Dec 22, 2021in

Should You Join A Data Bootcamp? The FAQ Guide

Goal: Answer all of your questions on Your Data Bootcamp Decision. Have more questions? Reach me on twitter @dataindependent

Not a reader? Check out these 18 questions answered on Youtube

The decision to go to a data bootcamp is life changing.

But the choice is tough. You’ll have to come up with the five-figure tuition, invest your time (with opportunity cost), and likely transition careers. All three decisions deserve careful thought. On the other side, it could bring a big increase in salary, new skills, and a fulfilling career change.

I personally attended Galvanize Data Science Immersive in April ‘15 as one of their first cohorts. After Galvanize, I worked at a very large (+40K employees) San Francisco-based tech company. After that, I made the switch to a start-up as their first data hire to grow out their data practice.

The choice to go to a data bootcamp set me on a path that would open doors, be financially lucrative (2x my previous salary), and ultimately, be more fun and fulfilling than my previous career. Looking back on it, I would do it again 100/100 times.

This guide is about helping you make the choice for yourself. In life, you’re always presented with multiple paths. Your job is to look at each path, evaluate what is best for you, and move forward feeling confident with your decision.

My alliance in this article is with the student. Below are the top questions I hear from bootcamp prospectives. Each one is critical to contemplate when making your decision.

So should you do a Data bootcamp? Well, it depends on your goal, of course.

Should You Join A Data Bootcamp Questions:

  • How To Frame Your Decision
  • What Is A Data Bootcamp?
  • History Of Data Bootcamps
  • Should I Join A Bootcamp?
  • What Do You Actually Learn In A Data Bootcamp?
  • How Technical Is The Bootcamp?
  • Couldn’t I Learn All Of This On My Own For Free?
  • Should I Do A Masters Program? What About A MOOC?
  • What WON’T You Learn In A Data Bootcamp?
  • Is It Worth The Money?
  • How Hard Is It To Get A Job Afterward?
  • Will A Bootcamp Help Me Get A Job?
  • Should I Do A Remote Or In-Person Bootcamp?
  • Should I Do Part-Time Or Full-Time?
  • Should I Do An Income Share Agreement Or Pay Up Front?
  • What Comes After A Data Bootcamp?
  • As A Bootcamp Graduate, Has It Been Worth It?
  • Where Can I Find Out More? Who Can I Talk To?

How To Frame Your Decision

What Goes In, What Comes Out?

Here’s how I like to frame decisions: What do you put in vs what do you get out?

If the output of that equation looks (or doesn’t look) attractive to you, then you’ve found your answer. Usually the hard part is filling it out.

Throughout this post I’ll reference this framework to evaluate whether or not you should go to a bootcamp. You put in time and effort, both of which have opportunity cost, in hopes of happiness coming out.

What Is Your Goal?

Another saying I like is, “Completing goals is the easy part, figuring out goals is the hard part.” The same applies for a bootcamp, you need to really think about what you hope to get out of it.

Many people will tell you they want to “get into data” or “become better at data.” Both of those sound great to me, but what do they really mean?

When defining where you want to go make sure your goals are tangible. “I want to make $110K” or “I want to be able to automate a web scraper.”

What Is A Data Bootcamp?

“Data Bootcamp” is a slang word for a niche educational program that focuses on either data science, data analytics or data engineering. Programs can range from 12-24 weeks, full-time/part-time, and online or on-campus. Generally they are technical — meaning you’ll be coding.

Outside of bootcamps, other data programs including Massive Online Open Courses (MOOCs) and university led certificate or masters programs.

This post is intentionally program agnostic, but for sake of tangibility, here are a few examples

Data Bootcamps – Galvanize Data Science Intensive, Metis Data Science Bootcamp, Data Science Dojo

Data Certificates – Coursera, Berkeley Certificate

Data Masters (Non-University Led) – Galvanize Data Science Masters

Data Masters (University Led) – Harvard, Stanford

The most traditional example of a Data Bootcamp is the 15-30 student, 12-week, full-time, in-person experience.

