Data Science Bootcamps: Are They Worth It? (Explained!)

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This post may contain paid links to my personal recommendations that help to support the site!

If you are aspiring to be a data scientist, I’m pretty positive that you have heard of the prospects of Data Science Bootcamps and how they can help land you that first data job. Fret not, as I would be providing you with more information to better assess if these bootcamps are worth it.

The short answer is yes, but only when combined with experience. Many of such bootcamps focus on necessary data science skills in a short but intensive period but fail to lay out essential statistical foundations.

Why are they worth it, you say? Well, data science bootcamps can provide you with several skills and experiences that can enable you to stand out among other data science job applicants. Let’s look at some reasons why they are and aren’t worth it.

Why are Data Science Bootcamps Worth It?

1. Fast-paced Learning Style

After attending a data science bootcamp myself, I have to say that the learning style of the data science boot camp is arguably the best advantage the bootcamp can give.

Why? Bootcamps tends to have a strong focus practical applications, where things tend to be moving at a fast pace. This fast-paced environment places a similar pressure on the student or bootcamp attendee as that of in the office in the data science department. In data science, things move quickly, where collaborations, presentations and coding could all take place within the span of a few hours.

Although some might argue that having such a fast-paced learning style might not help you to sort out important foundations, a bootcamp can simulate an environment closest to the true data science working experience that hiring managers look for.

Bonus Tip: This added work experience is especially useful when applying for roles in startups! Startups, being small and fast-paced, have to be agile in their work and quick to implement new ideas. Now, does that ring a bell?Hiring managers in startups might have a second look at your resume if you do include a bootcamp or two in there.

2. Provides a Great Well-rounded Headstart

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As do most data science bootcamps, a capstone project is required for a bootcamp attendee to pass the course. This capstone project typically involves a culmination of all the data science skills acquired from the period of the bootcamp.

A capstone project ensures that the attendee is able to successfully understand the use cases for all the data science skills taught and apply them in a real-life scenario. Although the project might not have any actual business impact, having a Github portfolio with such projects might give the hiring manager more chances to assess your skill level.

3. Cost


Bootcamps are substantially cheaper than most other formal education options out there. University degrees and diplomas may range from $40,000/year to $70,000/year for up to 2 years. On the other hand, bootcamps can start from $20-30/month for online bootcamps and $5000-$50,000/course for physical lessons. In a financially tight situation, bootcamps would be a far cheaper option compared to formal data science/statistical education out there.

If you are a job-seeker looking to switch careers, I’m sure that you would like to pick the option that would involve less cost for such a transition.

Why are Data Science Bootcamps Not Worth It?

1. Lack of Depth of Knowledge

If there’s one thing that would stop people from attending bootcamps, it would be this – shallow understanding and foundations. Some might say that the bootcamp does not provide sufficient foundational concepts needed for advanced data science analysis.

I might have to agree with them partially too. Sure, a bootcamp can provide the attendee with practical skills to implement complex models and perform predictions. But at times, some work is required for data scientists and analysts to tweak and modify models for improvements. This is where the gap grows wider between formal education and bootcamp learning. Foundational statistical concepts are taught methodically in colleges, where can provide that extra edge to better improve a model.

2. A Strong Mathematical Background is Still Required

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Sometimes, the truth might be hard to take in but most hiring managers still look for data scientists with quantitative and mathematical backgrounds. Many data scientists believe that the bootcamps are meant for individuals with relevant technical backgrounds to transition into data science. What this means is that those looking to do a full transition over to data science from a non-technical degree might find it harder to clinch that data science job.

3. When Bootcamps Focus on Data Analytics Instead

Depending on the course content of a bootcamp, some might offer more training in data analytics as compared to data science. This might be a large difference for many, especially when the data scientist is paid much higher, at a starting average of $96k/year, according to Burtchworks.

These bootcamps might end up not teaching much about data science because of misleading marketing. You might not get what you intended to pay for, which is to specifically develop data science skills.

Relevant Questions

Who Should Go for Data Science Bootcamps?

1. Individuals with Prior Backgrounds Looking to Expand Their Skillset

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The people who can benefit the most from attending bootcamps are those who already have their foundations laid out through another formal course or degree. If you are one of them, and are looking to expand your data stack of skills, then bootcamps would really give you a well-rounded broad-based introduction to a variety of data skills.

2. Individuals who want a Good Supplement to their Data Science Resume

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As mentioned above, those who need that extra boost to their data science resume can actually take something back from a bootcamp through their capstone project.

Plus, having just that little extra project can help spark a passionate conversation with your interviewers!

Is a Data Science Bootcamp Enough to get into Data Science?

This is a question that I, too, have thought of before I set off on my data journey. Although I am a data analyst, I have learnt from many data scientists that a bootcamp alone is insufficient to be hired as a data scientist.

Data science is a complex field, with new models and algorithms being developed each day. With such a rapidly changing environment, foundational statistics and problem solving skills are the most important to ensure that you can stay afloat. Therefore, you should consider taking up more formal education courses like a Master’s degree or a diploma to complement the bootcamp.

Personally, I have used the combined experiences of a bootcamp as well as 3 other internships to apply for my current data analyst role. I would recommend looking out for such similar internship experiences to boost your chances of getting hired.

Should You Choose Formal Data Science Education Instead of a Bootcamp?

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A formal education in data science is still a rather new concept and not many have taken that path. Generally, formal education provides better structure and support for their students. If you require a well-structured course for learning data science and if you can afford the higher tuition fees, then this route should be for you.


Data science can be a complex field. Moreover, it is a rather new one. Therefore, bootcamps can only truly be worth it if you have some relevant technical experience or similar working experiences. If you do not belong to this category, that’s fine as well, but do not rely on just one bootcamp to develop your data science skills. Ultimately, everyone’s still on a learning journey and a data scientist is no different so press on and all the best in your data science journey.

My Favorite Data Learning Resources:

Here are some of the learning resources I’ve personally found to be useful as a data analyst and I hope you find them useful too. These may contain affiliate links and I earn a commission from them if you use them. However, I’d honestly recommend them to my juniors, friends, or even my family!

Recommended Online Course Provider: I find Coursera online courses the most well-structured and comprehensive! You can get a Coursera Plus Membership to get started here.

Using my link, you’ll only pay $1 for your first month (Offer ends 4 December 2021). I’d recommend using this to just get started, with just a small cost, and if you find that it’s not for you, you can always cancel before the next month!

Learning Data Analytics: I really like the Google Data Analytics Professional Certificate program made by Google, because of its credibility and focus on the skills required as a data analyst. You’d get the first month off of the subscription using my link!

Learning Tableau: Tableau is my main data visualization tool for work. I recommend going for Data Visualization with Tableau for an online course and Practical Tableau by Ryan Sleeper.

Learning Python: I’d recommend Learning Python for Data Analysis and Visualization for an online course and Python for Data Analysis as a resource book.

Learning Power BI: Power BI is a great tool I use for my personal projects and analysis for its lower cost. Getting Started with Power BI Desktop is a great online course to start with and Beginning Microsoft Power BI is a good book to accompany your learning.

Learning R: The Data Science: Foundations using R Specialization online course is real solid one you should check out. For books, I’d recommend Learning R.

Learning SQL: A good started course is Introduction to SQL from Datacamp and for books, SQL: The Ultimate Beginners Guide: Learn SQL Today should be a useful resource while you learn.

Learning Data Visualization: I personally think that the Big Book of Dashboards is an excellent book for reference when designing your dashboards, especially on Tableau.

To see all of my most up-to-date recommendations, check out this resource I’ve put together for you here.


A budding data analyst with great interest in writing all things about data!

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