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Download or read book Into You For:Data written by Dawn Bradley and published by Xlibris Corporation. This book was released on 2012-02-29 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: Myra Rae Watts doesnt realize she has a stalker until a theft occurs. She has to find out who he is in order to reclaim her losses.
Book Synopsis R for Data Science by : Hadley Wickham
Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Book Synopsis Python for Data Science For Dummies by : John Paul Mueller
Download or read book Python for Data Science For Dummies written by John Paul Mueller and published by John Wiley & Sons. This book was released on 2019-01-25 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fast and easy way to learn Python programming and statistics Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library. Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud. Get started with data science and Python Visualize information Wrangle data Learn from data The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.
Book Synopsis SQL for Data Scientists by : Renee M. P. Teate
Download or read book SQL for Data Scientists written by Renee M. P. Teate and published by John Wiley & Sons. This book was released on 2021-08-17 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Jump-start your career as a data scientist—learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that’s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data. This guide for data scientists differs from other instructional guides on the subject. It doesn’t cover SQL broadly. Instead, you’ll learn the subset of SQL skills that data analysts and data scientists use frequently. You’ll also gain practical advice and direction on "how to think about constructing your dataset." Gain an understanding of relational database structure, query design, and SQL syntax Develop queries to construct datasets for use in applications like interactive reports and machine learning algorithms Review strategies and approaches so you can design analytical datasets Practice your techniques with the provided database and SQL code In this book, author Renee Teate shares knowledge gained during a 15-year career working with data, in roles ranging from database developer to data analyst to data scientist. She guides you through SQL code and dataset design concepts from an industry practitioner’s perspective, moving your data scientist career forward!
Book Synopsis DevOps for Data Science by : Alex Gold
Download or read book DevOps for Data Science written by Alex Gold and published by CRC Press. This book was released on 2024-06-19 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Scientists are experts at analyzing, modelling and visualizing data but, at one point or another, have all encountered difficulties in collaborating with or delivering their work to the people and systems that matter. Born out of the agile software movement, DevOps is a set of practices, principles and tools that help software engineers reliably deploy work to production. This book takes the lessons of DevOps and aplies them to creating and delivering production-grade data science projects in Python and R. This book’s first section explores how to build data science projects that deploy to production with no frills or fuss. Its second section covers the rudiments of administering a server, including Linux, application, and network administration before concluding with a demystification of the concerns of enterprise IT/Administration in its final section, making it possible for data scientists to communicate and collaborate with their organization’s security, networking, and administration teams. Key Features: • Start-to-finish labs take readers through creating projects that meet DevOps best practices and creating a server-based environment to work on and deploy them. • Provides an appendix of cheatsheets so that readers will never be without the reference they need to remember a Git, Docker, or Command Line command. • Distills what a data scientist needs to know about Docker, APIs, CI/CD, Linux, DNS, SSL, HTTP, Auth, and more. • Written specifically to address the concern of a data scientist who wants to take their Python or R work to production. There are countless books on creating data science work that is correct. This book, on the otherhand, aims to go beyond this, targeted at data scientists who want their work to be than merely accurate and deliver work that matters.
Book Synopsis SPSS Statistics for Data Analysis and Visualization by : Jesus Salcedo
Download or read book SPSS Statistics for Data Analysis and Visualization written by Jesus Salcedo and published by John Wiley & Sons. This book was released on 2017-04-20 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive deeper into SPSS Statistics for more efficient, accurate, and sophisticated data analysis and visualization SPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique, and then walk you through the execution with a pragmatic, nuts and bolts example. Coverage includes extensive, in-depth discussion of advanced statistical techniques, data visualization, predictive analytics, and SPSS programming, including automation and integration with other languages like R and Python. You'll learn the best methods to power through an analysis, with more efficient, elegant, and accurate code. IBM SPSS Statistics is complex: true mastery requires a deep understanding of statistical theory, the user interface, and programming. Most users don't encounter all of the methods SPSS offers, leaving many little-known modules undiscovered. This book walks you through tools you may have never noticed, and shows you how they can be used to streamline your workflow and enable you to produce more accurate results. Conduct a more efficient and accurate analysis Display complex relationships and create better visualizations Model complex interactions and master predictive analytics Integrate R and Python with SPSS Statistics for more efficient, more powerful code These "hidden tools" can help you produce charts that simply wouldn't be possible any other way, and the support for other programming languages gives you better options for solving complex problems. If you're ready to take advantage of everything this powerful software package has to offer, SPSS Statistics for Data Analysis and Visualization is the expert-led training you need.
