Einführung in Data Science

Einführung in Data Science Dieses Buch F Hrt Sie In Data Science Ein, Indem Es Grundlegende Prinzipien Der Datenanalyse Erl Utert Und Ihnen Geeignete Techniken Und Werkzeuge Vorstellt Sie Lernen Nicht Nur, Wie Sie Bibliotheken, Frameworks, Module Und Toolkits Konkret Einsetzen, Sondern Implementieren Sie Auch Selbst Dadurch Entwickeln Sie Ein Tieferes Verst Ndnis F R Die Zusammenh Nge Und Erfahren, Wie Essenzielle Tools Und Algorithmen Der Datenanalyse Im Kern FunktionierenFalls Sie Programmierkenntnisse Und Eine Gewisse Sympathie F R Mathematik Mitbringen, Unterst Tzt Joel Grus Sie Dabei, Mit Den Mathematischen Und Statistischen Grundlagen Der Data Science Vertraut Zu Werden Und Sich Programmierf Higkeiten Anzueignen, Die Sie F R Die Praxis Ben Tigen Dabei Verwendet Er Python Die Weitverbreitete Sprache Ist Leicht Zu Erlernen Und Bringt Zahlreiche Bibliotheken F R Data Science MitAus Dem Inhalt Absolvieren Sie Einen Crashkurs In Python Lernen Sie Die Grundlagen Von Linearer Algebra, Statistik Und Wahrscheinlichkeitsrechnung Kennen Und Erfahren Sie, Wie Diese In Data Science Eingesetzt Werden Sammeln, Untersuchen, Bereinigen, Bearbeiten Und Manipulieren Sie Daten Tauchen Sie In Die Welt Des Maschinellen Lernens Ein Implementieren Sie Modelle Wie K Nearest Neighbors, Naive Bayes, Lineare Und Logistische Regression, Entscheidungsb Ume, Neuronale Netzwerke Und Clustering Entdecken Sie Empfehlungssysteme, Sprachverarbeitung, Netzwerkanalyse, MapReduce Und Datenbanken

Is a well-known author, some of his books are a fascination for readers like in the Einführung in Data Science book, this is one of the most wanted Joel Grus author readers around the world.

❮EPUB❯ ✽ Einführung in Data Science  ✸ Author Joel Grus – Webcambestmilf.info
  • Kindle Edition
  • 352 pages
  • Einführung in Data Science
  • Joel Grus
  • English
  • 15 June 2019

10 thoughts on “Einführung in Data Science

  1. says:

    I m still struggling to find the book I want around data science I ve learned that there are two levels 1 KNOWING data science2 DOING data scienceThis book is about the second one Make no mistake, this is a statistical computation manual This shows you how to find statistical answers using Python Fully half this book is code samples. If you do not plan to actually attempt to find statistical answers to known questions by writing Python code, then this isn t the book for you.I would look at the code samples in this book and think, What am I supposed to do with this I ll take the author s word for it that this works, but what is it supposed to tell me The code samples don t even show much inner computation, since most of the work is rolled up into Python libraries, and the code samples really just show magical method calls and the code around those This is damn near a Python manual.And I disagree with the title from Scratch It s not from scratch, and this is my major complaint knowing how to find the answer is the second half of the process The major problem is this no one knows the right questions. I can find or hire someone to give me the answer Explain to me what questions I should be asking of data.And this is where the book falls down The scenarios described are enormously contrived, and they re glossed over in a mad rush to get to the code samples the very part I didn t care about I want time spent on why the question matters Real world examples would be nice too.I get that this might not be what the author was going for But I fault him for the title It s not Data Science from Scratch It s, How To Compute Statistics with Python I guess I should have paid attention to the subtitle.So, this is my problem with this book, and with about every book I ve read on data science in the last two years All these books are written by statisticians who are very quick to show you math or code I want a book written by a business person that starts with the idea of what solutions we can unlock from our data.How to find those solutions That s a readily solvable problem.

  2. says:

    I worked thru all of the examples in this book Rather than have you import numpy and pandas and scikit learn, he walks you through how to build up these tools yourself What you build will be terribly inefficient and you should never use them in real life, but you will get a great feel for how they work under the hood I also learned that my linear algebra is very rusty and I need a brush up I disagree with some of the reviews that they he doesn t do a good job explaining the computation he does that in the comments of the code, where he walks you step by step thru what s he s doing A great intro for a beginner like me.

  3. says:

    Decent book on introduction to data science using Python.BTW, we should seriously stop writing books on elementary data science using R or Python We have too many and they already started to look alike.

  4. says:

    The idea of the book is nice, I still think is a useful book, but 1 you ll not learn math behind this or the methods will be explained it s good for a programming, though 2 regarding programming part, I think that people would benefit if there were some actual exercises for them to do, not just type in this code attitude3 would be nice if all of the data sets are actually generated in a book, not just there is some data set with 2000 points, that I just pulled out of my ass 4 usage of numpy would be useful5 all of the titles which contain learn from scratch suck6 the whole american attitude with Congratulations You now work in DataScienster Welcome aboard is annoying and treats people like idiots.The book is actually not that bad as I described it, but sometimes, while working you ll be so annoyed but all of these details Good thing that s not expensive.

  5. says:

    to be read for purposes of demonstrating fundamentals most of work here can be accomplished much simpler with advanced libraries, but this type of text helps one to understand the why and the building blocks of elaborate practice.

  6. says:

    Good at Practicing entry projects exercises simple languageBad at lack of some required details in some sections outdated code the apps codes are not that useful in some sectionsOverall the book is a good refreshing read but not that good for studying

  7. says:

    Good introductory book on data science I would recommend this to people who wish to learn basic things in a hands on fashion.

  8. says:

    Really good overview, but needed a little information about which software packages implement the functionality discussed.

  9. says:

    A brief introduction to many concepts and step by step construction of a working code I would expect a little math and theory that s why I gave four stars instead of five.

  10. says:

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