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Top Customer Reviews: Introducing ...

Rating: 4 out of 5 with 2 ratings
4 / 5 Suzi
Formed: Kindle Easy Edition partorisca read, gave the good fund in a statistician that was my less mathematician preferred zone like the student.
4 / 5 Cleora
Formed: Kindle Edition A very big level overview of statistics. Better the description that knows book that to the learning likes the pound.

Top Customer Reviews: The Misbehavior of ...

Rating: 4 out of 5 with 8 ratings
4 / 5
I have wanted to you love/ it That. A masterpiece of an inventor of Fractals. A classical. Highly recommended.
4 / 5
No the plot of mathematics but the very good presentation of an idea.
5 / 5
Very interesting bed of a founder of a field. Transmissions your perspective in a hypothesis of effective phase.
4 / 5
Adds read!! Shjould Be compulsory in all the funds 101 run
5 / 5
Has to that read partorisca any student of financial phases
4 / 5
Extremely rids written although it takes calm by means of a world of the financial turbulence that common felt uses and an approximation of @@the @modeling alternative mathematician that use fractals. Although a book has shouted more the questions that answered, resupplies interesting perspective in some limitations of Black-Scholes, Optimisation of Bad Variance, a Hypothesis of Effective Phase and a Theory of Random walk. Mandelbrot Shoots down an idea of normality in some distributions of prizes of house and financial advantage on two particularly pesky characteristic of financial phases: leptokurtosis (fat rows) and correlation of serial (memory). This book is adapted more partorisca readers with the level of diplomado that comprises of finance.
4 / 5
This book was easy to read and writing for the brilliant man, for this reason so only gives comes from it.
A question with a book is is tentativa partorisca apply the science that explains principles and natural growth of character to a financial industry. An access is was!
A movement of some looks to stage partorisca try so only that special and seperate is animal and character. We are not like this easy to comprise and predict like model of growth of the fern and this fractal science of Mandlebrot is the failure is tentativa partorisca do like this.
Are happy failure! Help partorisca celebrate me like this something only and the place averts of a world-wide around me, something with the touch of God my life.
Has the few useful things that a book says this in spite of, a duquel if a fact that volatility begets volatility or simply partorisca expect a prize of movement of days partorisca be equivalent in measure, although no in direction, to a movement of prize of leading days.
Another very useful part is his condemnation of a 'theorem of centrical limit' or a curve of bell in your ad, he the work adds of uncovering an error of following such the philosophy.
He Take a time partorisca read a calm book will enjoy it included although an application of fractals to the finance is the piece .
5 / 5
Has loved that. A masterpiece of an inventor of Fractals. A classical. Highly recommended.

Top Customer Reviews: Weapons of Math ...

Rating: 4 out of 5 with 26 ratings
5 / 5 Lisette
Cathy Or'Neil written with clarity and expertise. It distills an information around an invasion of algorithms to citizen' bolt in the more understandable way. His examples vivify the visit of a reader of a moral, social, and psychological implications of the intrusion of the Big data to our lives-- is taking the work, when be pointed partorisca subprime ready, when be estimated, etc. Some subjects are real and present. It offers some wise solutions that confidence on improving the lives of the people more than winning money irrespective of a side the society. Also it explains in of the clear terms because a lot of algorithms are bad and some are good--this has to that do with constant updating of new data.
3 / 5 Arcelia
A book of entity. It is directed in a harm that the blind applications of algorithms can do the society, the perspectives of occupation and taxes of incarceration. A escience' for behind the algorithms can be at all more than guesswork instantiated like this of the mathematical formulas. The sound asks the measure of the accountability partorisca some algorithms of harms can cause is timely and pertinent. But his worry that the algorithms are closing some poor to poor districts, and one a lot-was to the his own comfortable precincts, looks overdrawn. But his main conclusions am very done. 'I different fields have concepts very different that constitutes evidence, the one who elements of a need to argue to be 'tried', and when the quantities of evidence have accrued to persuasive querella.' My coolness to some roots partorisca reserve that it feels that ossia so only any mine causes, although it can be the worthy a, and can be one would have to that take up.
5 / 5 Delila
An excellent addition to a bookshelf of any that tries to maintain up with advances in the artificial intelligence and our addiction have augmented on automation in human test.

The worries of Cathy are ethical and apply to the plot of some systems 'dumb' old, as it has SEATED partorisca value. But HAVE the systems are increasingly 'black boxes '' doing decisions that life to change that can not be defied.

One adds read. They are Cathy with Alert of Google. A listened lovely of the voice.
4 / 5 Shaunna
The superficial tests easily can cause collateral harm. The algorithms are tools that can and evolves quite quickly, be of feedback one of some main factors inside a developer of process. An author thinks that would have to that have more transparency the external entrance reason the collateral harms create additional questions to solve and spend his own costs that any, to somewhere ought to assume in an economy of data.
5 / 5 Rickie
Pound a lot informative. It develops a subject of in reliance in of the statistics that can be entirely invalid. So only reason is numbers he no the fact well. Ossia The very serious question in our system of the education where expects student tests to resupply results of confidence.
3 / 5 Kenda
I think that that a book was well, but tended to run out of steam. It thinks some needs of author to flush was more subject versus revising more chances of alike subjects. So only you result the little repetitive. But, you are the good book and the good bed this in spite of. It recommends it like this thought cause read.
5 / 5 Suzanne
As any with the fund of the mathematician and that has experienced only models that the injustice could be during one 2007-08 crises crediticia, had grown tired of an obsession of data. The mathematical models are so only like this very like this of the suppositions have dipped in them. Cathy Or'Neil' the book has renewed my interest in mathematicians and die. I give hope that the mathematical models can be used for good purposes, to help people, and level a field to touch. While the measure is static, the models can be dynamic, as it shows. I recommend Arms of Destruction of Mathematics for any interested in of the data, @that @the @modeling mathematical and algorithmic, and a measure of results, particularly people those who are in an intersection of these fields and impact entrepreneurship or social innovation.
5 / 5 Kati
Writing very good.

Takes a hype of big data.
3 / 5 Kiara
A lot Basic bed in Big Data for a layman. A bit repetitive but still the good bed
4 / 5 Signe
Really very written and has researched. He illuminates to the plot of worries thinks that a lot of people have in an age of data and offer really obliging the evidence on to the equal that can the to us fix.
5 / 5 Tessie
Cathy Or'Neil written with clarity and expertise. It distills an information around an invasion of algorithms to citizen' bolt in the more understandable way. His examples vivify the visit of a reader of a moral, social, and psychological implications of the intrusion of the Big data to our lives-- is taking the work, when be pointed partorisca subprime ready, when be estimated, etc. Some subjects are real and present. It offers some wise solutions that confidence on improving the lives of the people more than winning money irrespective of a side the society. Also it explains in of the clear terms reasons a lot of algorithms are bad and some are good--this has to that do with constant updating of new data.
5 / 5 Sadye
An excellent addition to a bookshelf of any that tries to maintain up with advances in the artificial intelligence and our addiction have augmented on automation in human test.

The worries of Cathy are ethical and apply to the plot of some systems 'dumb' old, as it has SEATED to value. But HAVE the systems are increasingly 'black boxes '' doing decisions that life to change that can not be defied.

One adds read. They are Cathy with Alert of Google. A listened lovely of the voice.
4 / 5 Edythe
Rids a lot informative. It develops a subject of in reliance in of the statistics that can be entirely invalid. So only reason is numbers he no the fact well. Ossia The very serious question in our system of the education where expects student tests to resupply results of confidence.
5 / 5 Ursula
Thinks that a book was well, but tended to run out of steam. It thinks some needs of author to flush was more subject versus revising more chances of alike subjects. So only you result the little repetitive. But, you are the good book and the good bed this in spite of. It recommends it like this thought cause read.
5 / 5 Estela
Like any with the fund of the mathematician and that has experienced only models that the injustice could be during one 2007-08 crises crediticia, had grown tired of an obsession of data. The mathematical models are so only like this very like this of the suppositions have dipped in them. Cathy Or'Neil' the book has renewed my interest in mathematicians and die. I give hope that the mathematical models can be used for good purposes, to help people, and level a field to touch. While the measure is static, the models can be dynamic, as it shows. I recommend Arms of Destruction of Mathematics for any interested in of the data, @that @the @modeling mathematical and algorithmic, and a measure of results, particularly people those who are in an intersection of these fields and impact entrepreneurship or social innovation.
5 / 5 Malena
Ciao Crew,

partorisca receive a container, a focus is open and there is any interior of product.
4 / 5 Joy
The must read partorisca all concerned in big data, comprising Amazon
4 / 5 Halina
that Interest and readable account. Plots partorisca think roughly.
4 / 5 Micaela
The mathematician can be dangerous.

Automation without transparency and built in the loops of positive feedback can be productive counter.

Can head to some the worse results faster.

Requires partorisca be vigilant. We require visibilities and of the better accesses to these WMDs. There is hope to turn these WMDs the positive tools partorisca the better society.
5 / 5 Kathi
Scary! An end is really scary when you discover the one who really killed a radio star. So many people think it was video, but is SOOOO much more sinister and darker. I can it does not believe it it has taken people so anxiety the imagine was.
4 / 5 Tonya
Writing a lot good and a lot of enlightening. It is good to listen another perspective in a wonderful world of 'Big Data'.
4 / 5 Kamala
Excellent, pertinent. Structure legustado partorisca reserve around use of analytics in of the different sectors, the heavy example. The place of the author in a danger of analytics is pressed aggressively but the examples of benign uses are has mentioned also.
5 / 5 Renata
Amiably writing, clear and accessible to a layperson. Subject timely and of entity, the weekend adds has read.
5 / 5 Fletcher
Has devoured this book. The mathematics is not my stronger dress, but Or'Neil a @@@subject extremely easy to comprise and enjoyable partorisca read. His tended and calm the querellas has the strong social justice slant, so that it disagrees with his politics can not enjoy this book, but his expertise is inarguable.
Highly recommends this book by all the world - Or'Neil is drawing attention to the @@@subject that too many of us have not annoyed partorisca think roughly, and his analysis I leaves partorisca comprise some intrinsic dangers in these systems.
5 / 5 Cherri
We give form all now. Evaluations of professor, labour applicants, applications crediticias, on-line purchasing, voting models, crime – quite anything can think of this modeled in some opaque black box of unaccountable algorithms. They are like this inherently faulty, discriminatory and racist as to be shameful. They have cut poor to maintain on, and resupply a rich with all the classes of advantages. Scientist of data Cathy Or'Neil has said in of the Arms of Destruction of Mathematics: ”processes of Big Data codify a past. They do not invent a future.”

Something that simple like the code of zip can say the system the one who bondadoso of the lives of neighbourhood in, and suppositions of mark. History of investigation, activity of half comunicacionales social, shabby memories – all contribute to a decision of instant that is worthy or no. These values are plugged in the applications escoles, labour applications, and personal evaluations like HR records, tests of personalities and has included to date places. Even purged, Forgiven, and there is expired the details remain active. Neighbourhoods of police model. They harass resident for each pocola what in of the poorest neighbourhoods, while giving spends it free of richer some, where the crimes are far main, but mostly with the target. So only ten states have outlawed an use of controls crediticios in labour applications. For compraventa in downscale tents, credit cardholders has had his limits there is stabbed, doing them poorer and doing them the poorest risk – so many in taxes to interest main. It is models of computers that gone back of schedule, without worry for some needs of an employee in of the terms of priest of girl, the time was among turns, or opinions for advanced. The manager is paid to optimise the shows for now have done, so memos from above gone unheeded.

