Guide to Data Science Bootcamps

If you’re debating your options for your career in data science, have a look at our advice section below. It includes practical tips, interviews with DS3 and District Data Labs graduates, and a breakdown of the degree vs. bootcamp debate.

Looking for a quick way to compare programs? Check out our Mega List of Data Science Bootcamps. We cover everything you need to know – from location and education requirements, to job placement figures and curriculum details.

How They Work

Aim

Bootcamps are short, intense educational programs that are intended to get data professionals up to speed in critical data science skills and technologies. As a data scientist, you’ll often have to use a variety of tools and tricks (e.g. Hadoop) to make big data work for organizations. Bootcamps are intended to help you fill your knowledge gaps.

Skill Level

Traditionally, data science bootcamps (e.g. Metis, NYC Data Science Academy, and Zipfian/Galvanize) have been aimed at folks with experience in at least one object-oriented programming language – for example, mid-level data analysts/professionals or software engineers. These folks already have some skills in statistics, databases, math and business procedures.

Having said that, there are now camps for all levels of skill. For example, Microsoft’s DS3 summer school hopes to convert curious college students into data scientists. On the flip side of experience, the free Insight Health Data Science: Fellows Program is intended to help PhD or MD graduates transition to big data jobs at leading healthcare organizations.

To help you sift through the possibilities, we’ve divided our Mega List of Data Science Bootcamps into 3 levels: Beginner, Intermediate & Advanced.

Format

Bootcamps like to market themselves as unique. However, if you’re interested in intermediate-level bootcamps such as Metis and NYC Data Science Academy, you can generally expect 3 months of:

• Classes in fundamental data science principles (e.g. Python, Hadoop, machine learning, data visualization, etc.) followed by advanced topics
• Senior data scientists serving as teachers and mentors
• Real-world project work culminating in a capstone or professional portfolio
• Guest lectures/seminars, meet-ups, field trips and networking events
• Extensive career preparation, including job interviews and hiring fairs with partner companies

Location

Many bootcamps are clustered in technology centers such as Silicon Valley/San Francisco and New York City. But there are also plenty of European options (e.g. Berlin, Turin, London, and Dublin) and even satellite bootcamps in smaller U.S. cities. For example, Galvanize (formerly Zipfian Academy) has set up shop in Denver, Washington D.C. and Austin. District Data Labs caters to those in the D.C./Arlington, VA area.

In-Person vs. Online

In-person bootcamps have a huge number of advantages, especially when it comes to team-based projects and one-on-one mentoring. Three months spent hobnobbing with data science professionals? That’s a serious networking opportunity. Some experts even suggest that you avoid online bootcamps entirely unless you’re doing a refresher course.

On the other hand, technology is rapidly improving. Fellowships such as S2DS Virtual are doing their best to include online networking events and virtual meet-ups with partner companies.

Time Investment

Be ready to rumble – fellowships and immersive bootcamps such as Metis require a full-time commitment. It’s not just the courses that take up your time; it’s the meet-ups, the after-hours networking events and the field trips. If you can’t commit to a full-time schedule, we’ve listed a few part-time options in our Mega List of Data Science Bootcamps.

Price

For a 3-month, full-time immersive bootcamp, you could be looking at a fee of $14,000+. Thinking of applying for a fellowship? The fellowship may be free, but usually it doesn’t include accommodation and spending money. At the other end of the spectrum, some beginner courses are entirely free.

We’ve listed notes on scholarships and financial assistance in our Mega List of Data Science Bootcamps, but we may have missed opportunities. Always ask about financial options.

Education Requirements

Education requirements vary widely. More advanced bootcamps such as the Startup.ML Fellowship require knowledge of certain programming languages and/or an advanced degree in a quantitative science.

Admission Procedures

Getting into an intermediate bootcamp or a data science fellowship might be harder than you’d think. For example, candidates applying to Zipfian/Galvanize are expected to complete a take-home assignment and pass 2 technical interviews. Metis candidates are scored on a six-part rubric after a virtual interview. In return for its 100% rate of job placement, Data Science Europe only accepts around 10 applicants per cohort.

If you’re worried about your chances, you may want to consider completing online workshops or courses from vendors like Springboard and Thinkful first. This may:

1. Improve your skill level
2. Demonstrate your commitment

Established vs. New Programs

Established programs like Metis may have the backing of a big company like Kaplan, but you might prefer to network with Wall Street types at Bit Bootcamp. The age of the program matters much less than your personal/professional goals. If you’re torn between a new program and one with a long track record, you can use our section on what to look for in a bootcamp as your guide.