History Of Data Bootcamps

General Technology Bootcamps have been around for a while. However, the first ones focused on mobile development (mobile apps), front-end (websites), and full-stack development (web applications).

The first official instance of a Data Bootcamp was Zipfian Academy (now rebranded) started in late ‘13 – early ‘14. This was acquired by Galvanize in November of ‘14. Shortly after Metis came along.

Since then 100s of other programs have jumped into the market. General Assembly and Berkeley Data Science Bootcamp come to mind.

Should I Join A Bootcamp?

We’ve finally come to The Question.

I’m going to answer this example using the “What Goes In, What Comes Out?” Framework and tailor it specifically to the 12-week full-time bootcamps.

With bootcamps, you put in your time & money. Both of these have opportunity costs.

An opportunity cost is what you could have done in a different scenario. What else could you have done with the time you put in? What about the money?

First, what goes in

What You Put Into A Bootcamp:

  • ~$17K Tuition
  • 12 weeks of your time
  • Give up stability of your current job
  • Career-transition risk

What You Get From A Bootcamp:

  • New hard technical skill – Basic Python Coding
  • New hard technical skill – Basic Statistical Analysis
  • Brand name on your resume (we’ll talk why this may not mean as much as you think later)
  • Student connections/network
  • 12-weeks of “think-tank” like minded people who are on the same journey
  • Light career counseling (salary negotiations + job finding support)

Second, think about your goal. What are you trying to accomplish that you think a Data Bootcamp will help you accomplish? Make sure it’s tangible.

If your goal is more money, you might be able to do this with a Data Bootcamp. But it’ll be a long round-about way to get there.

If your goal is to ‘break into tech,’ well, there are a lot of ways to break into tech. Why pay $17K and leave your job to do it?

If your goal is to uplevel and improve yourself, there are also a lot of ways to do this. Why data?

Should you do a Data Bootcamp? Yes, but only if the expected benefit you get is worth the cost to complete your goal.

Make sure to think about your decision in terms of years, not weeks or months.

What Do You Actually Learn In A Data Bootcamp?

In a data bootcamp you’ll cover a little of a lot.

You only have 12 weeks (3 of which are usually for projects and job prep) to pick up python and learn a gamut of algorithms.

You’ll Learn:

  • Foundational Programming: Jupyter Notebooks, Python Programming Language, Data Analysis Libraries (Pandas, Numpy, Scikit Learn)
  • Foundational Stats: Distribution types, probability analysis
  • Foundational Machine Learning Algorithms: Classification, Clustering, Time-Series Analysis, Regressions, Decision Trees. You’ll cover 1) What each one is used for 2) tuning basic hyperparameters (editing how the algorithm works)

Keep in mind you will only get a surface level application understanding of these topics. You could take entire semesters on any single topic above.

How Technical Is The Bootcamp?

You’ll learn the basics of python coding. Python is the most common data analysis language for data folks. R is also used, but this leans towards academics.

If you’re intimidated, good. Learning a technical skill is challenging, but it is also what will set you apart from other candidates. Get excited that at the end of the bootcamp you’ll have a new superpower.

Couldn’t I Learn All Of This On My Own For Free?

Of course! Yes, all of this information is available online for free.

A very disciplined person could find this information online, make a learning schedule for themselves, and spend 12 weeks watching videos, reading articles, and coding on their own.

However, this isn’t the case for most people. Bootcamps require commitment and give you a learning environment that can’t be matched on your own.

As the Derek Sivers line goes “If more information was the answer, then we’d all be billionaires with perfect abs.”

Leaving your job and spending tuition (maybe even going in debt) will increase the “seriousness” of your choice and make you focus. There is nothing like your back up against a wall to help you learn data.

Jokes aside, yes you can learn it all for free online. However, I’ve yet to see anyone actually do this and change careers. The commitment of time/money actually increases what you get out of the program and ultimately your outcome.