That the models are often bad, bad drawn, misinformed and misconstrued, means that the people are denied service , or any one assumed, or has shot frankly. But there have it always more for behind his, how is so only a cost to do @@subject. “Unfairness Is a material black belching out of some chimneys. It is an emission , the toxic a,” Or'Neil has said. We are all the just collateral harm .

A company of sure instantly currency if the client is likely to tend it around. If it judges no, he to his key more. In fact it has 100,000 microsegments (cubes) according to bookmarks/marcadors of client of the instant. Florida, the engine with the cleaned but calm record the pays of bookmark crediticio poor $ 1552 more paralizaciones sure that the engine with the bookmark crediticio big and the conviction of drunk behaviour. Places of the cost will not offer you discount it if you are already logged in. Ready of payday and for apresamiento of schools of the profit in a disadvantaged and a desperate, extracting billions they. Some games are without finals.

WMD Is extremely quickly paced, the fact has packed, and depressing. It is coming to a point that the cars dictate those who can have the successful life, right out of a door. Initiative, value, creativity, walk, human advantage – does not enter his. We are all typecast for Big Data - has attributed - values mathematically that it can it he stymie the life. There is not any appeal. There is not even any one knowing. A poor taking poorer. A rich find one was new refreshing.

And of course, any of of the this is transparent. The clients can not arrive in these prizes, these decisions or this mark they. It is everything in a mathematics, manipulating. This in spite of, 73 of the Americans think that the results of the researcher is “attentive and impartial”. 62 it believes estacas of Facebook his submissions by all the world. At all it can be further of a truth. Worse, draw of banks of the data in the each one another, multiplying his errors, sometimes creating entirely dud profiles of the person, the one who then can not take the work, hire a paving or buy the car.

Or'Neil says that it is outraged for his own profession. You will be too much.

David Wineberg
5 / 5 Cynthia
rids ecrit the pair joins auteure that has joins reelle terrain of experience, ca transmission !

Top Customer Reviews: The Art of ...

Rating: 5 out of 5 with 1 ratings
5 / 5 Danny
An author, the veteran statistician, has done the glorious work transmits, by means of this book, some concepts and subtleties of statistics and statistical reasoning. In the prose that is clear, friendly, authoritative and accessible, drives a reader by means of some technicians of statistical analysis, comprising a diverse pitfalls and abuse that can easily the a lot of plague. A glossary in an end of a book contains a lot of definitions of varied of some terms have used in a book as well as mathematical formulas of sure quantities, partorisca these interested.

This book could be lovely immense like the text of reference partorisca the course of formal university statistician. But also it can be enjoyed for enthusiasts of statistician/of the mathematician interested the one who is looking for friendly, explanations any mathematicians so that it is often a lot counterintuitive concepts.

Top Customer Reviews: HBR Guide to Data ...

Rating: 4 out of 5 with 4 ratings
4 / 5 Randall
A book is good to gives guidance in of the points keys but like all the books of HBR drive, an organisation is the pocola cute .
5 / 5 Mirna
A lot very read. Summarized By means of 20+ coverage of pieces the majority of canal of process of science of data. Quite short to finalise in the long weekend. Helped me he refresh some basic concepts in statistics, predictive analysis, visualisation, etc..
5 / 5 Pierre
The majority of some volumes in a “HBR drives the” series is anthologies of the previously has published pieces in Harvard the Business description in those varied collaborators shares his ideas regarding the subject business that imports like Better Business Writing, that Takes one Does Very Done, and Management of Project.

How is also true of volumes in another such series, notably HBR Essentials, HBR the must Reads, and HBR Tips of Management, HBR drive them offer substantial value in thought of cutting flange of 25-30 sources in the alone volume in the prize (each one that like this in $ 15-20 of Amazon in a version joined) for the fraction of the like the reprints of piece would cost.