What to Look For

Instructor & Mentor Quality

Data science is skills-based, so you should be looking for instructors who have serious, hands-on experience in your preferred fields. A lot of bootcamps are taught by working data scientists; many in-depth camps mix academic staff with professionals. For example, Data Science Retreat has both university professors and start-up entrepreneurs on its staff. You can find biographies of instructors on most bootcamp websites.

Don’t forget to look at the quality of mentors and guest speakers – networking and career development are a big part of why you’re there. Bootcamps that take place in tech hotspots (e.g. Silicon Valley Data Academy) often host guest lectures from experts like Guy Kawasaki and Peter Thiel.

Cohort Make-Up

Because data science is a collaborative field, bootcamps love to focus on teamwork. That means you may be spending a lot of your time working – and arguing and socializing – with fellow students as you tackle course materials and portfolio projects. Examine the backgrounds of alumni (often listed on the bootcamp website). Do they come from diverse fields? Do they have cool education backgrounds? Do they have entrepreneurial spirits? Did they land interesting jobs? You’ll want to be in good company.

Curriculum Structure

Decide how much structure you want. Are you comfortable with self-paced learning? Or do you prefer deadlines, assignments, and homework? In some camps, you may have no choice – to make sure you’re prepared for a job, bootcamps at the beginner and intermediate levels often include mandatory lectures or courses in fundamentals (e.g. machine learning). But if you’re looking at advanced fellowships and camps, you’ll have more independence. The Insight Data Science fellowship is completely self-directed – no courses at all.

Portfolios & Products

For data science jobs, your portfolio is your real resume – employers want to know what you’ve done, why you did it and how it’s original. For example, Thinkful helps its Python learners develop an active GitHub portfolio; NYC Data Science Academy asks students to spend the last two weeks of camp on a capstone project. With the ASI Data Fellowship, you have the chance to work with industry partners on real-world problems. A strong bootcamp will help you build a portfolio of diverse data products.

Soft Skills

In addition to drilling you in data, Bootcamps should also be preparing you to work with real people on real business problems. Again and again, folks lose out on jobs because they can’t communicate with clients and collaborate with colleagues. Once you hit the job market, you should be fully prepared to lead an analytics team and explain your work to both technical and non-technical audiences.

Career Training

Many bootcamps have set themselves up as talent pipelines, funneling trained data scientists to eager partner companies. If you’re hoping to land a job after camp, look for programs that provide plenty of career training. For example, Metis asks you to take part in mock interviews, company site visits and consulting projects. General Assembly provides an in-house career coach and instruction in salary negotiations.

Job Placement Rates

Bootcamps love to advertise job placement rates (some boast rates of 100%), but these percentages are only part of the story. During your bootcamp’s Hiring Day or Career Fair, you may only receive job offers from the bootcamp’s partner companies. You may not end up in a “true” data scientist role. You may have to wait 3 months before receiving an offer. You may be starting at a lower median salary than you expected. Take a careful look at what job placement entails. Make sure the career they’re preparing you for is the career you want.

Post-Graduation Support

Just like universities, some bootcamps have gone the extra mile to provide alumni with post-graduation support. But always ask what this support is about. While you’re there, you can also ask whether the bootcamp has established an alumni network or a way for graduates to keep in touch.

Before You Commit

1. Establish Your Career Goals

Where do you want to be in 5 years? Do want to get a data science job straight out of bootcamp or do you just want a skill set that will allow you to work on independent projects? Knowing your goals will help you narrow your options. If you’re lost in a dead-end data role and not sure what your next step is, you might want to explore the uses of data science in different industries and check out our profiles of data science careers.

2. Contact Data Scientists in Your Chosen Field

(LinkedIn, Twitter, and meet-ups are a great place to start.) Ask them what they do during a normal day. Get recommendations on skill sets (e.g. R vs. Python). Talk to them about your education options. You may not need a bootcamp to land a job. A few months with Coursera or other MOOCs and a good textbook could just do the trick.

3. Research Requirements in Job Listings

But don’t take them too much to heart. Sometimes, HR departments just dump a huge list of requirements into the skills section and hope for the best. When in doubt, ask your mentors what knowledge is critical to have.

4. Ruthlessly Assess Your Skill Level

This will help when it comes to deciding whether you need a part-time course in Python (e.g. General Assembly), a crash course in Hadoop, or a full 3-month immersion in major data science technologies (Metis, NYC Data Science Academy, Zipfian/Galvanize, etc.). Think about soft skills too. Do you need practice developing your own projects and procedures? Do you need some experience leading a team?

