Books: **14**

Cover | Title | Year |
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Your ticket to breaking into the field of data science! Jobs in data science are projected to outpace the number of people with data science skills—making those with the knowledge to fill a data science position a hot commodity in the coming years. Data Science For Dummies is the perfect starting point more » for IT professionals and students interested in making sense of an organization's massive data sets and applying their findings to real-world business scenarios.
From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you'll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization.
* Provides a background in data science fundamentals and preparing your data for analysis
* Details different data visualization techniques that can be used to showcase and summarize your data
* Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques
* Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark
It's a big, big data world out there—let Data Science For Dummies help you harness its power and gain a competitive edge for your organization. « less | 2017 | |

A Python Approach to Concepts, Techniques and Applications This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, more » and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website. « less | 2017 | |

KEY FEATURES
* Your entry ticket to the world of data science with the stability and power of Java
* Explore, analyse, and visualize your data effectively using easy-to-follow examples
* Make your Java applications more capable using machine learning
BOOK DESCRIPTION
Data science is concerned more » with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this book, we cover the important data science concepts and how they are supported by Java, as well as the often statistically challenging techniques, to provide you with an understanding of their purpose and application.
The book starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. The next section examines the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation.
The final chapter illustrates an in-depth data science problem and provides a comprehensive, Java-based solution. Due to the nature of the topic, simple examples of techniques are presented early followed by a more detailed treatment later in the book. This permits a more natural introduction to the techniques and concepts presented in the book.
WHAT YOU WILL LEARN
* Understand the nature and key concepts used in the field of data science
* Grasp how data is collected, cleaned, and processed
* Become comfortable with key data analysis techniques
* See specialized analysis techniques centered on machine learning
* Master the effective visualization of your data
* Work with the Java APIs and techniques used to perform data analysis « less | 2017 | |

Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world problems with data. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming more » in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling statistical questions.
Contemporary data science requires a tight integration of knowledge from statistics, computer science, mathematics, and a domain of application. This book will help readers with some background in statistics and modest prior experience with coding develop and practice the appropriate skills to tackle complex data science projects. The book features a number of exercises and has a flexible organization conducive to teaching a variety of semester courses. « less | 2017 | |

Learn the techniques and math you need to start making sense of your data KEY FEATURES
* Enhance your knowledge of coding with data science theory for practical insight into data science and analysis
* More than just a math class, learn how to perform real-world data science tasks with R and Python
* Create actionable insights and transform raw data into tangible value
BOOK more » DESCRIPTION
Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking―and answering―complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas.
With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You'll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.
WHAT YOU WILL LEARN
* Get to know the five most important steps of data science
* Use your data intelligently and learn how to handle it with care
* Bridge the gap between mathematics and programming
* Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results
* Build and evaluate baseline machine learning models
* Explore the most effective metrics to determine the success of your machine learning models
* Create data visualizations that communicate actionable insights
* Read and apply machine learning concepts to your problems and make actual predictions
ABOUT THE AUTHOR
Sinan Ozdemir is a data scientist, startup founder, and educator living in the San Francisco Bay Area with his dog, Charlie; cat, Euclid; and bearded dragon, Fiero. He spent his academic career studying pure mathematics at Johns Hopkins University before transitioning to education. He spent several years conducting lectures on data science at Johns Hopkins University and at the General Assembly before founding his own start-up, Legion Analytics, which uses artificial intelligence and data science to power enterprise sales teams.
After completing the Fellowship at the Y Combinator accelerator, Sinan has spent most of his days working on his fast-growing company, while creating educational material for data science.
TABLE OF CONTENTS
1. How to Sound Like a Data Scientist
2. Types of Data
3. The Five Steps of Data Science
4. Basic Mathematics
5. Impossible or Improbable – A Gentle Introduction to Probability
6. Advanced Probability
7. Basic Statistics
8. Advanced Statistics
9. Communicating Data
10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials
11. Predictions Don't Grow on Trees – or Do They?
12. Beyond the Essentials
13. Case Studies « less | 2017 | |