That has in this now on sale volume — for EUA of Amazon for only $ – is the wealth of information, ideas, and joint that can leaders in almost any organisation to establish or strengthen the workplace the culture inside that manager comprises some numbers and do the better decisions have based in these numbers.

For example, when it comes to ask new data or analytics of scientists of data, the plot of the manager does not know some right questions to ask to take an information requires. But if you do not frame one asks correctly, could leave your uncertain analyst roughly like this to come from. Here it is three questions to think roughly:

or That with a data? Be like this concretise like possible in those calm decisions and one do one has based in an information. A scientist of data, to listen the one who attentive achieves, then can collaborate with you to define a together right of questions to answer and better comprises exactly that information to look for.

Or Is an easily available data? Ask if any one has has collected already a relevant data and the analyses have treated — neither in your company or using public die. One that grows never breadth of the public data often resupplies easily of the accessible responses the common questions.

Or Likes to take a data , The scientists of data owe that decide among using the data compiled for a company by means of a normal course of business, as in of the observational studios, and collecting new data by means of experiences, which can be expensive. It Likes him the course of your conversation with analysts, question on some costs and of the profits of these options.

The analysis of data can help class by means of complexity and of the decisions of mark, but still with some better analytics tools, is still vulnerable to human deceptions. For chances, is likely to pay more attention to has thought that rows with our faiths and to ignore other facts and models in a data. This calls a cheat of chance. You can avert he to try to embrace information that contadoras yours (or your boss is) faiths to do of the following:

or Specific for advanced a data and of the analytical approximations on which will base your decision. This will reduce a temptation to cherry-choose thought that is in accordance with your prejudices.

Or Actively look for thought that disprove yours faiths. You ask, “If my expectations are bad, which the model probably sees in a data?” Enlist The sceptic to help you.

Or Extracted your findings like this of the forecasts, and try them. If you uncover the correlation that thinks that your organisation can benefit, use an experience (or perhaps more than a) to validate this correlation.

This volume will help the manager improves likes to identify one the majority of information of need of entity, scrollings of complete knowledge, work more effectively with scientists of data, behaviour business experiences, value a value of data, marks prompt (albeit provisional) forecasts, distinguishes among correlation and causation, averts cognitive sprains (p. p.ej. Chance), and maximizas a potential value to learn of car.

I impulse that action my big consideration thus book to verify out of the volume of mate in a series, HBR Drive to Fund Basics for Manager.
5 / 5 John
The majority of some volumes in a “HBR drives the” series is anthologies of the previously has published pieces in Harvard the Business description in those varied collaborators shares his ideas regarding the subject business that imports like Better Business Writing, that Takes one Does Very Done, and Management of Project.

How is also true of volumes in another such series, notably HBR Essentials, HBR the must Reads, and HBR Tips of Management, HBR drive them offer substantial value in thought of cutting flange of 25-30 sources in the alone volume in the prize (each one that like this in $ 15-20 of Amazon in a version joined) for the fraction of the like the reprints of piece would cost.

That has in this now on sale volume — for EUA of Amazon for only $ – is the wealth of information, ideas, and joint that can leaders in almost any organisation to establish or strengthen the workplace the culture inside that manager comprises some numbers and do the better decisions have based in these numbers.

For example, when it comes to ask new data or analytics of scientists of data, the plot of the manager does not know some right questions to ask to take an information requires. But if you do not frame one asks correctly, could leave your uncertain analyst roughly like this to come from. Here it is three questions to think roughly:

or That with a data? Be like this concretise like possible in those calm decisions and one do one has based in an information. A scientist of data, to listen the one who attentive achieves, then can collaborate with you to define a together right of questions to answer and better comprises exactly that information to look for.

Or Is an easily available data? Ask if any one has has collected already a relevant data and the analyses have treated — neither in your company or using public die. One that grows never breadth of the public data often resupplies easily of the accessible responses the common questions.

