamps. Bootcamps offered by universities tend to offer deeper instruction (and cost more) than independent bootcamps. Ultimately, you should focus on filtering for a bootcamp that offers the skill or skillset that you need to succeed.

How Is a Data Science Master’s Different?

Master’s degrees in data science are becoming increasingly common, with in-person and virtual offerings at universities around the world.

Data science master’s degrees offer some key advantages over bootcamps:

  • Broad domain knowledge - A master’s degree provides depth of learning. For example, a master’s program in data science might offer additional coursework in statistics, machine learning, data visualization, and data mining.
  • Instructor-led training - These courses are taught either in-person or virtually by university professors, with ultimately more individualized instruction.
  • Job placement and networking - Master’s students often have access to alumni networks, job fairs, and training seminars, which can help with gaining referrals to top data science jobs.

Key Differences in a Bootcamp vs Master’s Program

There are many factors to consider when comparing data science bootcamps and master’s degree programs. Here’s a breakdown of some of the most important differentiators:


Data science bootcamps run around $12,500, whereas master’s programs can average $48,000 in tuition alone. NOTE: Online master’s degree programs do narrow the cost gap, with an average cost of about $25,000.

Time Commitment

Bootcamps take around 12-16 weeks to complete intensive programs, with some single-skill bootcamps only requiring 6-8 weeks. Master’s programs, on the other hand, usually involve a minimum of 12 months of instruction but may take as many as three years.

Of course, more time spent in a master’s program does result in deeper domain knowledge, while the shorter timeline of a bootcamp may put you into a job faster.

Career Opportunity

Data science bootcamps will help you level up your skills in as little time as possible. They’re particularly well-suited for those in transition with their careers and professionals looking to enhance their data science credentials. The best bootcamps also offer career help and planning, and may have an established alumni network.

However, Master’s degrees seem to be becoming increasingly necessary for data science careers. Many FAANG companies require or strongly prefer a master’s to qualify for certain data science positions. Master’s programs do usually also have robust career networks and often include built-in segments for career planning and training.

Skills Improvement

Data science bootcamps have steep learning curves and are designed to provide you with enough skills and expertise to do the job in as little time as possible. Yet, after a bootcamp, you will have to continue learning.

One disadvantage of master’s programs is that you might not receive as much hands-on training in a particular tool or skill. You’ll likely need to supplement your coursework with independent learning on data science tools like Python, R, and SQL.

Bootcamp vs Masters: Which Is Right for You?

Choosing between a bootcamp and a master’s program isn’t an easy decision. But it can have important implications for your career. Therefore, you’ll want to weigh the pros and cons. Here are some considerations:

  • Bootcamps are great for reaching career goals. For example, if you want to learn a new programming language or skill, or you want to transition laterally to a career in data science, a bootcamp will help you get there faster.
  • Prepare for the time commitment. Bootcamps require all of your attention during the course session, so you may need to take those 12 weeks off of work to attend. Master’s programs, on the other hand, can be completed over 1-4 years, with a much more flexible schedule.
  • What are your long-term goals? A master’s degree is probably going to be more helpful in the long run for more senior-level roles and managerial positions. Bootcamps will help you land a job in data science, but to advance, you may need additional training and on-the-job experience.

Data Science Learning Resources

Take your data science career to the next level with these learning resources from Interview Query: