Datafangst og dataanalyse

Murach's R for Data Analysis as a guide, you can learn the R skills you need to become a data analyst, and you can learn them faster and better than ever before.

The purpose of this report is to assist Department of Defense decisionmakers called upon to respond to adverse economic shocks by identifying opportunities for improving analysis of shocks and their effects on U.S. defense postures.

In this report, RAND researchers present practical knowledge to inform law enforcement agencies about available broadband options and opportunities, governance issues, funding options, costs, and barriers to implementation.

With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale.

"A comprehensive introduction to the Julia programming language. Provides a step-by-step foundation on the language's basics before diving into how to write effective Julia programs that solve real-world problems in various scientific fields. Readers will use Julia to explore and visualize data, solve equations, write simulations, and create libraries. Contains hundreds of working code examples and illustrations"--

Build efficient data lakes that can scale to virtually unlimited size using AWS GlueKey Features:Learn to work with AWS Glue to overcome typical implementation challenges in data lakesCreate and manage serverless ETL pipelines that can scale to manage big dataWritten by AWS Glue community members, this practical guide shows you how to implement AWS Glue in no timeBook Description:Organizations these days have gravitated toward services such as AWS Glue that undertake undifferentiated heavy lifting and provide serverless Spark, enabling you to create and manage data lakes in a serverless fashion. This guide shows you how AWS Glue can be used to solve real-world problems along with helping you learn about data processing, data integration, and building data lakes.Beginning with AWS Glue basics, this book teaches you how to perform various aspects of data analysis such as ad hoc queries, data visualization, and real-time analysis using this service. It also provides a walk-through of CI/CD for AWS Glue and how to shift left on quality using automated regression tests. You'll find out how data security aspects such as access control, encryption, auditing, and networking are implemented, as well as getting to grips with useful techniques such as picking the right file format, compression, partitioning, and bucketing. As you advance, you'll discover AWS Glue features such as crawlers, Lake Formation, governed tables, lineage, DataBrew, Glue Studio, and custom connectors. The concluding chapters help you to understand various performance tuning, troubleshooting, and monitoring options.By the end of this AWS book, you'll be able to create, manage, troubleshoot, and deploy ETL pipelines using AWS Glue.What You Will Learn:Apply various AWS Glue features to manage and create data lakesUse Glue DataBrew and Glue Studio for data preparationOptimize data layout in cloud storage to accelerate analytics workloadsManage metadata including database, table, and schema definitionsSecure your data during access control, encryption, auditing, and networkingMonitor AWS Glue jobs to detect delays and loss of dataIntegrate Spark ML and SageMaker with AWS Glue to create machine learning modelsWho this book is for:This book is for ETL developers, data engineers, and data analysts who want to understand how AWS Glue can help you solve your business problems. Basic knowledge of AWS data services is assumed.

This is a prestige reference work offering a comprehensive overview of the state of current knowledge and emerging scholarship in the discipline of AIS. It has been updated to capture the changes in technology since the previous edition, including chapters on artificial intelligence, predictive analytics and data visualisation.

The book provides a snapshot of practices used by contemporary designers, researchers, and artists who create objects, spaces, and experiences imbued with data. They draw from a range of domains and traditions, and represent a fascinating, inspiring, and revealing cross-section of contemporary maker and data culture.

Numerical simulation models are used in all engineering disciplines for modeling physical phenomena to learn how the phenomena work, and to identify problems and optimize behavior. Smart Proxy Models provide an opportunity to replicate numerical simulations with very high accuracy and can be run on a laptop within a few minutes, thereby simplifying the use of complex numerical simulations, which can otherwise take tens of hours. This book focuses on Smart Proxy Modeling and provides readers with all the essential details on how to develop Smart Proxy Models using Artificial Intelligence and Machine Learning, as well as how it may be used in real-world cases.Covers replication of highly accurate numerical simulations using Artificial Intelligence and Machine LearningDetails application in reservoir simulation and modeling and computational fluid dynamicsIncludes real case studies based on commercially available simulatorsSmart Proxy Modeling is ideal for petroleum, chemical, environmental, and mechanical engineers, as well as statisticians and others working with applications of data-driven analytics.

As mobile devices continue becoming a larger part of our lives, the development of location acquisition technologies to track moving objects have focused the minds of researchers on issues ranging from longitude and latitude coordinates, speed, direction, and timestamping, as part of parameters needed to calculate the positional information and locations of objects, in terms of time and position in the form of trajectory streams. Recently, recent advances have facilitated various urban applications such as smart transportation and mobile delivery services.Unlike other books on spatial databases, mobile computing, data mining, or computing with spatial trajectories, this book is focused on smart transportation applications.This book is a good reference for advanced undergraduates, graduate students, researchers, and system developers working on transportation systems.

