7 or so weeks of slow incline followed by 2 and a bit weeks of racing through technique after technique.

The layout of this unit seems problematic.

It starts out with clustering to compare it to classification methods, then goes back to classification algorithms (bagging and boosting) and then moves forward to NLP and naive Bayes (which are probably in the correct order) and then forward to MapReduce (which is really helpful for text related tasks) before ending up in time series analysis land.

The ARIMA model is complex, certainly.

But I think it should’ve been introduced much earlier (businesses really care about forecasting).

Either way, it definitely doesn’t belong in a module focussed on machine learning.

Each of these things are so different on the surface that there’s just no way the proper attention is given to each of them and how they vary.

Again this unit seems to make the error of mixing conceptual ideas with syntactical ideas and it seems as though Hadoop has been crowbarred in to this module.

Unit 5: Advanced Topics and TrendsThe final module covers recommender systems, neural network basics, multi-arm bandits and ends up focussing on portfolio and interview prep.

The project for the unit has students make a public presentation on how they used all of the techniques of the bootcamp to analyse a dataset they find or scrape.

Unit reviewThe unit starts off with neural networks and backpropagation.

It doesn’t say if things like dropout, regularisation, data transformation or hyper-parameter tuning are also covered but I imagine not as deep learning is probably worthy of its own bootcamp.

It also seems as though recurrent and convolutional nets aren’t covered.

The recommender system section says that students will learn to build a basic recommendation engine, which I can only assume means that deep learning is not involved and that matrix transformations and Euclidean distances are used instead.

That is a little bit of a strange mix.

Finally we get into the work-related stuff.

It states that the career coaches will help you polish your portfolio which, as far as I can see, contains only 1 or 2 projects that would be unique to you.

You finish by going through some practice technical interviews (which is very helpful).

General Assembly Bootcamp reviewWhile this bootcamp may rush through a lot of the topics, having exposure to them is beneficial.

I think that the first few modules take less time than the last two (and I hope I’m right) which helps balance things out a little.

The biggest problems with this bootcamp are:The few opportunities to build a portfolio different from everyone else’s.

The introduction of key topics syntax-first instead of idea-first.

The end-heavy approach to preparing students for interviews.

If you’re going to take this bootcamp, I’d suggest:Making plenty of time outside of the course to do projects that interest you.

Reading up on the ideas behind the syntax (and not being afraid to ask why when your instructor tells you something).

Having family and friends give you mock interviews (this would really help you explain what you’ve just learnt to do in simple terms).

The Flatiron School Data Science BootcampThis is the link to the Flatiron bootcamp.

This is a 15-week course that costs £12,500This data science bootcamp pitches itself as the most comprehensive course.

Like the GA bootcamp there’s a big focus on career development and Flatiron also offer one-on-one career advice and practice interviews.

The Flatiron course run for 50 hours a week, which means 10 hour days everyday, from 9–7.

Module A: Exploratory Data Analysis and Descriptive StatisticsThis module covers the basics of Python, NumPy, Pandas and SQL.

It also includes Matplotlib and an explanation of Linear Regression.

The projects in this bootcamp seem to all be at the end of the course in their own module — Module E.

Module ReviewThis 3 week long module is about the right length to cover the topics needed.

I’m glad that it covers SQL, something ominously omitted by General Assembly, but it doesn’t include anything about git or basic UNIX commands.

It’s not clear how you are introduced to Linear Regression, perhaps (though this may be wishful thinking) you are taught about the normal equation and you compute the line of best fit using matrix transformations in NumPy.

This module seems like a fine introduction to the technical skills you’ll need in the rest of the course but the lack of git training is a little disconcerting.

Module B: Advanced Data Retrieval and Statistical AnalysisIn this module you learn about gradient descent as it applies to Linear Regression, you also learn about Bayes’ theorem (I wonder if they teach the maximum likelihood approach to regression?).

