Over the past year, I have had the opportunity to speak with a lot of prospective Data Science bootcamp students sharing my pre and post bootcamp experiences and helping them put in context some of the major factors they need to consider before deciding to attend a Data Science bootcamp. This post is a summary to shed some light on some of those thoughts I've shared privately with prospective Data Science bootcamp students, things they should look for and things to look out for.
The list below may not be encompassing as each prospective Data Science bootcamp student is unique in their own way and what they hope to get out of the experience.
Do keep in mind this list was put together for those considering full time Data Science bootcamp programs.
Without further ado.. here we go
Background : Data Science is a hybrid role. Having a background with the right mix of Quantitative skills, Programming, Statistics, Math, Business Acumen, Databases, and Machine Learning would probably work in your favor. 6 or 12 weeks is a very short time to learn these things from scratch.
Also, having a good background improves your chances of getting into one of these Data Science bootcamp programs. I hear they're getting quite competitive these days.
Cohort Makeup : At most bootcamps part of your learning comes from lectures and interactions with the instructors, TA's and guest speakers. The other half comes from working and collaborating with your cohort mates on the course materials and projects. It is important that a cohort have people with a diversity of past educational / professional experiences. Your cohort mates will become your friends, co-workers, collaborators and maybe even co-founders.
Placement Rate : This is a really interesting one. A bootcamp with 100% placement may not always be the best choice. I've heard some bootcamps drop students who they feel may not be able to find a job and don't include them in the numbers. Prospective students have to dig deeper on the placement rates and ask the following questions:
Hiring Day : As as far I know, most of the Data Science bootcamps have a hiring day event where students get an opportunity to present their capstone projects to potential employers and "speed date" with those employers. Some Data Science bootcamps have exclusive hiring events with employers in their Hiring Network or guest lectures and presentation from companies that might be looking for talent.
Cost : This could be major factor in deciding to attend a bootcamp. Data Science Bootcamp program tuition range from free to $16,000. There might also be other costs like room and board, incidentals, relocation and lost wages. What this mix looks like will be different for each prospective student.
One way to look at cost is that this a short term investment for a chance to break into a new career.
A good amount of the material you need to learn to become a Data Scientist is free and available on the internet. Some students that get admitted to these Data Science bootcamps could have chosen to lock themselves in a room for 6 months and study all this material and then emerge having learned all the material / skills required to be able to land a job.
This is entirely possible but you lose out on all the intangibles you get from attending an in-person Data Science bootcamp - mentoring from instructors/guest lecturers, structured learning, motivation, positive reinforcement, collaborating with cohort mates, networking, getting a different view on approaching and solving problems, etc. What these intangibles are worth / could be worth down the line should be carefully evaluated and added to the cost equation.
Interview Prep / Soft skills / Business Acumen : It is important to know how much time the Data Science bootcamp spends on soft skills, interviewing and white boarding. Most job interviews you go to may require you to work through programming problems, communicate the results of an analysis you may have worked on to a technical / non-technical audience, working through a modeling case study, etc.
These are skills you get better at with practice. Some Data Science bootcamps weave this in as part of the curriculum so the students are more comfortable with this by the end of the program whereas others may reserve time towards the last few weeks of the program to work on these.
Curriculum : It is difficult to learn everything you need to become a Data Scientist in 6 or 12 weeks. You want to look for a program that will give you enough breadth and depth and a good enough foundation to start and build a career in this field.
Location : Majority of the Data Science bootcamp programs are based either in the Bay Area, New York or scattered through Europe (London, Dublin, Berlin) and most graduates end up working in those places. I've seen some setup shop in other tech metros like Boston, Seattle and Denver.
Contact Alumni : There is a lot of information to be gleaned from talking to past students of Data Science bootcamp programs you're considering. You'll get a raw and unfiltered view of their experience.
Projects : You should look for programs that will enable you work on variety of projects with small, medium and large data. This way you'll have a broad range of experiences and a portfolio of interesting projects or analysis to talk about once you hit the interview trail.
