I'm going to sound very aggressive, but I don't understand the point of this book. They say
> The material in this book is too valuable not to share.
and after reading the sample I feel their definition of valuable doesn't align with mine. It's a "handbook" but the chapters are interviews that don't go in depth into anything. Here's a sample "question"
> Compassion is also critical for designing beautiful and intuitive products, by solving the pain of the user. Is that how you chose to work in product, as the embodiment of
data?
Really? This reads like an onion article about data science.
As a data scientist, I draw inspiration from the infinite depths of understanding. Life is merely the unfolding of a series of recursive recommendation engines, each one AB testing the local gradients of human emotion. You think you are just buying a pair of socks online. Foolish mortal. That transaction was a multi-armed map-reduce set into motion long before the internet existed. Data science is the internet of things, it is big data at the speed of entropy, quantum mechanics at the scale of desire.
In data we trust. All others bring EVEN. MORE. DATA.
You had me at "life is merely the unfolding of a series of recursive recommendation engines, each one AB testing the local gradients of human emotion". The more i think about it the more it makes sense.
Your comment is meaningless. I don't mean that negatively. I mean that it really is meaningless. It's buzzwords connected together with a lack of thought or direction.
Agree, this seems like a collection of interviews with some famous folks who are in the field of 'data science' (can we just call it stats please). A handbook is a poor description indeed.
For folks interested in learning about this topic there are tons of online courses/videos on the real stuff. The stanford islr course is a great place to start
I think this passage actually addresses the context of that question:
>The difference between empathy and compassion is big. Empathy is understanding the pain. Compassion is about taking away the pain away from others, it’s about solving the problem. That small subtle shift is the difference between a data scientist that can tell you what the graph is doing versus telling you what action you need to do from the insight. That’s a force multiplier by definition.
I think the context you've quoted only further proves my point. This sentence means absolutely nothing to me, it's trivial and devoid of any actionable information. But because the interviewee is a "famous data scientist" it is suddenly important info that needs to be shared with the world?
To be fair, I know some of the co-authors and I'd say they're pretty sharp at data science. However, this book highlights something they're not so sharp at: self-promotion.
I believe this book is meant to be a gag in the way the original Facebook Brogrammer store was meant to be a gag. And, similarly, the authors are going to learn the consequences of having a lot of people take the gag seriously.
I am not a native english speaker and I am wondering why is a book, which is a collection of interviews called a 'handbook'?
The definition of a handbook according to wikipedia:
"A handbook is a type of reference work, or other collection of instructions, that is intended to provide ready reference."
Agreed. I was definitely expecting a reference, and I was confused that the table of contents listed the authors & their positions before mentioning the title of the chapter.
I purchased the book. It is just a general series of interviews with high profile data scientists. Kudos to the author for getting some very busy and prominent people to agree to interviews.
I think it would be valuable to aspiring data scientists - students who are looking to break into the field. It has good general advice on what backgrounds companies look for and some inspiring and motivational stories.
It will not be useful for current data scientists looking for information on how the companies in the book do data science. There are very few technical details included.
Flashy website, but a quick background check of the contributors reveals that they've barely been working at their respective positions for over a year.
In any event, kudos on providing an interesting narrative.
That's probably why they put together the content in the form of a collection of interviews. They don't establish authority of knowledge. They establish authority of intelligent curiosity.
I don't understand how a lack of documented experience correlates with the quality of this handbook. In fact, you might as well assume that W person needs X degree over Y years to become an expert in the field of Z...
Not everyone documents how long they have been in the field publicly, and for all you know (or don't know...), some of the contributors (that I personally know myself), have been immersed in the field for years.
Also, from a more credible standpoint, majority of the book has direct Q&A + perspectives of some of the best data scientists...
Love the business plan. Also love the format in terms of interviews. I have over the years learned more from interviews and just watching someone's workflow and them doing day to day "mundane" work than offical teaching sessions.
