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Python for Data Science For Dummies
Unleash the power of Python for your data analysis projects with For Dummies!
Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide.
Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLibWhether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.
432 pages, Kindle Edition
First published April 21, 2014
82 people are currently reading
326 people want to read
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Displaying 1 - 9 of 9 reviews
March 10, 2018
"Data Science mit Python für Dummies" wirbt auf dem Cover mit dem Text „Einführung in Python und die Programmumgebung Anaconda“, „solider Überblick über die Python-Bibliotheken für Data Science“, „Daten vorbereiten, verarbeiten, visualisieren“. Vor dem Hintergrund dieses Werbetextes und des Reihentitels "... für Dummies" habe ich eine (wenn auch nur kurze) Einführung in Python und die Datenanalyse erwartet, also ein Buch für Anfänger. Dieser Band baut jedoch auf Python programmieren lernen fr Dummies auf, das reine Einsteiger vorher lesen sollten.
Auf den ersten 55 Seiten werden zunächst die Vorzüge von Python blumig beschrieben. Inhaltlich ist praktisch nichts geboten. Anschließend wird die Installation der Programmierumgebung Anaconda erläutert, leider jedoch nicht, was Anaconda eigentlich ist. An dieser Stelle fehlt, welche Programme und zugehörigen Bibliotheken dieses Buch nutzt. Noch wichtiger: dass Anakonda neben der reinen Programmierumgebung auch alle diese Bibliotheken mitbringt. Stattdessen wird man in dem Buch plötzlich mit einem System namens Python Notebook konfrontiert und hat keine Vorstellung, warum und wofür (das muss man einfach wissen). Auch sollen bereits Beispiele in Python Notebook eingegeben werden, noch bevor die Installation beschrieben wurde. So zieht sich das Buch weitere ca. 50 Seiten hin. Erst wenn es um den Einsatz der einzelnen Bibliotheken geht, wird das Buch etwas systematischer und geht auf die einzelnen Data Science Bbibliotheken ein. Dabei fehlen …
- eine Einführung in Python, die man aufgrund des Titelblatts erwarten könnte
- eine systematische Einführung in Data Science
- ein umfangreiches, durchgehendes Beispiel, um die Datenanalyse, Komprimierung, Aufbereitung und Darstellung in einem größeren Zusammenhang von Anfang bis Ende zu zeigen.
Stilistisch wirkt das Buch durchgehend verschwurbelt, in Bandwurm-Satzungetümen umständlich und dadurch teils unpräzise. Satzkonstruktionen aus bis zu 6 Teilsätzen müssen auch in Fachtexten nicht sein. Die Marotte, das Verb häufig ans Ende dieser langen Sätze zu stellen, anstatt möglichst weit vorn, macht die Misere nicht besser. Ich hatte ständig den Eindruck, dass ich mit dem englischen Original besser klarkommen würde, weil ich dann wenigstens nicht darüber nachdenken müsste, ob die Übersetzung den Text verschlimmert hat.
Das Layout der Printausgabe ist von mäßiger Qualität. Die notwendigen unterschiedlichen Schrifttypen könnten deutlicher voneinander zu unterscheiden sein. Die ebook-Ausgabe für Tablet und Smartphone zeigt immerhin noch eine zweite Schriftfarbe für die Überschriften und farbige Icons, die auf dem ebook-Reader leider nicht nutzbar ist.
--> Nutzen Sie bitte vor dem Kauf den Blick ins Buch und laden evtl. eine ebook-Leseprobe der Originalausgabe.
Fazit:
Um Data Science mit Python sinnvoll zu nutzen, sollte Python bereits bekannt sein, sowie die Vorgehensweisen und Systematiken des Data Science. Ein Anfänger, der die Hürde mit der Programmumgebung und Python nimmt, kennt anschließend immerhin viele Werkzeuge in Python und wie man sie nutzen kann. Deshalb 3 Sterne für den sachlichen Inhalt, für die Systematik und Didaktik des Buches allerdings nur einen.