In-Person vs Online : It is very difficult to replicate the collaborative environment of a full time in-person Data Science bootcamp in an online setting. Assuming there are no other extenuating factors, choosing a full time in-person bootcamp should be the preferred option.
Established vs New Programs : This is actually one of the most frequent questions I get. Prospective students are usually torn between going with a more established program which has gone through several cohorts and has established a track record versus a new and upcoming program which may have gone through one or two cohorts or is just getting started.
Prospective students need to evaluate Data Science bootcamp programs they're considering on their merits and the factors that are actually most important to the student. There are advantages going with either a more established program or a much newer one. The prospective student needs to do some introspecting after which the path they have to take becomes very obvious.
To keep things in context, none of these Data Science bootcamp programs existed 3 years ago.
Alumni Network : Generally, this is a perk. Going to a bootcamp with a strong alum network could sometimes make the difference. You could be exposed to opportunities and / or jobs that you may not otherwise have access to. Having access to an active and collaborative alum network is worth its weight in gold (or whatever precious metal you prefer)
Outcomes : Students going through Data Science bootcamps usually have different goals. For most, it's probably getting a Data Science / Machine Learning focused job. For others it could be gaining a skill set that'll enable them work on their own ideas, break into the industry and / or move up the ladder in their current job. Whatever those goals are, going with a bootcamp that can work with you or even personalize some of the curriculum to ensure you're getting the best value for your time and money would be most ideal.
As far as customizing the curriculum, some of the bootcamps with smaller cohorts will have a much easier time doing this.
These are outcome based programs so you should go with a program that'll give you the best chance of finishing with a positive outcome whatever that may be.
As with programming bootcamps, Data Science bootcamps are now becoming commoditized. Some of these Data Science bootcamps consider themselves Post Doctoral training programs while others want to own a different segment of the market.
If you're considering a Data Science bootcamp program , do go through this list and then pick the program that is the best fit and will deliver the highest delta / value for you.
Hopefully, this blog post will help start the conversation.
The list below may not be encompassing as each prospective Data Science bootcamp student is unique in their own way and what they hope to get out of the experience.
Do keep in mind this list was put together for those considering full time Data Science bootcamp programs.
Without further ado.. here we go
Background : Data Science is a hybrid role. Having a background with the right mix of Quantitative skills, Programming, Statistics, Math, Business Acumen, Databases, and Machine Learning would probably work in your favor. 6 or 12 weeks is a very short time to learn these things from scratch.
Also, having a good background improves your chances of getting into one of these Data Science bootcamp programs. I hear they're getting quite competitive these days.
Cohort Makeup : At most bootcamps part of your learning comes from lectures and interactions with the instructors, TA's and guest speakers. The other half comes from working and collaborating with your cohort mates on the course materials and projects. It is important that a cohort have people with a diversity of past educational / professional experiences. Your cohort mates will become your friends, co-workers, collaborators and maybe even co-founders.
Placement Rate : This is a really interesting one. A bootcamp with 100% placement may not always be the best choice. I've heard some bootcamps drop students who they feel may not be able to find a job and don't include them in the numbers. Prospective students have to dig deeper on the placement rates and ask the following questions:
- Percent of students placed in actual Data Science roles
- Percent of students placed within one month or three months of finishing the program
- Percent of students placed through Hiring Day
- Percent of students placed through an introduction that the bootcamp made
- Percent of students actually looking for a job post bootcamp
- Median Salaries for students placed
- Salary Range for students placed
Going through a Data Science bootcamp is definitely not a silver bullet. There are a lot of people that go through these programs and still end up with non-optimal outcomes.
Hiring Day : As as far I know, most of the Data Science bootcamps have a hiring day event where students get an opportunity to present their capstone projects to potential employers and "speed date" with those employers. Some Data Science bootcamps have exclusive hiring events with employers in their Hiring Network or guest lectures and presentation from companies that might be looking for talent.
Cost : This could be major factor in deciding to attend a bootcamp. Data Science Bootcamp program tuition range from free to $16,000. There might also be other costs like room and board, incidentals, relocation and lost wages. What this mix looks like will be different for each prospective student.
One way to look at cost is that this a short term investment for a chance to break into a new career.