5. Contact Bootcamp Graduates for an Honest Opinion

Many bootcamp organizations list their alumni on the website, but you can also do a LinkedIn and Twitter search. Ask for an inside take on coursework, instructors, job preparation and career support after graduation. You might also want to browse through our interviews.

6. Create a Budget

That $16,000 fee is just the beginning; you’ll also need to think about food, transport, lost wages and accommodation. In a place like San Francisco or New York City, housing can be downright painful. Some bootcamps offer merit and need-based scholarships; be sure to ask about options.

7. Draw Up a Shortlist

You can start with our Mega List of Data Science Bootcamps, but you may also want to do your own research. What kind of reputations do the instructors have? Can you commit to full-time? Some bootcamps are highly selective – what are your realistic chances of getting in?

Graduate Degrees vs. Bootcamps

Which One to Choose

Let’s say you have a bachelor’s degree in a quantitative science (or a related field) and you’re thinking of becoming a data scientist. You’ve done a few online courses (e.g. Coursera, Udemy, etc.) and are ready to invest in more education. You now have 3 options – bootcamp, master’s, or PhD in data science. Which one do you choose?

We don’t have the definitive answer. Unlike, say, medicine, there is no tried and true path to a career in data science. Some gurus hold a PhD in statistics and have built up an arsenal of data tools; others only have a B.S. and an incredible portfolio of projects. Hard-hitting entrepreneurs have created start-ups after minimal time in an academic setting.

Getting a Job

It’s also hard to give you firm statistics on degrees versus bootcamps in job placement. Because data science is a new field, some employers are still insisting that candidates hold a master’s or a PhD in a quantitative science. As of March 2017, data science professionals that hold an advanced degree earn higher base salaries. When it comes to top jobs, you may find it hard to compete with someone who has a graduate degree from Columbia and great networking contacts.

This situation is slowly changing – undergraduate degrees are popping up in data science and employers are recognizing that real-world experience is golden – so don’t be discouraged. As expectations shift, you may find that a bootcamp certificate and proof of your commitment (e.g. online coursework, independent projects, certifications, etc.) are all you need to persuade a company that you’re ready to roll.

Blow-by-Blow Comparison

Bootcamp

• Time Commitment: A few hours – 3 months
• Target Audience: Aimed at folks who want a data science job after graduation
• Instruction: Instructors often have industry experience designing real data science solutions
• Curriculum: Coursework is usually focused on applied skills & practical projects
• Interaction: Team-based projects and experiential learning
• Tip: Focus on mastering key skill sets; gaps in knowledge could be disastrous down the track

Master’s

• Time Commitment: 1.5 – 2 years
• Target Audience: Aimed at students interested in exploring the field
• Instruction: Professors may be a mix of academics with theoretical chops and industry professionals
• Curriculum: Coursework typically includes theory as well as applied skills
• Interaction: A mix of team-based learning and individual research
• Tip: Amass as much practical experience and job training as you can; the market is competitive

PhD

• Time Commitment: 4-7 years
• Target Audience: Aimed at true lovers of research and data challenges
• Instruction: Thesis supervisors are typically serious academics interested in complex problems
• Curriculum: Coursework is heavily focused on theory and personal research
• Interaction: Opportunity for teamwork depends on the thesis
• Tip: Be sure your thesis incorporates data analysis, programming, and practical experience

Interview with DS3 Graduate

Eiman Ahmed

Bootcamp: Microsoft Research Data Science Summer School (DS3)
Projects: https://ds3.research.microsoft.com/projects

“If you’re interested in becoming a researcher, especially in the line where you analyze and look at data, this program is amazing.”

Why did you choose to apply to DS3? What influenced your decision?

I applied because DS3 was for students in NYC and hosted by a company whose research and work I’ve adored since I was in middle school. One of my closest friends, Briana Vecchione, took part in the program in 2014. She recommended that I apply since I was looking to branch out in my knowledge of CS and do some other work besides software development.

What kind of data science skills and experience did you have before you started?

I had just completed my first year at Pace University, so I was a rising Sophomore. I had come in with some advanced placement credits from college, so I was at a Junior standing in terms of my CS core. I didn’t have any data science skills; I just knew how to program in Java.

What goals did you have for the bootcamp?

I really wanted to explore different fields within Computer Science, and I was especially intrigued by Data Science. From the bootcamp, I wanted to decide whether this was something I could see myself doing once I graduated from college. I think that now it is definitely an option for me.

What was the application process like? Were any parts difficult?

The application was online and required basic information. Applicants had to write an essay describing why they wanted to be a part of the program and how it could be of benefit to them. It was a very fair and simple process.

What was the coursework like?