Book Synopsis Google Cloud Platform for Data Engineering by : Alasdair Gilchrist
Download or read book Google Cloud Platform for Data Engineering written by Alasdair Gilchrist and published by Alasdair Gilchrist. This book was released on with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Google Cloud Platform for Data Engineering is designed to take the beginner through a journey to become a competent and certified GCP data engineer. The book, therefore, is split into three parts; the first part covers fundamental concepts of data engineering and data analysis from a platform and technology-neutral perspective. Reading part 1 will bring a beginner up to speed with the generic concepts, terms and technologies we use in data engineering. The second part, which is a high-level but comprehensive introduction to all the concepts, components, tools and services available to us within the Google Cloud Platform. Completing this section will provide the beginner to GCP and data engineering with a solid foundation on the architecture and capabilities of the GCP. Part 3, however, is where we delve into the moderate to advanced techniques that data engineers need to know and be able to carry out. By this time the raw beginner you started the journey at the beginning of part 1 will be a knowledgable albeit inexperienced data engineer. However, by the conclusion of part 3, they will have gained the advanced knowledge of data engineering techniques and practices on the GCP to pass not only the certification exam but also most interviews and practical tests with confidence. In short part 3, will provide the prospective data engineer with detailed knowledge on setting up and configuring DataProc - GCPs version of the Spark/Hadoop ecosystem for big data. They will also learn how to build and test streaming and batch data pipelines using pub/sub/ dataFlow and BigQuery. Furthermore, they will learn how to integrate all the ML and AI Platform components and APIs. They will be accomplished in connecting data analysis and visualisation tools such as Datalab, DataStudio and AI notebooks amongst others. They will also by now know how to build and train a TensorFlow DNN using APIs and Keras and optimise it to run large public data sets. Also, they will know how to provision and use Kubeflow and Kube Pipelines within Google Kubernetes engines to run container workloads as well as how to take advantage of serverless technologies such as Cloud Run and Cloud Functions to build transparent and seamless data processing platforms. The best part of the book though is its compartmental design which means that anyone from a beginner to an intermediate can join the book at whatever point they feel comfortable.