A good amount of the material you need to learn to become a Data Scientist is free and available on the internet. Some students that get admitted to these Data Science bootcamps could have chosen to lock themselves in a room for 6 months and study all this material and then emerge having learned all the material / skills required to be able to land a job.
This is entirely possible but you lose out on all the intangibles you get from attending an in-person Data Science bootcamp - mentoring from instructors/guest lecturers, structured learning, motivation, positive reinforcement, collaborating with cohort mates, networking, getting a different view on approaching and solving problems, etc. What these intangibles are worth / could be worth down the line should be carefully evaluated and added to the cost equation.
Interview Prep / Soft skills / Business Acumen : It is important to know how much time the Data Science bootcamp spends on soft skills, interviewing and white boarding. Most job interviews you go to may require you to work through programming problems, communicate the results of an analysis you may have worked on to a technical / non-technical audience, working through a modeling case study, etc.
These are skills you get better at with practice. Some Data Science bootcamps weave this in as part of the curriculum so the students are more comfortable with this by the end of the program whereas others may reserve time towards the last few weeks of the program to work on these.
Curriculum : It is difficult to learn everything you need to become a Data Scientist in 6 or 12 weeks. You want to look for a program that will give you enough breadth and depth and a good enough foundation to start and build a career in this field.
Location : Majority of the Data Science bootcamp programs are based either in the Bay Area, New York or scattered through Europe (London, Dublin, Berlin) and most graduates end up working in those places. I've seen some setup shop in other tech metros like Boston, Seattle and Denver.
Contact Alumni : There is a lot of information to be gleaned from talking to past students of Data Science bootcamp programs you're considering. You'll get a raw and unfiltered view of their experience.
Projects : You should look for programs that will enable you work on variety of projects with small, medium and large data. This way you'll have a broad range of experiences and a portfolio of interesting projects or analysis to talk about once you hit the interview trail.
In-Person vs Online : It is very difficult to replicate the collaborative environment of a full time in-person Data Science bootcamp in an online setting. Assuming there are no other extenuating factors, choosing a full time in-person bootcamp should be the preferred option.
Established vs New Programs : This is actually one of the most frequent questions I get. Prospective students are usually torn between going with a more established program which has gone through several cohorts and has established a track record versus a new and upcoming program which may have gone through one or two cohorts or is just getting started.
Prospective students need to evaluate Data Science bootcamp programs they're considering on their merits and the factors that are actually most important to the student. There are advantages going with either a more established program or a much newer one. The prospective student needs to do some introspecting after which the path they have to take becomes very obvious.
To keep things in context, none of these Data Science bootcamp programs existed 3 years ago.
Alumni Network : Generally, this is a perk. Going to a bootcamp with a strong alum network could sometimes make the difference. You could be exposed to opportunities and / or jobs that you may not otherwise have access to. Having access to an active and collaborative alum network is worth its weight in gold (or whatever precious metal you prefer)
As far as customizing the curriculum, some of the bootcamps with smaller cohorts will have a much easier time doing this.
These are outcome based programs so you should go with a program that'll give you the best chance of finishing with a positive outcome whatever that may be.
As with programming bootcamps, Data Science bootcamps are now becoming commoditized. Some of these Data Science bootcamps consider themselves Post Doctoral training programs while others want to own a different segment of the market.
If you're considering a Data Science bootcamp program , do go through this list and then pick the program that is the best fit and will deliver the highest delta / value for you.
Hopefully, this blog post will help start the conversation.
Ike, great post! In addition to having the opportunity to work on projects with a variety of datasets, we believe it's critically important that these projects be real-world industry problems and not just toy exercises. The level of learning that happens through programs that work with actual customers, far exceeds theoretical programs.
ReplyDeleteAnother huge advantage is that such programs are essentially industry financed and do not require any student tuition.
We've structured Startup.ML's fellowship program based on these principles. Curious to hear if there are any others.
I think Startup.ML is taking a pretty interesting and unique approach to data science education. A lot of the other programs have their selling points, but Startup.ML is combining opportunity to work on real problems and also build a potent network.
Deleteis it also available for international students ??
ReplyDelete