Or Likes to take a data ? The scientists of data owe that decide among using the data compiled for a company by means of a normal course of business, as in of the observational studios, and collecting new data by means of experiences, which can be expensive. It Likes him the course of your conversation with analysts, question on some costs and of the profits of these options.

The analysis of data can help class by means of complexity and of the decisions of mark, but still with some better analytics tools, is still vulnerable to human deceptions. For chances, is likely to pay more attention to has thought that rows with our faiths and to ignore other facts and models in a data. This calls a cheat of chance. You can avert he to try to embrace information that contadoras yours (or your boss is) faiths to do of the following:

or Specific for advanced a data and of the analytical approximations on which will base your decision. This will reduce a temptation to cherry-choose thought that is in accordance with your prejudices.

Or Actively look for thought that disprove yours faiths. You ask, “If my expectations are bad, which the model probably sees in a data?” Enlist The sceptic to help you.

Or Extracted your findings like this of the forecasts, and try them. If you uncover the correlation that thinks that your organisation can benefit, use an experience (or perhaps more than a) to validate this correlation.

This volume will help the manager improves likes to identify one the majority of information of need of entity, scrollings of complete knowledge, work more effectively with scientists of data, behaviour business experiences, value a value of data, marks prompt (albeit provisional) forecasts, distinguishes among correlation and causation, averts cognitive sprains (p. p.ej. Chance), and maximizas a potential value to learn of car.

I impulse that action my big consideration thus book to verify out of the volume of mate in a series, HBR Drive to Fund Basics for Manager.

Top Customer Reviews: Discrimination and ...

Rating: 4 out of 5 with 5 ratings
5 / 5 Gladis
Thomas Sowell would owe that be celebrated more amply that the already is. His capacity to explain has has complicated concepts and of the theories and tear in social sprains and some fixed loans that typically have an opposite effect that is trying to achieve, is clear and concise. A highly readable book and of the entity of this scholar seasoned.
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Good content, could be more thorough state partorisca a prize.
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Another exceptional book for an excellent writer. Intelligent, insightful, and up to date.
Recommends highly!
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Sowell Is easily one of a more has estimated under thinkers of all the times. This book is extraordinarily informative.

Top Customer Reviews: Data Science for ...

Rating: 4 out of 5 with 25 ratings
5 / 5 Alexia
Formed: Paperback His the introduction adds book to a field of science of data. If some one has leading familiarity with some concepts, can help structure your thoughts and knowledge in the good way. A book falls short to detail so it is feigned partorisca the widest audience. So you are looking for specifics in of the algorithms or the science of data solves partorisca your questions can give you the basic idea, but at all further. Partorisca Any the one who laws in this field, the plot of some daily solutions devises is falsified in both technician and method and also that the emphasis is dipped on generating characteristic better partorisca such algorithms. A lot of big bad data there this requires in-knowledge of half the weed was.
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Formed: Kindle Edition One adds read, a glue among the subject taken and like some supports of technology he - Formateo of big
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level: Paperback Probably a more read of a year in my final - has transformed totally a discharge of my work, and king-shaped likes to conquer questions of analysis of the data. Of then all the people learn different statistical approximations, has found this result of invaluable book 'conscious' of the like the technicians & of tools are available to use for questions of concrete analysis.
5 / 5 Verda
Formed: Paperback love this book. They are analyst in finance with exposure very limited the science of data. It take this book mainly reason our company is so only now resulting more involved in exploring data and data of client for potential occasions.

