Practical Data Science

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Practical Statistics for Data Scientists

Peter Bruce,Andrew Bruce
Practical Statistics for Data Scientists Book PDF

Author : Peter Bruce,Andrew Bruce
Publisher : "O'Reilly Media, Inc."
Page : 395 pages
File Size : 46,7 Mb
Release : 2017-05-10
Category : Computers
ISBN : 9781491952917

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Practical Statistics for Data Scientists by Peter Bruce,Andrew Bruce Pdf

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data

Practical Data Science for Information Professionals

David Stuart
Practical Data Science for Information Professionals Book PDF

Author : David Stuart
Publisher : Facet Publishing
Page : 200 pages
File Size : 47,8 Mb
Release : 2020-07-24
Category : Language Arts & Disciplines
ISBN : 9781783303441

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Practical Data Science for Information Professionals by David Stuart Pdf

Practical Data Science for Information Professionals provides an accessible introduction to a potentially complex field, providing readers with an overview of data science and a framework for its application. It provides detailed examples and analysis on real data sets to explore the basics of the subject in three principle areas: clustering and social network analysis; predictions and forecasts; and text analysis and mining. As well as highlighting a wealth of user-friendly data science tools, the book also includes some example code in two of the most popular programming languages (R and Python) to demonstrate the ease with which the information professional can move beyond the graphical user interface and achieve significant analysis with just a few lines of code. After reading, readers will understand: · the growing importance of data science · the role of the information professional in data science · some of the most important tools and methods that information professionals can use. Bringing together the growing importance of data science and the increasing role of information professionals in the management and use of data, Practical Data Science for Information Professionals will provide a practical introduction to the topic specifically designed for the information community. It will appeal to librarians and information professionals all around the world, from large academic libraries to small research libraries. By focusing on the application of open source software, it aims to reduce barriers for readers to use the lessons learned within.

Practical Data Science with SAP

Greg Foss,Paul Modderman
Practical Data Science with SAP Book PDF

Author : Greg Foss,Paul Modderman
Publisher : O'Reilly Media
Page : 333 pages
File Size : 52,8 Mb
Release : 2019-09-18
Category : Computers
ISBN : 9781492046417

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Practical Data Science with SAP by Greg Foss,Paul Modderman Pdf

Learn how to fuse today's data science tools and techniques with your SAP enterprise resource planning (ERP) system. With this practical guide, SAP veterans Greg Foss and Paul Modderman demonstrate how to use several data analysis tools to solve interesting problems with your SAP data. Data engineers and scientists will explore ways to add SAP data to their analysis processes, while SAP business analysts will learn practical methods for answering questions about the business. By focusing on grounded explanations of both SAP processes and data science tools, this book gives data scientists and business analysts powerful methods for discovering deep data truths. You'll explore: Examples of how data analysis can help you solve several SAP challenges Natural language processing for unlocking the secrets in text Data science techniques for data clustering and segmentation Methods for detecting anomalies in your SAP data Data visualization techniques for making your data come to life

Practical Data Science with R

Nina Zumel,John Mount
Practical Data Science with R Book PDF

Author : Nina Zumel,John Mount
Publisher : Manning Publications
Page : 416 pages
File Size : 53,9 Mb
Release : 2014-04-10
Category : Computers
ISBN : 1617291560

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Practical Data Science with R by Nina Zumel,John Mount Pdf

Summary Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics. Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels. This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed. What's Inside Data science for the business professional Statistical analysis using the R language Project lifecycle, from planning to delivery Numerous instantly familiar use cases Keys to effective data presentations About the Authors Nina Zumel and John Mount are cofounders of a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win-vector.com. Table of Contents PART 1 INTRODUCTION TO DATA SCIENCE The data science process Loading data into R Exploring data Managing data PART 2 MODELING METHODS Choosing and evaluating models Memorization methods Linear and logistic regression Unsupervised methods Exploring advanced methods PART 3 DELIVERING RESULTS Documentation and deployment Producing effective presentations

Practical Data Science with Python

Nathan George
Practical Data Science with Python Book PDF

Author : Nathan George
Publisher : Packt Publishing Ltd
Page : 621 pages
File Size : 52,9 Mb
Release : 2021-09-30
Category : Computers
ISBN : 9781801076654

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Practical Data Science with Python by Nathan George Pdf

Learn to effectively manage data and execute data science projects from start to finish using Python Key FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook Description Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science. The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion. As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments. By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source. What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is for The book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor’s, Master’s, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science. The book requires basic familiarity with Python. A "getting started with Python" section has been included to get complete novices up to speed.