Auf den ersten 55 Seiten werden zunächst die Vorzüge von Python blumig beschrieben. Inhaltlich ist praktisch nichts geboten. Anschließend wird die Installation der Programmierumgebung Anaconda erläutert, leider jedoch nicht, was Anaconda eigentlich ist. An dieser Stelle fehlt, welche Programme und zugehörigen Bibliotheken dieses Buch nutzt. Noch wichtiger: dass Anakonda neben der reinen Programmierumgebung auch alle diese Bibliotheken mitbringt. Stattdessen wird man in dem Buch plötzlich mit einem System namens Python Notebook konfrontiert und hat keine Vorstellung, warum und wofür (das muss man einfach wissen). Auch sollen bereits Beispiele in Python Notebook eingegeben werden, noch bevor die Installation beschrieben wurde. So zieht sich das Buch weitere ca. 50 Seiten hin. Erst wenn es um den Einsatz der einzelnen Bibliotheken geht, wird das Buch etwas systematischer und geht auf die einzelnen Data Science Bbibliotheken ein. Dabei fehlen …
- eine Einführung in Python, die man aufgrund des Titelblatts erwarten könnte
- eine systematische Einführung in Data Science
- ein umfangreiches, durchgehendes Beispiel, um die Datenanalyse, Komprimierung, Aufbereitung und Darstellung in einem größeren Zusammenhang von Anfang bis Ende zu zeigen.
Stilistisch wirkt das Buch durchgehend verschwurbelt, in Bandwurm-Satzungetümen umständlich und dadurch teils unpräzise. Satzkonstruktionen aus bis zu 6 Teilsätzen müssen auch in Fachtexten nicht sein. Die Marotte, das Verb häufig ans Ende dieser langen Sätze zu stellen, anstatt möglichst weit vorn, macht die Misere nicht besser. Ich hatte ständig den Eindruck, dass ich mit dem englischen Original besser klarkommen würde, weil ich dann wenigstens nicht darüber nachdenken müsste, ob die Übersetzung den Text verschlimmert hat.
Das Layout der Printausgabe ist von mäßiger Qualität. Die notwendigen unterschiedlichen Schrifttypen könnten deutlicher voneinander zu unterscheiden sein. Die ebook-Ausgabe für Tablet und Smartphone zeigt immerhin noch eine zweite Schriftfarbe für die Überschriften und farbige Icons, die auf dem ebook-Reader leider nicht nutzbar ist.
--> Nutzen Sie bitte vor dem Kauf den Blick ins Buch und laden evtl. eine ebook-Leseprobe der Originalausgabe.
Fazit:
Um Data Science mit Python sinnvoll zu nutzen, sollte Python bereits bekannt sein, sowie die Vorgehensweisen und Systematiken des Data Science. Ein Anfänger, der die Hürde mit der Programmumgebung und Python nimmt, kennt anschließend immerhin viele Werkzeuge in Python und wie man sie nutzen kann. Deshalb 3 Sterne für den sachlichen Inhalt, für die Systematik und Didaktik des Buches allerdings nur einen.
September 9, 2019
This book is a good introduction to Python and data science that covers a broad range of topics. I give it four stars for content.
In terms of writing, formatting, and overall quality, I would give it two stars at most. The book seems to be published in a hurry without any editing.
1) There are at least a dozen errors in the book. For example, at one place it says that values above upper quartile and below lower quartile are outliers. This is totally wrong since each accounts for 25% of the data! Later chapters do provide the correct information. Several similar incidents give the distinct impression that the book is written by two authors.
2) Many terms are used inconsistently or even incorrectly throughout the book, including samples, examples, variables, predictors, classes, models, algorithms, etc. Again, it is likely due to the different authorship and lack of editing.
3) Introduction of datasets and concepts is out of order. For example, a dataset is used in a task, then a few chapters later, the same dataset appears again with a lengthy explanation of its background and details. Same with concepts - direct use first, definition later.
