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Kaiser Fung’s data analysis bootcamp

Kaiser Fung announces a new educational venture he’s created, a bootcamp (12-week full-time in-person program with a curriculum) of short courses with a goal of getting people their first job in an analytics role for a business unit (not engineering or software development, so he is not competing directly with MS Data Science or data science bootcamps). Their curriculum is deliberately designed to be broad but not deep.

I asked Kaiser if he had anything else he wanted to share, and he wrote:

I think our major differentiation from other bootcamps out there includes:

a. There are lots of jobs in these other business units outside engineering and software development. Hiring managers in marketing, operations, servicing, etc. are looking for the ability to interpret and reason with data, and use data to solve business problems. Our broad-based curriculum caters to this need.

b. I don’t believe that coding is the end-all of data science. Coding schools teach people how to code but knowing what to code is more important. Therefore, our curriculum covers R, Python, and machine learning but also statistical reasoning, survey design, Excel, intro to marketing, intro to finance, etc.

c. We provide quality through small class size, in-person instruction and instructors who are industry practitioners. The average instructor has 10 years of industry experience, and is in a director or higher level position. These instructors know what hiring managers want since they are hiring managers themselves.

d. We are building a diverse class. We take social scientists, designers as well as STEM people. We just require some exposure to programming concepts and data analyses, and a good college degree.


  1. Peter Norvig asked, in Teach Yourself Programming in Ten Years, “Why is everyone in such a rush?” Norvig argues you can’t even learn Python well in 12 weeks. Fung’s bootcamp is targeting “R, Python, and machine learning but also statistical reasoning, survey design, Excel, intro to marketing, intro to finance, etc.” But then I’m the guy that took a two-year job at Columbia after years of working on machine learning so that I could learn stats properly.

    I’m expecting we’ll soon see the same kind of backlash as in the first dot-com boom. The first time around, all the trombone majors filling up HTML-ing jobs were pushed out when their utility evaporated in the face of increasingly complex back-end and front-end content programming and design.

    I don’t understand why they require “a good college degree” for something that’s a 12 week non-college, no-prerequisite course. What does “good college” even mean? The two best programmers I know dropped out of college.

      • Adam says:

        Sorry, but I’m not sure I get this? I listened to the song and looked up the lyrics and it still went totally over my head

        • It’s less fun if you have to explain… but here goes.

          The song is all about re-evaluating whether what you’re doing in society is a useful contribution or just flim-flam “for the fast wealth” living in a “fantasy world”. That seems pretty apropos of the current tech bubble and the dot-com bubble and the desire to get a quick 12 week course in data analytics and start pulling down the quarter million dollar salaries at Uber or whatever.

      • And this one is really about how it doesn’t really matter whether you actually can do the work, you just “gotta make moves” and shovel in the cash while the shoveling is good. No time to play around, get down to hoovering jacksons.

        Those two songs sum up the tensions of the 2010’s pretty well.

    • Keith O'Rourke says:

      Bob: > What does “good college” even mean?
      I think this snippet from the link might be key – “have the analytical smarts to excel in these new data jobs.”

      If you can identify those likely to succeed then you are just providing window dressing and some useful tricks and insights to get them over the hump. (The executive MBA’s – in theory – work by having employers identify such candidates by making sure the tuition is high enough that they are being serious about that.)

      > I’m the guy that took a two-year job at Columbia after years of working on machine learning so that I could learn stats properly
      Update on that? ;-)

      For my self, I wish I had kept a diary but – I was able to do some decent analytic work for the Canada can compete book prior to entering statistics by figuring out how to write dynamic programs in Lotus 123 and make multiple panel graphs of industry activity over time by industry.

      Perhaps 5 years later after I gave a talk at the statistics department on meta-analysis I was starting to get a reasonable grasp on statistics and then 10 years later when I did seminars for grad students and faculty on Don Rubin’s work on observational studies prior to his visit I might have had a decent grasp.

      The point is to get in and be useful in some role so that you can continually grow over time. The idea that they will be moderately capable in many ways – when they come in – is a mug’s game (perhaps mainly at the expense of hopeful students).

      • That’s very insightful—I should try thinking like a statistician more often.

        I wrote the entire LingPipe application before I really understood stats. I couldn’t make heads or tails of Bayesian Data Analysis, but I loved learning BUGS and reading Gelman and Hill’s regression book. The new versions should be even better.

  2. zbicyclist says:

    It’s easy to see why hiring managers would have trouble in this area.

    One problem is that hiring managers getting their feet wet in these areas (“Hiring managers in marketing, operations, servicing, etc.”) are looking for several different skill sets, for example

    Figuring out what the problem is, and what data you want to use to address it.
    Cleaning/prepping the data, EDA.
    Hardcore statistical analysis, machine learning, predictive analytics, etc.
    Project management.
    Engagement management / report writing / explaining to the internal client.

    i.e. for their first hire in this area they want a one-man band.
    There aren’t many people who can handle all these roles well — and, if they can, they likely won’t want to handle them all.
    Plus, if you are hired in as a one-person show, it’s hard to see a career path.

  3. Jack says:

    20K for this course? C’mon, whoever is willing to pay for this probably can’t learn by other methods, that’s not a good signal…

  4. Hernan Bruno says:

    A marketing manager or a product manager has to understand what data are available, what questions can be asked from the data, what do the different techniques do and what their pros and cons, what are the challenges obtaining, cleaning, preparing and analyzing the data. How to quantify uncertainty. How you present results. How to link results to decisions. Etc. Etc. A manager needs the basic frameworks and language to talk to the programmers, or run small pilot analyses.

    I think this bootcamp will be a good idea for the right audience. $20k is not that much. $20k is what a 3-week “leadership” training costs at Harvard, Wharton or INSEAD.

    Note: I teach a course called **Marketing Data Analysis and Visualization in R** at my university targeted to business master students (pre-experience, ~23 year-olds). Readers of this blog would find the content very basic and even laughable (Basic R, data cleaning, how to aggregate and slice data, simple regression, ggplot2 and good graphics, and things like that). It has a great impact in their immediate careers (too early to say about the long-term) as they can hit the ground running in consulting, and other jobs like digital marketing, or product management. Could they learn all of this from a book, the manuals, and the web? Probably, yes. That is how I learnt it. But the course is faster and helps the students focus on what’s important and to identify the latest thinking on a technique. One day I will start including STAN, I hope.

    • Hernan Bruno says:

      I will go out on a limb and say that if a business manager goes through the whole book by Gelman and Hill, it would put him/her at the top 1% across business managers in terms of thinking about data, regression and inference.

      • Gelman and Hill is a pretty good intro book, so I’d say if you complete Gelman and Hill it qualifies you to be an unpaid intern for a year while you finish the last few semesters of your degree. In other words, when it comes to technical skills, business management is a market for lemons.

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