Practical Data Science

Andreas François Vermeulen
Practical Data Science Book PDF

Author : Andreas François Vermeulen
Publisher : Apress
Page : 821 pages
File Size : 46,5 Mb
Release : 2018-02-21
Category : Computers
ISBN : 9781484230541

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Practical Data Science by Andreas François Vermeulen Pdf

Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets. The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions. What You'll Learn Become fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge Coordinate the handling of polyglot data types in a data lake for repeatable results Who This Book Is For Data scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers

Practical Data Science with Python 3

Ervin Varga
Practical Data Science with Python 3 Book PDF

Author : Ervin Varga
Publisher : Apress
Page : 468 pages
File Size : 46,9 Mb
Release : 2019-09-07
Category : Computers
ISBN : 9781484248591

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Practical Data Science with Python 3 by Ervin Varga Pdf

Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code. As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science and how to document data engineering practices. This book is a good starting point for people who want to gain practical skills to perform data science. All the code will be available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science. Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors. What You'll LearnPlay the role of a data scientist when completing increasingly challenging exercises using Python 3Work work with proven data science techniques/technologies Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big Data Apply theory of probability, statistical inference, and algebra to understand the data science practicesWho This Book Is For Anyone who would like to embark into the realm of data science using Python 3.

Practical Data Science Cookbook

Prabhanjan Tattar,Tony Ojeda,Sean Patrick Murphy,Benjamin Bengfort,Abhijit Dasgupta
Practical Data Science Cookbook Book PDF

Author : Prabhanjan Tattar,Tony Ojeda,Sean Patrick Murphy,Benjamin Bengfort,Abhijit Dasgupta
Publisher : Packt Publishing Ltd
Page : 428 pages
File Size : 55,8 Mb
Release : 2017-06-29
Category : Computers
ISBN : 9781787123267

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Practical Data Science Cookbook by Prabhanjan Tattar,Tony Ojeda,Sean Patrick Murphy,Benjamin Bengfort,Abhijit Dasgupta Pdf

Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and implement real-world projects in data science using R and Python Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn Learn and understand the installation procedure and environment required for R and Python on various platforms Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python Build a predictive model and an exploratory model Analyze the results of your model and create reports on the acquired data Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization

Practical Data Analysis

Hector Cuesta,Dr. Sampath Kumar
Practical Data Analysis Book PDF

Author : Hector Cuesta,Dr. Sampath Kumar
Publisher : Packt Publishing Ltd
Page : 338 pages
File Size : 44,9 Mb
Release : 2016-09-30
Category : Computers
ISBN : 9781785286667

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Practical Data Analysis by Hector Cuesta,Dr. Sampath Kumar Pdf

A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images A hands-on guide to understanding the nature of data and how to turn it into insight Who This Book Is For This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. What You Will Learn Acquire, format, and visualize your data Build an image-similarity search engine Generate meaningful visualizations anyone can understand Get started with analyzing social network graphs Find out how to implement sentiment text analysis Install data analysis tools such as Pandas, MongoDB, and Apache Spark Get to grips with Apache Spark Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you'll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark. Style and approach This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.