4) Sloppy writing. Lots of sentences are simply awkward. For example, "Each tree tries to build a model that successfully predicts what trees that were built before it weren't able to forecast". Repetitive words and phrases abound - A paragraph with five lines might include the word 'example' four times; "In fact" appears at least 100 times in the book.
I won't list all the formatting problems in case people think I am OCD.
To summarize, this book gives a good overview of Python and Data Science to get people started in the field. A thorough careful editing would make it more valuable and less annoying.
In terms of writing, formatting, and overall quality, I would give it two stars at most. The book seems to be published in a hurry without any editing.
1) There are at least a dozen errors in the book. For example, at one place it says that values above upper quartile and below lower quartile are outliers. This is totally wrong since each accounts for 25% of the data! Later chapters do provide the correct information. Several similar incidents give the distinct impression that the book is written by two authors.
2) Many terms are used inconsistently or even incorrectly throughout the book, including samples, examples, variables, predictors, classes, models, algorithms, etc. Again, it is likely due to the different authorship and lack of editing.
3) Introduction of datasets and concepts is out of order. For example, a dataset is used in a task, then a few chapters later, the same dataset appears again with a lengthy explanation of its background and details. Same with concepts - direct use first, definition later.
4) Sloppy writing. Lots of sentences are simply awkward. For example, "Each tree tries to build a model that successfully predicts what trees that were built before it weren't able to forecast". Repetitive words and phrases abound - A paragraph with five lines might include the word 'example' four times; "In fact" appears at least 100 times in the book.
I won't list all the formatting problems in case people think I am OCD.
To summarize, this book gives a good overview of Python and Data Science to get people started in the field. A thorough careful editing would make it more valuable and less annoying.
August 30, 2025
I absolutely loved this self-guidebook on Data Science! It took me on a deep dive into data analysis, building on what I had already learned from previous books. The book provides a comprehensive understanding of the various tools in the field of data science. It gets straight to the point on how to apply Python in data science and introduces ML concepts effectively. It's a fantastic overview and guide, especially if you already have a good grasp of SQL, Power BI, and Tableau. The insights on machine learning were particularly enlightening and have prepared me well for future roles. Highly recommended!
March 4, 2025
Solid book giving an overview of Python and using it for Data Science. It did not feel wonderful and have anything groundbreaking and some ml parts could use explanation in layman’s terms. Good educational effort!
August 1, 2018
High level introduction to lots of topics. Its a good overview, but I thought there was a lack of depth.
June 28, 2020
Just okay for me.
April 28, 2016
re you a Beginner who would like to learn python, in context with a specific area, and tired of using syntax focused books sans practical examples? OR
Are you exploring data science landscape and want to see practical examples of how to actually use machine learning algorithms in data science context?
If you answer in the affirmative to either of the questions above, "Python for data science for dummies" is the perfect book for you. Luca Massaron is a practicing data scientist, and a prolific author of several books including Regression Analysis with Python , Machine Learning For Dummies, Python Data Science Essentials, Regression Analysis with Python, and Large Scale Machine Learning with Python. He is also a leading Kaggle enthusiast, and you can see his 'practitioner fingerprints' all over this book; especially in later chapters about data processing, ETL, cleanup, data sources, and challenges.
This book starts with the fundamentals of Python data analysis programming, and explains the setup of Python development environment using anaconda with IPython (Jupyter notebooks). Authors start by considering the emergence of data science, outline the core competencies of a data scientist, and describe the Data Science Pipeline before taking a plunge into explaining Python’s Role in Data Science and introducing Python’s Capabilities and Wonders.
Once you get your bearings about the IDE setup, chapter 4 focuses on Basic Python before you get your Hands Dirty with Data. What I like about this manuscript is that the writing keeps it real. Instead of giving made up examples, authors talk about items like knowing when to use NumPy or pandas and real world scenarios like removing duplicates, creating a data map and data plan, dealing with Dates in Your Data, Dealing with Missing data, parsing etc; problems which practicing data scientists encounter on a daily basis.