Our mentors would either assign work to us, based on a guest lecture or their own lectures, or we’d complete some introductory tutorials on R, Python, or Git. Later, we’d go over the work as a class to make sure we all had a proper grasp on the subjects.

Here is the full coursework that we followed: https://github.com/msr-ds3/coursework.

Did you work on a portfolio or capstone project? What was it?

There were two final projects that we all worked on. Four students were placed on each project. My team project had to do with computing the flow of the NYC transit system and deriving more accurate estimates of local populations by time-of-day using MTA Subway Data and NYC Census Data.

Project link: https://ds3.research.microsoft.com/projects.

How would you judge the quality of the instructors and/or mentors?

Jake and Justin were always willing to lend us a helping hand whenever we needed it. They went over their lectures and the work we had to do thoroughly, and checked in a few times in-between while we were working to ask us if we needed any assistance. They were supportive of us throughout the entire process.

How many people were in your cohort? Was there a lot of diversity?

There were eight of us in 2015 and we all came from diverse backgrounds. Students had previous programming language, as I did, and were looking to make a difference. Everyone absolutely loved being a part of the program and learning every single day.

Were you guaranteed a job after graduation?

No, but Jake and Justin offered their full support in helping us with our resumes and writing recommendation letters for us. They still send us emails on different opportunities that they find to this day. Jake and Justin also aided us in writing abstracts for our projects so we could present at conferences such as CODE@MIT and the Tapia Conference.

Did your classmates find jobs?

I recently spoke to my friends from the camp – one of them has successfully landed the job and the others are interviewing for jobs right now as well. We all still speak and support each other with our goals and aspirations.

What are you up to now? Do you feel the bootcamp helped get you to this stage?

Right now, I am very focused on my schoolwork, growing my resume, and making the most out of my time in college. I will be interning this year at Microsoft as a part of the Explorer program – I’ll be exploring Software Engineering and Project Management more in-depth. I am still deciding what I want to do once I graduate, but the bootcamp really helped me explore a field I never thought I’d get into.

Would you recommend DS3? Who would be the best candidates for it?

I can’t say enough great things about DS3. To any students interested: PLEASE APPLY. You will not regret it.

I feel the best candidate would be anyone genuinely looking to make a difference in his or her community:

• Someone who goes beyond the extent of just doing coursework in school.
• Someone who may be working on a side project that is going to positively affect those around them.
• Someone that is extremely determined to do the best that they can.
• Someone who is passionate about Computer Science.
• Someone who is genuinely really interested in Data Science and willing to learn!

Do you have any other advice for folks interested in a data science bootcamp?

DO IT! I did and it is arguably the best decision I’ve made to this day.

Interview with DDL Graduate

Mehdi El-Amine

Bootcamp: District Data Labs – Data Science Incubator
Title: Data Analytics Manager
Company: www.circleback.com

“You can take all the online courses in the world – you can buy books, read blogs, do code exercises – but eventually you need to get your hands dirty with a real project.”

Why did you choose to apply to District Data Labs? What influenced your decision?

Location was a factor – the DC Metro area is not as rich in data science education offerings as, let’s say, the Bay Area or NYC. Also, the incubator was free of charge; you just had to submit an application and get selected. The format (mostly group self-directed) was a great fit for my schedule as a full-time professional, and it offered me the chance to do hands-on data science work outside of the paradigm of “class-based education.”

What kind of data science skills and experience did you have before you started?

I have a Bachelor’s in Computer Engineering. At the beginning of my career, I was a full stack .NET guy, with good knowledge of SQL databases. I’d done some amateur web development with the LAMP stack, too. Around the late 2000s, I did some cool stuff with OLAP databases (or, at least, it was cool in my head). So I did have a technical foundation, albeit a dated one.

However, in the few years leading up to the DDL Incubator program, I was far removed from all kinds of technical work. Eventually, I quit my job and some old professional connections helped me land a new gig as a Data Analyst. I had just been introduced to Python and I started using it at work, along with a lot of SQL and some exploratory data analysis work (the single-machine-csv-type, not the multi-node-cluster-hadoop type). From there, I put my hands into every data-related project I could find, at work or outside of it.

What goals did you have for the bootcamp?

Most of all, I wanted to do some hands-on data science in the “wild.” You can take all the online courses in the world – you can buy books, read blogs, do code exercises – but eventually you need to get your hands dirty with a real project or your learning will be incomplete.

Deep down, I knew this. While I was taking online courses, I would think to myself: “this thing I’m working on is very directed; if someone were to come to me and hand me the problem statement with no additional instructions, I wouldn’t know where to start, where to get data, what modeling technique to use, etc.”