Practical Data Science with Hadoop and Spark

Ofer Mendelevitch,Casey Stella,Douglas Eadline
Practical Data Science with Hadoop and Spark Book PDF

Author : Ofer Mendelevitch,Casey Stella,Douglas Eadline
Publisher : Addison-Wesley Professional
Page : 463 pages
File Size : 48,5 Mb
Release : 2016-12-08
Category : Computers
ISBN : 9780134029726

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Practical Data Science with Hadoop and Spark by Ofer Mendelevitch,Casey Stella,Douglas Eadline Pdf

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 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

Topics in Data Science with Practical Examples

Abdolreza Abhari
Topics in Data Science with Practical Examples Book PDF

Author : Abdolreza Abhari
Publisher : Createspace Independent Publishing Platform
Page : 148 pages
File Size : 40,6 Mb
Release : 2018-09-26
Category : Electronic
ISBN : 1727124847

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Topics in Data Science with Practical Examples by Abdolreza Abhari Pdf

Data Science, sometimes known as methods of processing and analyzing massive data sets (Big Data), is a rapidly evolving field. This book teaches important topics of the emerging data science by providing simple and practical examples in R language. Initial chapters are about data collection and management at large scale, and then data analytics and applying statistical and machine learning models on the collected data are discussed in rest of the book. Ten important topics in data science are explained in ten chapters of this book with practical examples in Oracle SQL, R, Hadoop, and MapReduce. The fundamental of data management such as relational database systems, data mining and distributed computing with practical examples of SQL and implementing Hadoop and MapReduce are detailed in chapters 1 to 3. Regression and statistical analysis, neural networks, support vector machines and machine learning are explained in simple language together with R programming examples, in chapter 4 to 7. Natural language processing, recommendation systems and analyzing social networks graphs are explained in chapters 8 to 10 of this book. Dr. Abdolreza Abhari, a professor of computer science department at Ryerson University, has collected the material of this book after many years of teaching Data Science. With the background in computer science dating back to before the invention of the world wide web, professor Abhari has extensive experience in analyzing web and social network data and creating database systems for the companies and industrial sectors in Europe and North America. His teaching area in academia includes database systems, distributed systems, and data science for graduate and undergraduate students. Although this book is written for professionals and graduated students who have a university or college degree, it is also useful for whoever considers working in the data science industry.

Data Science on AWS

Chris Fregly,Antje Barth
Data Science on AWS Book PDF

Author : Chris Fregly,Antje Barth
Publisher : "O'Reilly Media, Inc."
Page : 524 pages
File Size : 45,7 Mb
Release : 2021-04-07
Category : Computers
ISBN : 9781492079361

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Data Science on AWS by Chris Fregly,Antje Barth Pdf

With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more

Practical DataOps

Harvinder Atwal
Practical DataOps Book PDF

Author : Harvinder Atwal
Publisher : Apress
Page : 289 pages
File Size : 46,5 Mb
Release : 2019-12-09
Category : Computers
ISBN : 9781484251041

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Practical DataOps by Harvinder Atwal Pdf

Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles. This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. What You Will LearnDevelop a data strategy for your organization to help it reach its long-term goals Recognize and eliminate barriers to delivering data to users at scale Work on the right things for the right stakeholders through agile collaboration Create trust in data via rigorous testing and effective data management Build a culture of learning and continuous improvement through monitoring deployments and measuring outcomes Create cross-functional self-organizing teams focused on goals not reporting lines Build robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data products Who This Book Is For Data science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production.

A Hands-On Introduction to Data Science

Chirag Shah
A Hands On Introduction to Data Science Book PDF

Author : Chirag Shah
Publisher : Cambridge University Press
Page : 459 pages
File Size : 51,5 Mb
Release : 2020-04-02
Category : Business & Economics
ISBN : 9781108472449

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A Hands-On Introduction to Data Science by Chirag Shah Pdf

An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.

Practical Data Analysis for Designed Experiments

Brian S. Yandell
Practical Data Analysis for Designed Experiments Book PDF

Author : Brian S. Yandell
Publisher : Routledge
Page : 452 pages
File Size : 54,8 Mb
Release : 2017-11-22
Category : Mathematics
ISBN : 9781351422994

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Practical Data Analysis for Designed Experiments by Brian S. Yandell Pdf

Placing data in the context of the scientific discovery of knowledge through experimentation, Practical Data Analysis for Designed Experiments examines issues of comparing groups and sorting out factor effects and the consequences of imbalance and nesting, then works through more practical applications of the theory. Written in a modern and accessible manner, this book is a useful blend of theory and methods. Exercises included in the text are based on real experiments and real data.