Book Synopsis Semantic Modeling for Data by : Panos Alexopoulos
Download or read book Semantic Modeling for Data written by Panos Alexopoulos and published by O'Reilly Media. This book was released on 2020-08-19 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: What value does semantic data modeling offer? As an information architect or data science professional, let’s say you have an abundance of the right data and the technology to extract business gold—but you still fail. The reason? Bad data semantics. In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You’ll learn how to master this craft to increase the usability and value of your data and applications. You’ll also explore the pitfalls to avoid and dilemmas to overcome for building high-quality and valuable semantic representations of data. Understand the fundamental concepts, phenomena, and processes related to semantic data modeling Examine the quirks and challenges of semantic data modeling and learn how to effectively leverage the available frameworks and tools Avoid mistakes and bad practices that can undermine your efforts to create good data models Learn about model development dilemmas, including representation, expressiveness and content, development, and governance Organize and execute semantic data initiatives in your organization, tackling technical, strategic, and organizational challenges
Book Synopsis Software Engineering for Data Scientists by : Catherine Nelson
Download or read book Software Engineering for Data Scientists written by Catherine Nelson and published by "O'Reilly Media, Inc.". This book was released on 2024-04-16 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering,and clearly explains how to apply the best practices from software engineering to data science. Examples are provided in Python, drawn from popular packages such as NumPy and pandas. If you want to write better data science code, this guide covers the essential topics that are often missing from introductory data science or coding classes, including how to: Understand data structures and object-oriented programming Clearly and skillfully document your code Package and share your code Integrate data science code with a larger code base Learn how to write APIs Create secure code Apply best practices to common tasks such as testing, error handling, and logging Work more effectively with software engineers Write more efficient, maintainable, and robust code in Python Put your data science projects into production And more
Book Synopsis Emotional Leadership for Data-driven Leaders by : Karishma Khoja
Download or read book Emotional Leadership for Data-driven Leaders written by Karishma Khoja and published by Blue Rose Publishers. This book was released on 2023-12-10 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: In “Audacity to Achieve”; my previous book, we explored techniques to stretch beyond our comfort zones and accomplish our genuine desires. On our journey towards accomplishing our life and business objectives, we collaborate and engage in teamwork with various individuals. This book will teach us the skill of guiding and developing alongside others. This book contains real-world case studies and instances from my ongoing involvement in the corporate world. It is an outcome of comprehensive research on merging emotions and data to arrive at decisions that support the growth of both your business and employees.
Book Synopsis NATURAL LANGUAGE PROCESSING (NLP) FOR DATA ANALYSIS by : Dr. Vijaya Krishna Sonthi
Download or read book NATURAL LANGUAGE PROCESSING (NLP) FOR DATA ANALYSIS written by Dr. Vijaya Krishna Sonthi and published by Xoffencerpublication. This book was released on 2024-04-18 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical guide to processing and generating natural language text in the real world, Natural Language Processing in Action is a book that focuses on natural language processing. Within the pages of this book, you will find all of the tools and methods that you require in order to construct the backend natural language processing systems that are necessary to support a virtual assistant (chatbot), spam filter, forum moderator, sentiment analyzer, knowledge base builder, natural language text miner, or virtually any other natural language processing application that you can think. The Natural Language Processing in Action course is designed for Python developers who are intermediate to advance in their skills. In addition, readers who are already capable of designing and constructing complicated systems will find the majority of this book to be valuable. This is because it offers a multitude of examples of best practices and provides insight into the possibilities of the most advanced natural language processing algorithms. In spite of the fact that having knowledge of objectoriented Python development could make it easier for you to construct better systems, making use of what you learn in this book is not needed. A suitable amount of background material and citations of resources (both textual and online) are provided for those individuals who are interested in acquiring a more indepth comprehension of specific topics. Natural languages are different from computer programming languages. They are intended to be translated into a finite set of mathematical operations, like programming languages are. Natural languages are what humans use to share information with each other. We don’t use programming languages to tell each other about our day or to give directions to the It is important to note that natural languages are distinct from computer programming languages. As is the case with programming languages, they are designed to be capable of being converted into a limited collection of mathematical operations. It is via the use of natural languages that humans communicate with one another and share information. When it comes to communicating with one another about our day or providing directions to the grocery store, we do not employ computer languages retail outlet. Using a programming language, a computer program can communicate to a machine the specific instructions it needs to carry out. However, natural languages such as English and French do not have any compilers or interpreters designed specifically for them.
Book Synopsis M Is for (Data) Monkey by : Ken Puls
Download or read book M Is for (Data) Monkey written by Ken Puls and published by Tickling Keys, Inc.. This book was released on 2015-06-01 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Power Query is one component of the Power BI (Business Intelligence) product from Microsoft, and "M" is the name of the programming language created by it. As more business intelligence pros begin using Power Pivot, they find that they do not have the Excel skills to clean the data in Excel; Power Query solves this problem. This book shows how to use the Power Query tool to get difficult data sets into both Excel and Power Pivot, and is solely devoted to Power Query dashboarding and reporting.