Contemporary topics like Text mining are also addressed in the book with enough details of topics such as working with Raw Text, Stemming and removing stop words, Bag of Words Model and Beyond, Working with n‐grams, Implementing TF‐IDF transformations, and adjacency matrix handling. This is also where you start getting a basic understanding of how machine learning algorithms work in practice.
Practical aspects of evaluating a data science problem are addressed later, with techniques defined for researching solutions, formulating a hypothesis, data preperation, feature creation, binning and discretization, leading up to vectors and matrix manipulation, and visualization with MatPlotLib. Even though the book does not discuss theano, DL4J, Torch, Caffe or TensorFlow, it still provides an introduction to key python ML library Scikit‐learn. This 400 page book also covers key topics like SVD, PCA, NMF, Recommendation systems, Clustering, Detecting Outliers, logistic Regression, Naive Bayes, Fitting a Model, bias and variance, Support Vector Machines, and Random Forest classifiers to name a few. The resources provided in the end are definitely worth subscribing to for every self-respecting data science enthusiast.
I highly recommend this book for those beginners interested in data science and also want to learn and leverage Python skills for this rapidly emerging field.
Are you exploring data science landscape and want to see practical examples of how to actually use machine learning algorithms in data science context?
If you answer in the affirmative to either of the questions above, "Python for data science for dummies" is the perfect book for you. Luca Massaron is a practicing data scientist, and a prolific author of several books including Regression Analysis with Python , Machine Learning For Dummies, Python Data Science Essentials, Regression Analysis with Python, and Large Scale Machine Learning with Python. He is also a leading Kaggle enthusiast, and you can see his 'practitioner fingerprints' all over this book; especially in later chapters about data processing, ETL, cleanup, data sources, and challenges.
This book starts with the fundamentals of Python data analysis programming, and explains the setup of Python development environment using anaconda with IPython (Jupyter notebooks). Authors start by considering the emergence of data science, outline the core competencies of a data scientist, and describe the Data Science Pipeline before taking a plunge into explaining Python’s Role in Data Science and introducing Python’s Capabilities and Wonders.
Once you get your bearings about the IDE setup, chapter 4 focuses on Basic Python before you get your Hands Dirty with Data. What I like about this manuscript is that the writing keeps it real. Instead of giving made up examples, authors talk about items like knowing when to use NumPy or pandas and real world scenarios like removing duplicates, creating a data map and data plan, dealing with Dates in Your Data, Dealing with Missing data, parsing etc; problems which practicing data scientists encounter on a daily basis.
Contemporary topics like Text mining are also addressed in the book with enough details of topics such as working with Raw Text, Stemming and removing stop words, Bag of Words Model and Beyond, Working with n‐grams, Implementing TF‐IDF transformations, and adjacency matrix handling. This is also where you start getting a basic understanding of how machine learning algorithms work in practice.
Practical aspects of evaluating a data science problem are addressed later, with techniques defined for researching solutions, formulating a hypothesis, data preperation, feature creation, binning and discretization, leading up to vectors and matrix manipulation, and visualization with MatPlotLib. Even though the book does not discuss theano, DL4J, Torch, Caffe or TensorFlow, it still provides an introduction to key python ML library Scikit‐learn. This 400 page book also covers key topics like SVD, PCA, NMF, Recommendation systems, Clustering, Detecting Outliers, logistic Regression, Naive Bayes, Fitting a Model, bias and variance, Support Vector Machines, and Random Forest classifiers to name a few. The resources provided in the end are definitely worth subscribing to for every self-respecting data science enthusiast.
I highly recommend this book for those beginners interested in data science and also want to learn and leverage Python skills for this rapidly emerging field.
November 15, 2016
Good introduction to Python and its uses in data science. Also a good reference for other resources out there.
September 24, 2019
A bit weak on python, the reader should know the basics of python
A good overview of ml models
A good overview of ml models
Displaying 1 - 9 of 9 reviews