After that you study XML (of all things) and JSON, do some web scraping, and learn about experiment design for A/B testing a website.

Module ReviewIt is just me or does Advanced Data Retrieval sound like you’re going to dive into the ocean looking for a lost aircraft’s black box?I like the fact that this bootcamp introduces Bayes so early.

However, again I’m concerned that the ideas will be lost on students coming to grips with XPath and querying JSON strings.

The project for this module, A/B testing a website, isn’t quite data science but it is a useful thing to do and to have in a portfolio (assuming its done well).

Module C: Models for Machine LearningIn this module (they’re all 3 weeks long) you’ll learn about Logistic Regression, Support Vector Machine and Decision Trees, dimensionality reduction (and Principal Component Analysis), k-means Clustering, and time series modelling.

Module ReviewThere’s a theme developing here.

Both Flatiron and General Assembly take a long time to introduce linear regression and then rush through as many different algorithms as possible.

Because of the layout of the course, we know that Flatiron teaches Logistic Regression, SVMs, PCA, XGBoost, k-means, and time series modelling at the same speed that they cover Linear Regression and Bayesian statistics.

There’s just no way they’re explaining these techniques in enough detail.

Also, there seems to be no opportunity to use these different techniques in projects as you go along.

The syllabus does mention that each day you’ll do some pair programming (an odd choice for data science), but it’s not clear if these are just exams / problem sets rather than actual projects.

Module D: Advanced Topics, Big Data and Deep LearningIn this module you’ll learn Regular Expressions (!?), PySpark, deep neural nets, convolutional neural nets and recurrent neural nets.

Module ReviewThe inclusion of regular expression here is directly related to that fact that the course makes no introduction to UNIX skills the way the General Assembly course does.

For the most part I agree with the pace and timing of this module.

However, the inclusion of convolutional nets is a little surprising as everything else in the module is about text data (including recurrent neural nets).

Seeing as they cover quite a lot of advanced topics, and knowing what they’ve taught students before this module, I’m finding it hard to see how people can really make the jump to understanding deep learning properly.

Module E: Advanced Data Science ProjectThese last three weeks of the bootcamp are dedicated to a pair project (Flatiron really like the pair programming thing).

You pitch your instructor a few different projects that you’d like to work on and the instructor picks one.

Module ReviewOkay, so finally there’s something to do that can go on your portfolio.

I’m not sure about the paired aspect (it’s hard to split work evenly between two people) but at least there’s a considerable amount of time to complete the project.

Because this comes right after the module on deep learning, I wouldn’t be surprised if a very high proportion of these projects were deep learning related.

But there’s a problem with that — businesses like results they can understand.

Flatiron School Bootcamp ReviewThis bootcamp started out strong and at the right pace.

The inclusion of SQL is very good, but it shouldn’t come at the expense of git and UNIX commands.

Flatiron’s course, like GA’s, is very squashed in the middle.

Knowing Linear Regression and gradient descent in-depth is not enough — they shouldn’t just pile on the algorithms without sufficient explanation.

Deep learning is very popular, but I question whether it should be taught right before the projects.

In most day-to-day work, data scientists build much simpler models and a good portfolio should reflect that.

The biggest problems with this bootcamp are:Very little mention of projects and an over-reliance on pair programming (nobody wants to have to carry someone else through the course).

There’s nothing in the modules that talks about what businesses need from data scientists and the career coaching seems to be a bit looser than GA’s where it was built into the end of the course.

I’m not confident that the portfolio you’d be left with after this course would really help you secure a job.

If you’re going to take this bootcamp, I’d suggest you:Go through everything by yourself after class has ended.

Don’t just assume that your partner knows what they’re doing.

Stretch out the middle by looking into all these different algorithms until you understand in which instances they work and why.

Ask the instructors a lot of questions about the applications of what you’re learning to real business problems, so that you can build a portfolio for yourself outside of the course.

The Data Science Dojo BootcampThis is the link to the Data Science Dojo course.