Book Synopsis Practical Linear Algebra for Data Science by : Mike X Cohen
Download or read book Practical Linear Algebra for Data Science written by Mike X Cohen and published by "O'Reilly Media, Inc.". This book was released on 2022-09-06 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications. This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms. Ideal for practitioners and students using computer technology and algorithms, this book introduces you to: The interpretations and applications of vectors and matrices Matrix arithmetic (various multiplications and transformations) Independence, rank, and inverses Important decompositions used in applied linear algebra (including LU and QR) Eigendecomposition and singular value decomposition Applications including least-squares model fitting and principal components analysis
Book Synopsis Docker for Data Science by : Joshua Cook
Download or read book Docker for Data Science written by Joshua Cook and published by Apress. This book was released on 2017-08-23 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn Docker "infrastructure as code" technology to define a system for performing standard but non-trivial data tasks on medium- to large-scale data sets, using Jupyter as the master controller. It is not uncommon for a real-world data set to fail to be easily managed. The set may not fit well into access memory or may require prohibitively long processing. These are significant challenges to skilled software engineers and they can render the standard Jupyter system unusable. As a solution to this problem, Docker for Data Science proposes using Docker. You will learn how to use existing pre-compiled public images created by the major open-source technologies—Python, Jupyter, Postgres—as well as using the Dockerfile to extend these images to suit your specific purposes. The Docker-Compose technology is examined and you will learn how it can be used to build a linked system with Python churning data behind the scenes and Jupyter managing these background tasks. Best practices in using existing images are explored as well as developing your own images to deploy state-of-the-art machine learning and optimization algorithms. What You'll Learn Master interactive development using the Jupyter platform Run and build Docker containers from scratch and from publicly available open-source images Write infrastructure as code using the docker-compose tool and its docker-compose.yml file type Deploy a multi-service data science application across a cloud-based system Who This Book Is For Data scientists, machine learning engineers, artificial intelligence researchers, Kagglers, and software developers
Book Synopsis IBM Cloud Pak for Data by : Hemanth Manda
Download or read book IBM Cloud Pak for Data written by Hemanth Manda and published by Packt Publishing Ltd. This book was released on 2021-11-24 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build end-to-end AI solutions with IBM Cloud Pak for Data to operationalize AI on a secure platform based on cloud-native reliability, cost-effective multitenancy, and efficient resource management Key FeaturesExplore data virtualization by accessing data in real time without moving itUnify the data and AI experience with the integrated end-to-end platformExplore the AI life cycle and learn to build, experiment, and operationalize trusted AI at scaleBook Description Cloud Pak for Data is IBM's modern data and AI platform that includes strategic offerings from its data and AI portfolio delivered in a cloud-native fashion with the flexibility of deployment on any cloud. The platform offers a unique approach to addressing modern challenges with an integrated mix of proprietary, open-source, and third-party services. You'll begin by getting to grips with key concepts in modern data management and artificial intelligence (AI), reviewing real-life use cases, and developing an appreciation of the AI Ladder principle. Once you've gotten to grips with the basics, you will explore how Cloud Pak for Data helps in the elegant implementation of the AI Ladder practice to collect, organize, analyze, and infuse data and trustworthy AI across your business. As you advance, you'll discover the capabilities of the platform and extension services, including how they are packaged and priced. With the help of examples present throughout the book, you will gain a deep understanding of the platform, from its rich capabilities and technical architecture to its ecosystem and key go-to-market aspects. By the end of this IBM book, you'll be able to apply IBM Cloud Pak for Data's prescriptive practices and leverage its capabilities to build a trusted data foundation and accelerate AI adoption in your enterprise. What you will learnUnderstand the importance of digital transformations and the role of data and AI platformsGet to grips with data architecture and its relevance in driving AI adoption using IBM's AI LadderUnderstand Cloud Pak for Data, its value proposition, capabilities, and unique differentiatorsDelve into the pricing, packaging, key use cases, and competitors of Cloud Pak for DataUse the Cloud Pak for Data ecosystem with premium IBM and third-party servicesDiscover IBM's vibrant ecosystem of proprietary, open-source, and third-party offerings from over 35 ISVsWho this book is for This book is for data scientists, data stewards, developers, and data-focused business executives interested in learning about IBM's Cloud Pak for Data. Knowledge of technical concepts related to data science and familiarity with data analytics and AI initiatives at various levels of maturity are required to make the most of this book.