This book is the start adds for any the one who is not the scientist of data.
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Formed: Paperback This book resupplies an excellent overview of as the science of data are used in business. Less than the that-to and more than the that-calm-has to that-do and reason-calm-would have to that it writes of book.
5 / 5 Yang
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Formed: Paperback Writes in simple English but require some fund analyses no parametric to take a fundamentals that is explained in this book. It is very organised and does not require for excusarse like this often when the technical explanation is required.
5 / 5 Pasty
Formed: Kindle Edition A book is to a large extent written for an executive any one knows a @@subject a late plus lang'.
A lot in fact resupply practical examples of like this to do datamining or included really best practices.
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Formed: Paperback the element was described like this and has arrived quickly.
5 / 5 Cruz
This book is invaluable resulted mine and my company. We inform his as 'the bible'.
HAS amazing coverage of subjects, is a lot clear and thorough, and a lot especially offers a lot inspiring and useful examples of as several technicians of the science of the data can be has applied concealed is not easy to find in any one another book. A book also has the discussions add in a field of science of data in business and like partorisca structure/squads directed of science of the data.
4 / 5 Vennie
His the introduction adds book to a field of science of data. If some one has leading familiarity with some concepts, can help structure your thoughts and knowledge in the good way. A book falls short to detail so it is feigned partorisca the widest audience. So you are looking for specifics in of the algorithms or the science of data solves partorisca your questions can give you the basic idea, but at all further. Partorisca Any the one who laws in this field, the plot of some daily solutions devises is falsified in both technician and method and also that the emphasis is dipped on generating characteristic better partorisca such algorithms. A lot of big bad data there this requires in-knowledge of half the weed was.
5 / 5 Sandy
One adds read, a glue among the subject taken and like some supports of technology he - big level
4 / 5 Alexander
This book resupplies an excellent overview of as the science of data are used in business. Less than the that-to and more than the that-calm-has to that-do and reason-calm-would have to that it writes of book.
5 / 5 Elias
Love this book. They are analyst in finance with exposure very limited the science of data. It take this book mainly reason our company is so only now resulting more involved in exploring data and data of client for potential occasions.

This book is the start adds for any the one who is not the scientist of data.
5 / 5 Agnus
If you are new to a world of science of data, ossia the good book to have and begin in a subject. Any like him To Him a way is divided this in spite of. In general estimate it like this when being the compraventa well.
5 / 5 Kathlyn
Writes in simple English but require some fund analyses no parametric to take a fundamentals that is explained in this book. It is very organised and does not require for excusarse like this often when the technical explanation is required.
5 / 5 Lakesha
Probably a more read of a year in my final - has transformed totally a discharge of my work, and king-shaped likes to conquer questions of analysis of the data. Of then all the people learn different statistical approximations, has found this result of invaluable book 'conscious' of the like the technicians & of tools are available to use for questions of concrete analysis.
5 / 5 Carita
A book is to a large extent written for an executive any one knows a @@subject a late plus lang'.
A lot in fact resupply practical examples of like this to do datamining or included really best practices.
4 / 5 Libbie
This book is incredibly useful for any only entering a field of science of data those who has the basic comprising of statisticians and mathematical concepts. Some pauses of book have has complicated concepts down to uses and simple terms a lot of visuals. Ideal for any the one who will be overseeing a work of scientists of data and included taking the analytics they.
5 / 5 Shaunda
Perfecto! Finally the book that concepts of coverages and the business chances in tongue can read.
5 / 5 Jeremy
Directs partorisca step a line among being too imprecise to be any gain and like this concretise that requires the tonne of mathematics for the follow. The introduction adds ot one of some hot subjects of a moment.
5 / 5 Meghann
Introduction really well the science of data for manager of business / leaders that look to build the basic comprising of science of data. It maintains some simple and pertinent concepts to the business context. A same time; he the work adds that it explains like this to comprise and link with some scientists of data in your crew
5 / 5 Lucretia
Like this Adoptive Provost and Tom Fawcett explains in a Preface, examine concepts that inner fallen one of three types:

'1. Concepts roughly like accesses of science of the data to an organisation and a competitive landscape, comprising ways to attract, structure, and nurture squads of science of the data; to ways to think roughly likes them the favour of science of the data the competitive advantage; and tactical concepts partorisca do well with projects of science of the data.