The actual bootcamp section of this course runs for 5 days and costs an unknown amount of money (if any knows, I’d appreciate you telling me.

)I wouldn’t consider the offering from the The Data Science Dojo to be a bootcamp the way people traditionally think of them.

This is more of an industrial training programme that aims to improve the skills of employees within a business.

They list some very impressive companies as past clients including Apple, Google, Amazon and Microsoft and they have a wealth of good reviews.

Unfortunately, I can’t go in depth into the syllabus and I can’t find one.

Regardless, I think it’s safe to say that this isn’t your average data science bootcamp and if you’re looking to change careers, you might want to consider a different programme.

The Ironhack Data Analytics BootcampThis is the link to the Ironhack Data Analytics course.

This 9 week course costs 7,000 euros (available in Europe and the US).

It’s called a data analytics bootcamp but covers much of the same material as the other courses, so it makes sense to include it here.

The primary market for Ironhack bootcamps is career changers.

Their marketing website is tailored for those wishing to change jobs and they seem to offer on-going career support after the course has finished which is a nice touch.

The course runs from 9–6 (with optional evening events).

PreworkHaving students improve their fundamental skills before the course can only help ensure that time in class is well spent.

The prework component covers SQL, git, Python and descriptive statistics, so it’s a good blend of both General Assembly’s and Flatiron School’s courses.

Plus, you don’t have to learn these things in person (they’re delivered online) which saves you money.

Module 1: Introduction to Data Analytics and Data EngineeringIn the first 3-week module you’ll go repeat the prework by learning more about git and MySQL.

You’ll also start to explore pandas for data wrangling and get used to working with APIs.

There’s no mention of a project for this module in their syllabus.

Module ReviewAlas, the promise of everyone being up to speed with the basics on the first day of the course wasn’t true.

I would be annoyed if I sat through 60 hours of online video (that’s how much they claim to have in the prework package) and then turn up in class to find that I’m going through that stuff again.

These are important skills but why make everyone go through that effort just to cover the stuff again?Another issue with this first module is that it’s all syntax.

I can’t see anything related to statistics in the syllabus, which is a concern because Ironhack’s offering is the shortest in length of the in-person bootcamps.

Module 2: Advanced Data Analytics (a Deep Dive)In this three week module you’ll learn about using Python for inferential statistics, you’ll do more work in Pandas, learn about Matplotlib and all the types of charts you can make with it and start ‘storytelling’ with data.

Module ReviewOkay, the syllabus for this module reiterates the fact that this is a data analytics bootcamp more than it is a data science bootcamp.

After 6 weeks you haven’t gotten very far in terms of predictive capabilities (there’s been no forecasting or linear regression).

Given that the students were supposed to learn about descriptive statistics before the course started, leaving inferential statistics until weeks 4–6 makes this course seem very slow.

Module 3: Advanced Data Engineering (Fundamentals of Machine Learning)These guys are just keyword padding their module titles.

In this module you learn about supervised vs unsupervised learning, get an in-depth intro to scikit-learn and do some feature engineering.

Again, there’s no mentions of project here even though this is the last module!Module reviewThe name of this module is just wrong.

But there are things I like about this part of the course.

For one, Ironhack haven’t jumped on the deep learning gravy train.

Secondly, they end the bootcamp with 3 weeks of exploring the algorithms that data scientists really use each and every day.

Like Flatiron School’s data science bootcamp, there’s no explicit mention of career guidance in the course syllabus itself so I’m not sure how that works or when it’s delivered.

Ironhack Data Analytics Bootcamp reviewFor something so focussed on delivering value to career changers, there’s a remarkable lack of information in the syllabus about how they help you to change careers.

Ironhack make no claims to be teaching you the in-depth mathematical intuition behind data science (as the others do) and so I don’t have such a big problem with their omissions here.

The biggest problems with this bootcamp are:I can’t find anything about projects in their syllabus.

