8 ms·
Foundations of Data Science (2018) [pdf]
- Pinegulf 4y agoThank you. A bit out of date, but the fundamentals are there. This does not seem to be a practical guide, but goes into theory. A good catch for a physicist.
- frozencell 4y agoWhat is outdated?
- turing_complete 4y agonobody uses wavelets these days.
- defrost 4y agoThey're still being used in a variety of geophysical imaging applications, eg: https://www.frontiersin.org/articles/10.3389/feart.2022.1011999/full https://www.frontiersin.org/articles/10.3389/feart.2022.1011... but I guess the cool kids are using something new.
- frozencell 4y agoShould we sometimes learn about history of (data) sciences instead then?
- thoi423uyo4i32 4y ago(the book is from 2020)
- scarmig 4y agoAncient.
- auggierose 4y agoThis is also an official book from Cambridge University Press: https://doi.org/10.1017/9781108755528 https://doi.org/10.1017/9781108755528 .
- college_physics 4y agoJudging by the content, a better title for this book might be "Mathematical foundations of data science". As has been repeated ad nauseum online by practitioners of this dark art, a defining fraction of what has come to be termed "data science" concerns data processes, data models, data quality etc. This is not just about semantic clarity: the range of algorithms and mathematical models that are applicable heavily depends on these adjacent data concerns.
- schnitzelstoat 4y agoFundamentals of Data Engineering by Reis and Housley covers those things very well.
- victor106 4y agoThis is about Data Science not about Data Engineering. How does a Data Engineering book cover topics on Data Science?
- hacker_junky 4y agoDo you think there could be other titles that might be better suited to this work?
- usgroup 4y ago"Yet another partial introduction to some things in data science from an impractically mathematical perspective"
- phkx 4y agoThe parent page by one of the authors links to a version which has been typeset more recently (in 2019). See https://www.cs.cornell.edu/jeh/ https://www.cs.cornell.edu/jeh/
- nicklaf 4y agoAnd the second link on that page is to an even more recent version (2020): http://ttic.edu/blum/book.pdf http://ttic.edu/blum/book.pdf
- usgroup 4y agoAs a practitioner with a research background I wouldn't recommend this based on the contents over something like Elements of Statistical learning. Generally speaking I feel that data science is missing an authoritative methodology book, well separated from books on algorithms or maths/CS foundations.
- college_physics 4y agoI think that is because data science is a recent term that has been coined to mean an increase of interest in the quantitative analysis of "data" across a diverse number of domains. To abstract what is common across all these fields is not a minor task... But achieving such an authoritative methodology book that will not be a cut-and-paste compilation of different strands is in some sense also an acid test whether the term has long term meaning or was simply a hype term during a particular speculative bubble period.
- usgroup 4y agoyeah i think that's accurate. "what they do" rather constrains a book about "how they do it" :-)
- unixhero 4y agoUnreadable book. Sorry. I am a data science practitioner. Edit: In my view, dificult to gain much from this.
- sfpotter 4y agoWow. Thoughtful and insightful comment right here.
- bcd3169 4y agoMeanwhile 90% of data science is sql + linear regression at best
- scarmig 4y agoIn practice, linear regression is highly effective and likely to be better founded than your million parameter neural net.
- sfpotter 4y agoNone of the comments here discuss anything about the book. They're just knee jerk responses, either saying the book is too mathematical, or that other books are better. I wish the quality of comments on HN was higher. Here are some actual thoughts about the book: - Having actually read a few chapters of this book, I find it to be poorly written on the whole. The chapters are extremely verbose and written in a bit of a circuitous and winding style. I find the explanations to be strained and not very pedagogical. They might be insightful if you already understand the material well, but since this aims at being a "foundations" book, the style of writing seems to miss the mark. I think these guys might have been the wrong people to write this book. - I don't think the selection of topics makes any sense. It's a bit of a weird mish mash of topics which are arguably more or less fundamental, and have been popular at different times in recent years in the applied math, statistics, computational science, machine learning, and electrical engineering communities. If you are a student, it seems unlikely that you will benefit from taking a course taught out of this book, since you would be better served by taking more focused courses on the individual topics from this book that happen to be relevant to your research. If you are a researcher and need to learn one of the topics in this book, it's unlikely that this book is a better reference than any of the many existing books on these topics (or review papers). If you are self-studying, I think you are bound to fail with this book. You will benefit from something which is more thought through pedagogically. Ultimately, I don't really see the point of this book.
- renonce 4y agoI'm an undergraduate who took a course based on this book. While I agree that the book is not good at being a "foundations" book, I would say the ideas taught in the book are very helpful in building an intuition for data science. For example: n real numbers independently taken from a standard normal distribution, normalized to be length 1, forms a random vector on a n-dimensional sphere. While these facts are not significant in the sense that I could apply them to a problem directly or use them to prove a theorem, these concepts appear frequently in data science problems and I know immediately that a graph is not a random graph by testing one of the properties of a random graph.
- pessimizer 4y ago
- dayvid 4y agoAnyone read the Data Science Design Manual by Skienna and find it useful? Curious if it’s a good intro source or something like Statistics in a Nutshell