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I got recommended this book "Data Science for Business"- Tom Fawcett, for learning practical data analysis and such "practically" from scratch, I was wondering if it's worth it considering it's 10 years old and are there better alternatives?

>>37535 > How do I do this? What is the best way? Should I? The industrial revolution and its consequences have been a disaster for the human race.

Try reading it? And then maybe comparing with your "alternatives"? Do you need a basket weaving forum to give you permission?

>>37535 I recently completed Statistical Rethinking v2. Not business specific but a solid introduction to analysis with bayesian techniques. The book takes you through basic concepts (what is bayes) to modelling with causal graphs, comparing different models, managing measurement error and missing data. It provides hands-on questions to work through (which is where the book really shines). Although it provides a bayesian approach, it is simple enough to shift what it teaches to a frequentist one if desired. One big weakness is that it doesn't cover time-series data. The book itself is in R, but there is a python translation using PYMC3. I'm looking to following it up with a business focused stats book that would include time-series modelling.

>hey I was recommended this book, do you guys think it's still relevant? is there anything that would be more up to date? >>38097 >>38098 >O M G >ur such a loser >how dare you come here to ask for advice about something >just spend however much time reading that book and a bunch of other books which you don't even know exist then tell us which was best >the few minutes you spend asking the question is such a waste of time when you could spend several weeks reading and comparing every book on a given subject >sharing knowledge is like kicking dead whales down the beach LOL. The people who write this stuff are so funny. Clearly think they are intelligent but they have nothing useful to offer. Time/money is valuable and it's worth checking to see if something is worth it before you spend.

>>38237 Piracy tho. Zero expense and little effort to take a glance at the contents of a book if you aren't spending dozens of dollars buying it. I think this passage probably gives a good indication what it's all about and how it is written: >Despite the large number of specific data mining algorithms developed over the years, there are only a handful of fundamentally different types of tasks these algorithms address. It is worth defining these tasks clearly. The next several chapters will use the first two (classification and regression) to illustrate several fundamental concepts. In what follows, the term “an individual” will refer to an entity about which we have data, such as a customer or a consumer, or it could be an inanimate entity such as a business. We will make this notion more precise in Chapter 3. In many business analytics projects, we want to find “correlations” between a particular variable describing an individual and other variables. For example, in historical data we may know which customers left the company after their contracts expired. We may want to find out which other variables correlate with a customer leaving in the near future. Finding such correlations are the most basic examples of classification and regression tasks. Also seems notable that it is written for a less technical audience, taking the time to spell out basic things somewhat verbosely: >Database Querying >A query is a specific request for a subset of data or for statistics about data, formulated in a technical language and posed to a database system. Many tools are available to answer one-off or repeating queries about data posed by an analyst. These tools are usually frontends to database systems, based on Structured Query Language (SQL) or a tool with a graphical user interface (GUI) to help formulate queries (e.g., query-by-example, or QBE). For example, if the analyst can define “profitable” in operational terms computable from items in the database, then a query tool could answer: “Who are the most profitable customers in the Northeast?” The analyst may then run the query to retrieve a list of the most profitable customers, possibly ranked by profitability. This activity differs fundamentally from data mining in that there is no discovery of patterns or models. It seems to use the hypothetical throughout that you are a manager at a company called "MegaTelCo" and gives practical examples of challenges you might run into and how you could deal with them to illustrate data science concepts.

>>38237 If you haven't figured out how to pirate a book and figure out which resources are good for learning and do your own research, then I think "data science" is out of your IQ range.

>>38237 I concur. Seems like c*ckchan et al. are spilling over to this forum sadly.

>>38239 OP's question specifically centres around content not covered in the book. You can't read what isn't there. Even then, it's still beneficial if someone who knows what they're talking about suggests something better. >>38241 You forgot the "time" part of the time/money calculation. Even if you consider you time to be worth minimum wage, a book is usually more expensive to read than to buy.

>>38245 >it's still beneficial if someone who knows what they're talking about suggests something better Fair, but it seems that no one does, which makes sense since this is a cyberpunk imageboard and the book is about teaching midwit wagies to abuse information asymmetry to manipulate people for corporate profit. If it is wrong or outdated and that wastes OP's time and money (assuming that OP is hoping to use the book as intended), good. If there is a better alternative, let it be lost forever. >Even if you consider you time to be worth minimum wage, a book is usually more expensive to read than to buy This is an attitude that can only lead to simply not reading books.

>>37535 I worked through it a few months ago because I had a class to teach on that subject: It is really nice to get an overview over the topic and at times it goes quite in-depth. However I did not always like the organization within each chapter. Some things should have been placed earlier, reducing the "front loading" problem where the reader is forced to keep things in his mind for long before they become useful for understanding the later parts of the chapter. The part about text mining and searching algorithms was excellent.

>>38246 >This is an attitude that can only lead to simply not reading books. Yes, understanding that something has a cost associated with it will cause you to not do that thing when the benefit is less than the cost. This is in fact a good thing. I am sure there are many books you would not read as they would not be worth the time. >If it is wrong or outdated and that wastes OP's time and money good. If there is a better alternative, let it be lost forever. This is a very strange perspective. Assuming someone is evil with no basis and then hoping that the assumedly evil knowledge they seek becomes lost to time. >the book is about teaching midwit wagies to abuse information asymmetry to manipulate people for corporate profit Have you considered that data analytics can be used for both ill and good? That a collection of statistical techniques has little inherent morality?