2. General ways partorisca think die, analytically. This help in identifying pertinent data and consider pertinent methods. Some concepts comprise a [@begin italics] given mining process [italics of final] as well as a collection of different [@begin italics] big-date partorisca level fulfilled mining. [Italics Of final]

3. General concepts partorisca in fact extracting knowledge of data, which undergird a vast variety of tasks of science of the data and his algorithms.'

There have a character and spent of one THAT in as an information, ideas, and consulting of house. Provost And Fawcett consecrate the majority of his attention partorisca explain LIKE THIS partorisca apply these concepts partorisca achieve big-date of pipe of mining impact for data-analytical has thought. I share his faith 'this die partorisca explain science around such fundamental concepts a lot so only help a reader, also facilitates communication among and among business stakeholders and scientist of data. It resupplies the vocabulary shared and enables both parties [scientists of data and no-scientific of the dice the GustanI] to comprise each one which as another better. One has shared advantage of concepts to the deepest discussions that can it uncover critical subjects otherwise has lost.'
These are among some dozens of subjects and lovely business subjects and mine special interest, also listed to indicate a discharge of Provost and Fawcett coverage.

Or Of Big Data 1.0 to Big Data 2.0 (Pages 9-13)
or Of Business Questions to Mining Fulfilled Data(19-23)
or A Data Accuses Mining. (26-34)
Or Another Analytics Technical and Technologies (Pages 35-41 and 187-208)
or selecting Informative Attributes (49-56)
or the segmentation Supervised with Albero-has Structured Models (62-67)
or Estimativa of Probabilities of the Class and Logistic esgression' (97-100)
or Overfitting (113-119)
Note: Ossia the tendency to models of tailor to a data of formation.
Or Correlation of Similarity and Distance (142-144)
or Some Technical Details Of the entity that relates the Similarities and of the Neighbours (157-161)
or giving a very Behind: solving the Business Question Versus Exploitation of Data (183-185)
or A Tone of Analytical Frame: the value Expected (194-204)
or A Model of Impulse of Evidence' (244-246)
or the Analytical decision has thought II: The Analytical Engineering (279-289)
or has Sawed-occurrences and of the Associations: finding Elements That goes Near 292-298)
or Sprain, Variance, and Together Methods 308-311)
or sustaining Competitive Advantage with Science of Data (318-323)

To the equal that has done my way by means of a book the second time, in preparation to compose this description, was again adapted of commentaries for Eric Schmidt, executive president of Google: 'Of a dawn of civilisation until 2003, the humanity has generated five exabytes of data. Maintaining produced five exabytes each one that two one is not accelerating.' Correspondingly, Some challenge that this process of the accumulation of data creates will result even more adds. Provost And Fawcett has written this book so that they owe that direct this process but also to assist some tries to control those who now are preparing to do like this.
4 / 5 Madonna
Well, Is for the course. So that he a professor has said that it is the good product . It has it the be. It is well.

Top Customer Reviews: The Book of Why: ...

Rating: 4 out of 5 with 7 ratings
4 / 5 Nannie
Excellent introduction the causality. Has all has learnt that "the correlation is not causation" and Pearl explains reason this mantra short a risk partorisca launch a creature was with a bath-water. The calm big data give you correlation; the big data and the give causal form give you much more. A book is no-technical (any equation!) But act dipped more technical so that they want to undermine deeper. So only four stars because Pearl tends partorisca augment his own function while touching down a work of external entity.
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Well it corrects
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The reading has finalised 40 like this far

Top Customer Reviews: Naked Statistics: ...

Rating: 4 out of 5 with 23 ratings
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4 / 5 Magaly
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Whilst Reading this, found me really enjoying a book. A structure and content of a book does not feel like the textbook but instead explains statistical concepts in the practices and involving way. It is very easy to follow and perfect for readers of novice or any one looking to learn more in statistics.
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5 / 5 Marty
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