KEY FEATURES
* Optimize your work flow with Spark in data science, and get solutions to all your big data problems
* Large-scale data science made easy with Spark
* Get recipes to make the most of Spark's power and speed in predictive analytics
BOOK DESCRIPTION
Spark has emerged as the big data more » platform of choice for data scientists. The real power and value proposition of Apache Spark is its platform to execute data science tasks. Spark's unique use case is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations to allow data scientists to tackle the complexities that come with raw unstructured data sets.
This hands-on, practical resource will allow you to dive in and become comfortable and confident in working with Spark for data science. We will walk you through various techniques to deal with simple and complex data science tasks with Spark. We'll effectively offer solutions to problematic concepts in data science using Spark's data science libraries. The book will help you derive intelligent information at every step of the way through simple yet efficient recipes that will not only show you how to implement algorithms, but also optimize your work.
WHAT YOU WILL LEARN
* Explore the topics of data mining, text mining, NLP, information retrieval, and machine learning
* Solve real-world analytical problems with large data sets
* Get the flavor of challenges in data science and address them with a variety of analytical tools on a distributed system like Spark (apt for iterative algorithms), which offers in-memory processing and more flexibility for data analysis at scale « less | 2017 | |

To tap into the power of Python's open data science stack—including NumPy, Pandas, Matplotlib, Scikit-learn, and other tools—you first need to understand the syntax, semantics, and patterns of the Python language. This report provides a brief yet comprehensive introduction to Python for engineers, researchers, more » and data scientists who are already familiar with another programming language.
Author Jake VanderPlas, an interdisciplinary research director at the University of Washington, explains Python’s essential syntax and semantics, built-in data types and structures, function definitions, control flow statements, and more, using Python 3 syntax.
You’ll explore:
- Python syntax basics and running Python code
Basic semantics of Python variables, objects, and operators
- Built-in simple types and data structures
- Control flow statements for executing code blocks conditionally
- Methods for creating and using reusable functions
Iterators, list comprehensions, and generators
- String manipulation and regular expressions
- Python’s standard library and third-party modules
- Python’s core data science tools
- Recommended resources to help you learn more « less | 2016 | |

This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical more » data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses.
This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials.
This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners. « less | 2016 | |

Learn Data Science Programming in Python including munging, aggregating, and visualizing data. | 2016 | |

Designing and Building Effective Analytics at Scale The Complete Guide to Data Science with Hadoop—For Technical Professionals, Businesspeople, and Students
Demand is soaring for professionals who can solve real data science problems with Hadoop and Spark. Practical Data Science with Hadoop® and Spark is your complete guide to doing just that. Drawing more » on immense experience with Hadoop and big data, three leading experts bring together everything you need: high-level concepts, deep-dive techniques, real-world use cases, practical applications, and hands-on tutorials.
The authors introduce the essentials of data science and the modern Hadoop ecosystem, explaining how Hadoop and Spark have evolved into an effective platform for solving data science problems at scale. In addition to comprehensive application coverage, the authors also provide useful guidance on the important steps of data ingestion, data munging, and visualization.
Once the groundwork is in place, the authors focus on specific applications, including machine learning, predictive modeling for sentiment analysis, clustering for document analysis, anomaly detection, and natural language processing (NLP).
This guide provides a strong technical foundation for those who want to do practical data science, and also presents business-driven guidance on how to apply Hadoop and Spark to optimize ROI of data science initiatives.
Learn
* What data science is, how it has evolved, and how to plan a data science career
* How data volume, variety, and velocity shape data science use cases
* Hadoop and its ecosystem, including HDFS, MapReduce, YARN, and Spark
* Data importation with Hive and Spark
* Data quality, preprocessing, preparation, and modeling
* Visualization: surfacing insights from huge data sets
* Machine learning: classification, regression, clustering, and anomaly detection
* Algorithms and Hadoop tools for predictive modeling
* Cluster analysis and similarity functions
* Large-scale anomaly detection
* NLP: applying data science to human language « less | 2016 |

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