If you don’t get a portfolio out of the bootcamp, what’s the point?Going over everything from the prework again would annoy the hell out of me.

They don’t break down the precise algorithms you learn about and so I can only assume that they stop at the more common ones.

Which is fine, but means they cover less than their competition.

If you’re going to attend the Ironhack bootcamp, I’d suggest:Skimming the prework.

Don’t do it all, for the sake of your own sanity.

Using the skills you learn during the course to make portfolio pieces for yourself in the evening.

The Springboard Online Data Science BootcampThis is the link to the Springboard course.

This course lasts 6 months and costs $7,500 (but you only pay once you find a job).

This bootcamp offers weekly 1:1 calls with a mentor, uses DataCamp’s online courses to teach you the technical skills and includes 14 projects(!)Springboard offer over 500 hours of online material for you to learn from and the individual modules are pulled from the DataCamp library.

I’m not going to breakdown each module as they’re not delivered in person and that means you can take watch them again and again and can refer to outside sources if you struggle with any of the topics.

Once you’ve covered the basic skills you get to choose a specialism, which is a very nice way of handling things instead of smothering students with techniques from lots of different niche fields.

Overall, I think that this course is fairly expensive (which is mitigated by the fact that you only pay once you’re in a job) but one of the better offerings.

Springboard shows that some of the best education is being delivered online.

The real question is whether or not the qualification means as much as an in-person course when it comes to looking for that second, third, fourth job.

A potential problem is the projects.

I can’t find a breakdown of what these are but imagine that some of them aren’t portfolio pieces.

And so anyone taking this course should be aware of the fact that they should still seek opportunities to do projects by themselves.

The Thinkful Flexible Data Science BootcampThis is the link to the Thinkful course.

Very similar to Springboard’s offering The Thinkful programme is also online, also takes 6 months, and costs nearly the same amount at $7,990.

Thinkful offer a job guarantee, which I believe is the same as Springboard’s in that you won’t pay for the course if you fail to secure a position.

I entered my email address to get a hold of the syllabus but didn’t receive anything in-depth.

It looks to be about the same as the other bootcamps on this list, moving through SQL, pandas, matplotlib.

And then going on to cover linear regression, supervised learning, unsupervised learning and finishing on specialisations which are very similar to Springboard’s.

1:1 mentoring is also offered at Thinkful but instead of the once weekly calls they offer two calls a week with your assigned mentor.

As with Springboard it isn’t clear if the purpose of the mentor is to help you get a job or to complete the course (probably the latter).

There’s no specific number of projects mentioned on the course website and I’m not sure who provides their education content (it’s probably created in house).

Again there’s the question of whether this course will mean anything to employers going forward (after the initial job that satisfies the guarantee) and as it’s more expensive that the Springboard course I’ll have to say that this one also seems overpriced.

If you’re going to participate in the Thinkful flexible data science bootcamp, be sure to use your mentoring time to discuss how your job search is going and use your free time to bolster your portfolio with the skills you’re learning in the course.

The Brainstation Data Science DiplomaThis is the link to the Brainstation course.

This programme lasts for 12 weeks and, despite my best efforts, I couldn’t find a price.

Brainstation state that this bootcamp is project-focussed and that at the end you’ll be left with a single major portfolio piece.

Apparently, the application process can take between 4 and 6 weeks.

The programme includes mock interviews post-‘graduation’ support.

Unit 1: Collecting a storing dataIn this unit, you’ll learn about Excel(!) and SQL.

Unit reviewI have very little to go on here, Brainstation seems to guard their prices and syllabus more closely than the rest (I’d love to hear why).

That Excel is covered is unique and surprising.

Data scientists do often use spreadsheets as part of their workflows, but not to the extent that 2 weeks of training using them is worthwhile.

Including SQL training is helpful but is maybe not the best way to spend the first two weeks of a purported data science course.

Unit 2: Analysing DataThe Analysing Data unit covers Python, statistical modelling and something called visual analysis (making charts, I think).