Should I Do A Masters Program? A MOOC?

I answer this with the same “What Do You Put In?” Framework as above.

Masters Program:

What You Put In:

  • $50-$70K/year
  • 1-2 years of your time

What You Get Out:

  • Deep understanding of the technical and statistical sides of data
  • Increased “brand name” attached to your resume
  • Soft eligibility for more jobs. Employers like to see terms they recognize on your resume. “Masters” or “Berkeley” help get you through a gatekeeper


What You Put In:

  • <$5K? – Depends on the program or track you make for yourself
  • 30 weeks part-time? – Depends on your track

What You Get Out:

  • Coding abilities – If you code for 30 weeks part-time hopefully you gain some ability
  • Some stats skills
  • Certification that is lesser known

Everyone’s situation is different – do your cost benefit analysis and see which options are best for you.

What Won’t You Learn In A Data Bootcamp?

A bootcamp may seem like it’ll solve all of your skill gaps, but unfortunately it won’t. Here’s a list of things you won’t learn

  • Problem identification – Shortly after you start a job you’ll (hopefully) realize that data and data science is only a tool to solve business problems. I don’t care how good of a data person you are, if you’re working on the wrong thing then your efforts will be sub-optimal. Identifying problems is a skill you’ll need to learn. This is usually learned the hard way by working on the wrong thing a couple items.
    • Analogy: If data bootcamps were like wood working classes — they would teach you how to use a lathe, types of wood, etc, but identifying what your client wants and what you build is up to you.
  • Structured Query Language (SQL) – A mandatory skill for anyone trying to get into data. Simply put, if you’re a data person, you need data, and data sits in databases. In order to get it out you use SQL. Therefore, Data Person = SQL. This might not be a requirement in the future, but do you really want to be the person trying to get into data that doesn’t have SQL on their resume?
  • Engineering and Software Development Best Practices – If you don’t have a technical background, you likely don’t know how to correctly build your own tools. This is fine when you’re scrappy on your local computer. However, once you’re on a team and sharing resources with others, you’ll realize the gaps in your knowledge.
    • Analogy: Coming out of a bootcamp, you’ll know how to duct tape your analysis together, but once you join a team it’s better to code for others.
  • Data Modeling/ETL – An unsexy topic that is important. Data Modeling is how your data is structured — what columns, fields, subfields your tables have. Extract-Transform-Load (ETL) is the general process your data goes through from creation to analysis. Think of it as the data pipeline. As a data person, it’s important you know how your data is created. The data model and ETL process, though not your direct responsibility, is a must know.

Listing out items you won’t learn isn’t a jab at data bootcamps. There is already too much material to learn. The above topics (aside from SQL) are better learned on the job.

Is A Bootcamp Worth The Money?

Check out the “What Do You Put In?” Framework above to start answering this question. To further answer it, let’s talk numbers.

First, there are multiple parts of the salary package to consider. Galvanize says that the average data scientist makes $117K base salary, $24K annual bonus, $53K equity, and $15K signing bonus their first year. That’s $209K total pre-tax comp (My 2020 Opinion: The base seems a little low and benefits a little high).

Gusto says that’s $10,766/month in take home cash, or $129K/year. So a bootcamp will cost 13% of your first year’s take home salary. You may not be able to come up with the money right away, so look into income share agreements with your bootcamp or private loans.

To me, 13% is a lot, but I also like to think about this decision on a years timeline, not months.

Think about it this way, in 5 years will you remember the $17K you had to pay when you’re making ~$200K/year? Likely not. Will you remember the career change you made? Yes.

So in my opinion, yes, a bootcamp is worth the cash if (big if) you’re able to obtain the average numbers Galvanize mentions after you finish.

How Hard Is It To Get A Data Job After The Bootcamp?