Book Synopsis JavaScript and jQuery for Data Analysis and Visualization by : Jon Raasch
Download or read book JavaScript and jQuery for Data Analysis and Visualization written by Jon Raasch and published by John Wiley & Sons. This book was released on 2014-11-14 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Go beyond design concepts—build dynamic data visualizations using JavaScript JavaScript and jQuery for Data Analysis and Visualization goes beyond design concepts to show readers how to build dynamic, best-of-breed visualizations using JavaScript—the most popular language for web programming. The authors show data analysts, developers, and web designers how they can put the power and flexibility of modern JavaScript libraries to work to analyze data and then present it using best-of-breed visualizations. They also demonstrate the use of each technique with real-world use cases, showing how to apply the appropriate JavaScript and jQuery libraries to achieve the desired visualization. All of the key techniques and tools are explained in this full-color, step-by-step guide. The companion website includes all sample codes used to generate the visualizations in the book, data sets, and links to the libraries and other resources covered. Go beyond basic design concepts and get a firm grasp of visualization approaches and techniques using JavaScript and jQuery Discover detailed, step-by-step directions for building specific types of data visualizations in this full-color guide Learn more about the core JavaScript and jQuery libraries that enable analysis and visualization Find compelling stories in complex data, and create amazing visualizations cost-effectively Let JavaScript and jQuery for Data Analysis and Visualization be the resource that guides you through the myriad strategies and solutions for combining analysis and visualization with stunning results.
Book Synopsis Data Observability for Data Engineering by : Michele Pinto
Download or read book Data Observability for Data Engineering written by Michele Pinto and published by Packt Publishing Ltd. This book was released on 2023-12-29 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover actionable steps to maintain healthy data pipelines to promote data observability within your teams with this essential guide to elevating data engineering practices Key Features Learn how to monitor your data pipelines in a scalable way Apply real-life use cases and projects to gain hands-on experience in implementing data observability Instil trust in your pipelines among data producers and consumers alike Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn the age of information, strategic management of data is critical to organizational success. The constant challenge lies in maintaining data accuracy and preventing data pipelines from breaking. Data Observability for Data Engineering is your definitive guide to implementing data observability successfully in your organization. This book unveils the power of data observability, a fusion of techniques and methods that allow you to monitor and validate the health of your data. You’ll see how it builds on data quality monitoring and understand its significance from the data engineering perspective. Once you're familiar with the techniques and elements of data observability, you'll get hands-on with a practical Python project to reinforce what you've learned. Toward the end of the book, you’ll apply your expertise to explore diverse use cases and experiment with projects to seamlessly implement data observability in your organization. Equipped with the mastery of data observability intricacies, you’ll be able to make your organization future-ready and resilient and never worry about the quality of your data pipelines again.What you will learn Implement a data observability approach to enhance the quality of data pipelines Collect and analyze key metrics through coding examples Apply monkey patching in a Python module Manage the costs and risks associated with your data pipeline Understand the main techniques for collecting observability metrics Implement monitoring techniques for analytics pipelines in production Build and maintain a statistics engine continuously Who this book is for This book is for data engineers, data architects, data analysts, and data scientists who have encountered issues with broken data pipelines or dashboards. Organizations seeking to adopt data observability practices and managers responsible for data quality and processes will find this book especially useful to increase the confidence of data consumers and raise awareness among producers regarding their data pipelines.