Unit reviewA seemingly good progression from the first unit that shows you how you can do things in code that you’d previously done by hand in Excel (filtering, sorting, etc.

)This unit also includes hypothesis testing which is good to see.

At the end of this unit you’ll have most of the technical skills of a data analyst (though not the business skills as they haven’t been mentioned yet).

I’m a little suspicious of the ‘project-driven’ approach that Brainstation proclaimed — as of yet there’s nothing that would make one person’s portfolio stand out from another’s.

Unit 3: Visualising DataIn this unit you’ll learn Tableau.

Unit reviewIf it seems like I’m writing short descriptions of what’s in each unit it’s because I have no idea what the actual contents of the modules are.

Again, Brainstation have chosen to include a proprietary product in their bootcamp.

I’m not sure if learning Tableau after spending a couple of weeks with Matplotlib is the best idea.

It also takes students away from the ‘data science’ for two weeks, meaning they’ll have a harder time connecting the dots when they return to do more modelling.

Unit 4: Modeling DataIn Unit 4 you’ll learn about supervised clustering (that’s a new one) and unsupervised clustering with applications in finance and e-commerce.

Unit reviewThe unit description states that you’ll be using both R and Python to complete the unit which must be super confusing for students who have spent the past 6 weeks being passed around various domain specific languages (Excel, Pandas, Tableau).

From what I can see on the website it looks as though this module is where previous students have developed their portfolio pieces, which is worrying because it seems like they only get taught a handful of algorithms before this.

Unit 5: Presenting DataIn this unit you’ll learn … presentation skills, I think?Unit reviewIt’s a good thing that the course includes presentation skills but 2 weeks of mock presentations sounds like my personal hell.

Hopefully there’s some career advice that’s also a part of this section.

Unit 6: Advanced TopicsIn Unit 6 you’ll learn TensorFlow and Hadoop.

Unit reviewBolting this on to the end of the course seems odd to me.

These topics are far more advanced and nuanced and edge-casey than anything else the students would have learned up to this point.

Brainstation’s Data Science Diploma ReviewMaybe I’m just grumpy because I’ve written all of this in one sitting, or maybe it’s because I’ve received three emails from these guys with no mention of price, but I doubt that’s it.

I feel like this course relies too heavily on proprietary products, has too large gaps between the data science content, starts off too slow, and ends too abruptly.

Those are all the major problems.

If you like what you see at Brainstation, I’d suggest:Doing way more projects than they set.

Reviewing your notes from the previous weeks during the modules on Tableau and making presentations.

Reading widely about the algorithms they don’t teach.

Choosing your favourite language and committing to mastering it.

Closing thoughtsThis has been an eyeopening article to write.

I’m surprised to see that so many bootcamps claim to be about helping people make or change careers and then teach nothing about business throughout the courses (careers only really happen inside organisations).

It’s also disconcerting that so many of them blend lots of advanced and nuanced topics together without the proper prerequisites.

If I was looking to get started with data science and thought bootcamps were the way to do that, I’d start by taking a data analysis bootcamp which taught databases and SQL, git, basic stats and visualisation.

And then look for a data science bootcamp which skipped those things and dedicated more time to the algorithms and why they work.

Unfortunately, it doesn’t look like many of those exist.

The online courses are both very expensive and have me rethinking whether I’m charging way too little for my own courses.

But at least they delve more in to why things work than the in-person bootcamps.

Of all the bootcamps I reviewed, GA’s probably has the best syllabus, despite its problems.

In short, anyone who feels that a bootcamp is the best way for them to move forward should be ready to spend additional time and resources building a great portfolio.

They should also allocate time to studying the various algorithms in-depth.

If you do choose to participate in a data science bootcamp, be sure to use up as much of the instructors’ and career coaches’ time as possible — extract all you can.

Good luck!Originally published at carldawson.

net on March 23, 2019.

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