If there is only one thing you take away from this post, please have it be this: Completing a data bootcamp will not guarantee you get a data job. I have seen sad stories of classmates who did not get a job for ~12 months after a bootcamp. You’ll need to work just as hard after the program as you did during the program to get a job.

The data market, while in high demand, has also become saturated with students and programs. There is a lot more supply.

Do the program for yourself, to learn, and to make an impact with your increased skills. Don’t look at it as a free pass to more money.

I would estimate that it’ll take the average person 3-4 months of full-time (8hrs/day) energy to get a single job offer.

For a video on the data job process check out Lessons Learned.

Will A Bootcamp Help Me Get A Job Afterward?

Yes, there are career services within most Bootcamps — resume help, application help, few network connections. Keep in mind that these bootcamps are processing 100s of students a year. They aren’t able to give personal attention to everyone.

Do not assume any future employer knows the name of your bootcamp, or really cares that you took one. They will care about YOU and what you can do for them. Not how you spent your last 12 weeks.

Think of a bootcamp as the prep process to the final show. You must be the one to get on stage and perform.

Should I Do A Remote Or In-Person Bootcamp?

My preference is for the in-person experience. I find that physically being surrounded by data nerds and a supportive environment helps people learn better.

However, given the current macro environment, or your personal situation, it’s not always possible to do in-person. This is fine and not a deal breaker. Whatever you choose, work hard.

Should I Do Part-Time Or Full-Time?

I’m a bit more opinionated on this front. It depends, but my preference is Full-Time.

If you’re trying to make a career change, then commit and go for it full-time. If you’re trying to learn a side-skill to improve your current toolkit, then do something on the side.

Doing full-time means you’ll need to leave your current job (and forego $). If this isn’t possible for you, part-time is ok. Again, whatever you choose, work hard.

Should I Do Income Deferred Or Pay Up Front?

Either option is fine. It’s a moot point in the long run.

But again, it depends on your situation. If you’re not able to come up with the cash to pay right away, then you can either 1) get a loan or 2) do income share.

Income shares generally go like this: Pay ~10% of your salary for the next 18 months up to $17K. Bootcamps prefer cash upfront (so they can use it), so you may have to pay more with income share.

Don’t forget opportunity cost: If you’re able to pay the $17K upfront, think about what else you could do with that money (like invest) if you held on to it for longer.

What Comes After A Data Bootcamp?

After a bootcamp you will (hopefully) have a job and start to see what data is like in the real world.

Spoiler Alert: It’s messier and slower than you thought.

‘Doing data analysis’ is only 25% of your job and the rest is corporate fluff, or having meetings about your data.

After the bootcamp you will start at an entry or mid level data analyst or data scientist job. It’s rare your first job is the one you’ll like so expect to transition within 24-36 months. This is ok and it happens. After industry experience, you’ll be an actual data person (vs trying to act like one) and it’ll be easier to get a job.

Data careers are horizontal — you interact with many other departments (marketing, sales, product, etc.) within a company. You’ll have the luxury of finding your favorite and potentially shifting over.

You’ll then move on to mentoring other students, maybe write a blog post. Other people will call you a “data person.” You’ll realize that data isn’t the end game anymore, it’s business impact and movement. You’ll need to decide if you want to go into data/analytics management, or get deeper technically. Then 5 years later you’ll write a guide for other people who are considering making the bootcamp jump.

As A Bootcamp Graduate, Has It Been Worth It?

Simply put, my bootcamp experience changed my life.

It up-leveled my skills from an Excel monkey to a semi-technical data analyst/scientist. I was able to successfully transition out of my previous career (finance) and into product, data, and growth.

If I could go back, the only thing I would do differently is write about my experience more.

Where Can I Find Out More? Who Can I Talk To?

Head over to Google and type in the search “ data scientist”

The part will scope your results to linkedin profiles, the data scientist part will show you people in the industry.

Message 20 of them, get on the phone with 5, and ask more questions.

Or if you want to chat – @dataindepedent