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Fundamentals of Software Architecture PDF
By:Mark Richards,Neal Ford
Published on 2020-01-28 by O'Reilly Media

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Salary surveys worldwide regularly place software architect in the top 10 best jobs, yet no real guide exists to help developers become architects. Until now. This book provides the first comprehensive overview of software architecture’s many aspects. Aspiring and existing architects alike will examine architectural characteristics, architectural patterns, component determination, diagramming and presenting architecture, evolutionary architecture, and many other topics. Mark Richards and Neal Ford—hands-on practitioners who have taught software architecture classes professionally for years—focus on architecture principles that apply across all technology stacks. You’ll explore software architecture in a modern light, taking into account all the innovations of the past decade. This book examines: Architecture patterns: The technical basis for many architectural decisions Components: Identification, coupling, cohesion, partitioning, and granularity Soft skills: Effective team management, meetings, negotiation, presentations, and more Modernity: Engineering practices and operational approaches that have changed radically in the past few years Architecture as an engineering discipline: Repeatable results, metrics, and concrete valuations that add rigor to software architecture

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Book which was published by O'Reilly Media since 2020-01-28 have ISBNs, ISBN 13 Code is 9781492043423 and ISBN 10 Code is 1492043427

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The Healthy PC PDF
By:Carey Holzman
Published on 2003 by McGraw Hill Professional

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Provides tips on basic computer maintenance to keep a PC running smoothly, covering such topics as using Scandisk and defragmentation, managing cookies, backing up files, driver upgrades, and optimizing Internet connections.

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Manipulating data with R PDF
By:Valentina Porcu
Published on 2017-07-05 by Valentina Porcu

This book is meant to be an introduction to advanced data manipulation in R. The first chapter will deal with R structures, vectors, matrixes, lists, and dataframes. We will explain how to design objects in R and how to use R main functions, such as rearranging a vector or adding columns to a matrix. The second chapter will cover several topics. We will see how to generate random sequences, how to make a subsetting, use conditional instructions and logical operators and, more in depth, how to deal with the missing values. Third chapter is about importing data on R using various formats, mainly .csv, but also Excel, .txt, and .sav data The fourth chapter will introduce data processing and manipulation through some functions and packages, including dplyr and reshape2 In the fifth chapter we will see how to use the data.table package to handle big dataset. In the sixth chapter, we will see how to manipulate strings and text data using various strings management functions. We will also deal with the use of regular expressions. The seventh chapter will be about functions - how to write a function and all the conditional operators that make our functions more complex. Last, in chapter eight, we will quickly take a glance at how to export and reuse a model of analysis on other software using XML and the pmml package.

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Book which was published by Valentina Porcu since 2017-07-05 have ISBNs, ISBN 13 Code is 9788826478838 and ISBN 10 Code is 882647883X

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Learning Python for data mining PDF
By:Valentina Porcu
Published on 2017-07-29 by

My goal is to accompany a reader who is starting to study this programming language, showing her through basic concepts and then move to data mining. We will begin by explaining how to use Python and its structures, how to install Python, which tools are best suited for a data analyst work, and then switch to an introduction to data mining packages. The book is in any case an introduction. Its aim is not, for instance, to fully explain topics such as machine learning or statistics with this programming language, which would take at least twice or three times as much as this entire book. The aim is to provide a guidance from the first programming steps with Python to manipulation and import of datasets, to some examples of data analysis. To be more precise, in the Getting Started section, we will run through some basic installation concepts, tools available for programming on Python, differences between Python2 and Python3, and setting up a work folder. In Chapter 1, we will begin to see some basic concepts about creating objects, entering comments, reserved words for the system, and on the various types of operators that are part of the grammar of this programming language. In Chapter 2, we will carry on with the basic Python structures, such as tuples, lists, dictionaries, sets, strings, and files, and learn how to create and convert them. In Chapter 3 we will see the basics for creating small basic functions, and how to save them. Chapter 4 deals with conditional instructions that allow us to extend the power of a function as well as some important functions. In Chapter 5 we will keep talking about some basic concepts related to object-oriented programming, concept of module, method, and error handling. Chapter 6 is dedicated to importing files with some of the basic features. We will see how to open and edit text files, in .csv format, and in various other formats. Chapters 7 to 10 will deal with Python's most important data mining packages: Numpy and Scipy for mathematical functions and random data generation, pandas for dataframe management and data import, Matplotlib for drawing charts and scikit-learn for machine learning. With regard to scikit-learn, we will limit ourselves to provide a basic idea of the code of the various algorithms, without going, given the complexity of the subject, into details for the various techniques. Finally, in Conclusions, we will summarize the topics and concepts of the book and see the management of dates and some of the data sources for our tests with Python. This book is intended for those who want to get closer to the Python programming language from a data analysis perspective. We will therefore focus on the most used packages for data analysis, after the introduction to Python's basic concepts.

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Book which was published by since 2017-07-29 have ISBNs, ISBN 13 Code is 9788822803955 and ISBN 10 Code is 8822803957

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Learning Python for data mining PDF
By:Valentina Porcu
Published on 2017-07-29 by

My goal is to accompany a reader who is starting to study this programming language, showing her through basic concepts and then move to data mining. We will begin by explaining how to use Python and its structures, how to install Python, which tools are best suited for a data analyst work, and then switch to an introduction to data mining packages. The book is in any case an introduction. Its aim is not, for instance, to fully explain topics such as machine learning or statistics with this programming language, which would take at least twice or three times as much as this entire book. The aim is to provide a guidance from the first programming steps with Python to manipulation and import of datasets, to some examples of data analysis. To be more precise, in the Getting Started section, we will run through some basic installation concepts, tools available for programming on Python, differences between Python2 and Python3, and setting up a work folder. In Chapter 1, we will begin to see some basic concepts about creating objects, entering comments, reserved words for the system, and on the various types of operators that are part of the grammar of this programming language. In Chapter 2, we will carry on with the basic Python structures, such as tuples, lists, dictionaries, sets, strings, and files, and learn how to create and convert them. In Chapter 3 we will see the basics for creating small basic functions, and how to save them. Chapter 4 deals with conditional instructions that allow us to extend the power of a function as well as some important functions. In Chapter 5 we will keep talking about some basic concepts related to object-oriented programming, concept of module, method, and error handling. Chapter 6 is dedicated to importing files with some of the basic features. We will see how to open and edit text files, in .csv format, and in various other formats. Chapters 7 to 10 will deal with Python's most important data mining packages: Numpy and Scipy for mathematical functions and random data generation, pandas for dataframe management and data import, Matplotlib for drawing charts and scikit-learn for machine learning. With regard to scikit-learn, we will limit ourselves to provide a basic idea of the code of the various algorithms, without going, given the complexity of the subject, into details for the various techniques. Finally, in Conclusions, we will summarize the topics and concepts of the book and see the management of dates and some of the data sources for our tests with Python. This book is intended for those who want to get closer to the Python programming language from a data analysis perspective. We will therefore focus on the most used packages for data analysis, after the introduction to Python's basic concepts.

This Book was ranked at 3 by Google Books for keyword Valentina.

Book ID of Learning Python for data mining's Books is d7ouDwAAQBAJ, Book which was written byValentina Porcuhave ETAG "ElgJq9tHhpc"

Book which was published by since 2017-07-29 have ISBNs, ISBN 13 Code is 9788822803955 and ISBN 10 Code is 8822803957

Reading Mode in Text Status is true and Reading Mode in Image Status is false

Book which have " Pages" is Printed at BOOK under CategoryComputers

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