What was the application process like? Were any parts difficult?

You would go to Tony’s office (Tony Ojeda is the DDL founder), he’d give you one ingredient, and you had to make a meal out of it in under 20 minutes. Then you serve the meal in 3 plates, and you juggle the 3 plates while riding a bicycle.

Okay, maybe that’s not entirely accurate. You don’t have to ride a bicycle. The application process is actually a form with some questions – some more elaborate than others – but I don’t recall anything being especially difficult.

What was the coursework like?

The incubator program did not involve much coursework, if any. There were one or two tutorial-like sessions at the very beginning on the expectations of the program – how to report on progress, the schedule of deliverables, and dealing with the github repo. And there was instruction on how to build a data product.

But the act of skill-building was mostly your own responsibility; you could reach out to staff for guidance or help whenever you needed it. I ended up learning a ton of new skills, a lot of things that were intangible – yet crucial – to managing and executing a data project.

Did you work on a portfolio or capstone project? What was it?

The program was project-based. A few sponsoring organizations came in Week 1 and presented projects that we could work on. We were split in teams and each team would choose one of the projects, or come up with their own.

If you did work on a sponsored project, you were able to collaborate – to some degree – with folks at the organization itself (obtaining data, understanding the knowledge domain, getting direction on which questions to try to answer, etc.).

My team and I chose to work on a project by the Bureau of Labor Statistics. They proposed some work on identifying geographical regions with significant demand for (or supply of) particular job skills. But I decided that we should do something bigger.

Mind you, I had no data science skills, but that did not deter my ego; I somehow convinced my team that we could figure out how to forecast national employment growth, using BLS historical labor data. The idea was to take some economic variables such as the employment-population ratio, or labor participation rate, throw them into a multi-variate regression and forecast the monthly unemployment rate.

How would you judge the quality of the instructors and/or mentors?

Excellent. Benjamin Bengfort was the master data scientist with all the answers to both low-level questions (what library to use? how to scale this or that, etc.) and high-level ones (Is linear regression the right approach? are these findings useful?).

Laura Lorenz helped us engineer better code, structure it, and design a proper data product. She also made sure we stayed on track every 2 weeks with our deliverables, which was definitely needed!

What kind of career training and job preparation did the bootcamp provide?

Even though the program wasn’t structured as a training program, it still provided a huge amount of learning that was directly applicable to the professional work environment. Executing a data project and putting together a data product is a different beast from the standard exercise of “get some data, get an ML library, get predictions.”

In no particular order, you need to:

• Figure out the right questions to ask
• Break the work down into tasks
• Schedule and manage the tasks between yourself and your teammates
• Automate data retrieval and wrangling
• Test and validate your model(s)
• Deploy your model(s) into a production-type environment
• Build a web front (app, website, api) for others to consume data from your model(s)

We worked on all of these pieces over the course of the project.

How many people were in your cohort? Was there a lot of diversity?

Off the top of my head, the number was somewhere between 30 and 35 people, split roughly 60% male and 40% female. The folks in my cohort had different, but complementary, professional skills; so many of the projects presented at the end of the incubator were damn impressive! My own team was composed of an African American, an Indian, and a Middle Eastern Arab.

Were you guaranteed a job after graduation?

No, but I heard one or two people were planning to change jobs after the bootcamp. I wouldn’t be surprised if they did.

What are you up to now? Do you feel the bootcamp helped get you to this stage?

I am still with CircleBack, working on new and cool data projects. The bootcamp helped me almost immediately; I now make use of several tools I picked up (IPython notebook, pandas, matplotlib). I evaluate the performance and accuracy of data models, which I did not know how to do before the bootcamp. And I also interact with a lot of data APIs (our own or third-party vendors), which is something I got better at after working on the BLS project.

Would you recommend DDL? Who would be the best candidates for it?

Absolutely. The incubator itself is invaluable, but plenty of DDL’s standard workshop-type course offerings are also highly educational, too. If you were to apply to the incubator program, I would say you need:

1. A motivation to learn
2. One technical skill, which could be anything (SQL, or R, or statistics)

Do you have any other advice for folks interested in a data science bootcamp?

Don’t rely on being fed the information; you have to want to figure it out yourself. It doesn’t matter what the tool is – Python, R, or other – the most important skills you’ll learn are centered around data science “thinking.” Is this a good training set? Am I overfitting? How would I interpret the predictions of this model?

Finally, choose to work on something small and narrow-focused; otherwise, you won’t be able to get all the data you need, or figure out which features are relevant, or have useful results. The big questions can only be answered after the small questions.