I am always surprised how experts in fields under value their own mundane work. If people would just share the studpid day to day stuff people would find great value in that even more than their more advanced work.
Experts are the one's true genious is the things that are easy for them and not neccesarily the advance stuff.
In the past, when considering the term 'Data Science', I've assumed that this is roughly equivalent to the union of Probability Models, Statistics (with a view towards Decision Theory), CS, and Graphic/Web Design (perhaps throw in some knowledge of Heuristics and Biases too.) The number of individuals with sufficient experience in all of these fields should be significantly smaller than those in any given field alone.
I am aware the the demand for Data Scientists is higher than Statisticians at the present, at least in the US. Assuming that there is a non-trivial amount of employed Data Scientists, one is led to the conclusion that there is fairly large portion of under-skilled Data Scientists.
My question is, assuming the above hypothesis, do Data Scientists, on average, under-deliver, or are job requirements lowballed to avoid failure? First thoughts would be to compare to Quants, or Actuaries.
I have a belief that Data Scientist jobs are created due to the following process: Startup founded => Data collection => Predictive Model Exists => Data Scientist => "Visually confirm" hypothesis and send to marketing department. Obviously, the current order of this chain is not correct. A priori, mere sampling does not simultaneously guarantee regularity and high Fisher information.
Based on my interest in this field I am willing to pay around $5 for the book. However I agree that it is not a fair price. What should I do? Note that it is not a matter of money. I could afford the suggested price or even more but I am not that interested in the topic.
Downloading for free is not morally acceptable for me, I would like to support this business plan.
I'm going to quote Patrick McKenzie's article that was on here last week.
"Because everyone in the negotiation is a businessman and any number we are mutually happy with is a morally acceptable number."
I buy tons of books. If the author makes the book available at a choose your price, then I always choose the lowest price even if that price is free. If it is morally acceptable to the author, then it's acceptable to me. I've bought books for as low as free, but others for more than $100.
This is quite different from stealing or pirating a book. But you should do what you think is right and leaves you with a clean conscious.
I would take the authors at their word and pay just what you are comfortable paying.
Variable pricing is really an interesting business model. I have three books for sale on leanpub, each with a suggested price of $6 with a minimum of $4. A lot of people pay more than $6, BTW, the most so far that anyone has paid is $50. Payments in the $10 to $12 range are common, but probably less than 15% of sales.
Pay $5. Their marginal cost is essentially $0. I'm willing to bet the authors would rather have the $5 than nothing, and that is probably why it was priced this way.
Buy it for $19. Even if your aren't that interested in the topic just one potential insight from that book will make it worth way more than the $19USD.
My personal experience. Plus, I quit reading most of books I ever bought, not considering it worth my time (comparing to other activities, or - other books).
Is there a sample chapter? It's hard to know if I want to purchase it without knowing anything about how the book is written or whether it tried to achieve its purpose.
On the 'Get the book' link, the last paragraph states:
'We have set a minimum contribution of _FREE and a suggested contribution of $19 to cover the time and investment the four of us put into this book.'
The 19$ is the _suggested_ price, the actual sum is up to you. As you guessed it is a collection of interviews.
Thank you for this! I love the interview format as well. One bit of feedback so far as I haven't actually started reading yet, it would be helpful to have bookmarks in the PDF to be able to jump around. Thanks again!
if the author is reading this, I'd pay $40 for the audio. I'm assuming you recorded each interview so it could be transcribed. I've got 3 kids and audiobooks/podcasts during my commute is the only realistic time I can consume bulk content.
> The material in this book is too valuable not to share.
and after reading the sample I feel their definition of valuable doesn't align with mine. It's a "handbook" but the chapters are interviews that don't go in depth into anything. Here's a sample "question"
> Compassion is also critical for designing beautiful and intuitive products, by solving the pain of the user. Is that how you chose to work in product, as the embodiment of data?
Really? This reads like an onion article about data science.