Former Wall Street Journal technology reporter Yukari Iwatani Kane delves deep inside Apple in the two years since Steve Jobs's death, revealing the tensions and challenges CEO Tim Cook and his team face as they try to sustain Jobs's vision and keep the company moving forward.Steve Jobs's death raised one of the most pressing questions in the tech and business worlds: Could Apple stay great without its iconic leader? Many inside the company were eager to prove that Apple could be just as innovative as it had been under Jobs. Others were painfully aware of the immense challenge ahead. As its business has become more complex and global, Apple has been subject to intense scrutiny, much of it critical. Maintaining market leadership is crucial as it tries to conquer new frontiers and satisfy the public's insatiable appetite for "insanely great" products.Based on more than two hundred interviews with current and former executives, business partners, Apple watchers, and others, Haunted Empire is an illuminating portrait of Apple today that offers clues to its future. With nuanced insights and colorful details that only a seasoned journalist could glean, Kane goes beyond the myths and headlines. She explores Tim Cook's leadership and its impact on Jobs's loyal lieutenants, new product development, and Apple's relationships with Wall Street, the government, tech rivals, suppliers, the media, and consumers. Hard-hitting yet fair, Haunted Empire reveals the perils and opportunities an iconic company faces when it loses its visionary leader.

Computational Intelligence Applications for Text and Sentiment Data Analysis explores the most recent advances in text information processing and data analysis technologies, specifically focusing on sentiment analysis from multifaceted data. The book investigates a wide range of challenges involved in the accurate analysis of online sentiments, including how to i) identify subjective information from text, i.e., exclusion of 'neutral' or 'factual' comments that do not carry sentiment information, ii) identify sentiment polarity, and iii) domain dependency. Spam and fake news detection, short abbreviation, sarcasm, word negation, and a lot of word ambiguity are also explored. Further chapters look at the difficult process of extracting sentiment from different multimodal information (audio, video and text), semantic concepts. In each chapter, the book's authors explore how computational intelligence (CI) techniques, such as deep learning, convolutional neural network, fuzzy and rough set, global optimizers, and hybrid machine learning techniques play an important role in solving the inherent problems of sentiment analysis applications.

Over the past decade, the National Geospatial-Intelligence Agency (NGA) has evolved its programming organization multiple times, along with the process it uses for managing its resource investments. Each of these iterations was done to address challenges and inefficiencies. NGA is now considering additional steps to improve its process and is seeking to improve its practices through internal improvements, such as gaining an understanding of how previous changes affected the overall effectiveness of its resource management process, and what can be learned from other organizations. NGA is now entering a fourth period of acquisition restructuring that is intended to improve on how the planning and programming phases are managed. NGA asked the RAND Corporation to review the programming phase of the Intelligence Planning, Programming, Budgeting, and Evaluation (IPPBE) process. The authors looked at three organizational eras (pre-2013, 2013-2018, and 2018 to the present) to determine the conditions, causes, and effects of performance and effectiveness generally and of previous changes to this phase of NGA IPPBE for each era. NGA is not alone in its ongoing effort to modernize its IPPBE structure to improve efficiency and effectiveness. Although NGA has conducted several previous internal studies to identify areas for IPPBE process improvement, this research is the first to synthesize findings between external literature and findings gleaned from structured subject-matter expert interviews to highlight crucial program-process issues for NGA leadership to absorb and address in any future IPPBE restructuring phase.

Use your programming skills to create and optimize high-frequency trading systems in no time with Java, C++, and PythonKey Features- Learn how to build high-frequency trading systems with ultra-low latency- Understand the critical components of a trading system- Optimize your systems with high-level programming techniquesBook DescriptionThe world of trading markets is complex, but it can be made easier with technology. Sure, you know how to code, but where do you start? What programming language do you use? How do you solve the problem of latency? This book answers all these questions. It will help you navigate the world of algorithmic trading and show you how to build a high-frequency trading (HFT) system from complex technological components, supported by accurate data.Starting off with an introduction to HFT, exchanges, and the critical components of a trading system, this book quickly moves on to the nitty-gritty of optimizing hardware and your operating system for low-latency trading, such as bypassing the kernel, memory allocation, and the danger of context switching. Monitoring your system's performance is vital, so you'll also focus on logging and statistics. As you move beyond the traditional HFT programming languages, such as C++ and Java, you'll learn how to use Python to achieve high levels of performance. And what book on trading is complete without diving into cryptocurrency? This guide delivers on that front as well, teaching how to perform high-frequency crypto trading with confidence.By the end of this trading book, you'll be ready to take on the markets with HFT systems.What you will learnWho this book is forThis book is for software engineers, quantitative developers or researchers, and DevOps engineers who want to understand the technical side of high-frequency trading systems and the optimizations that are needed to achieve ultra-low latency systems. Prior experience working with C++ and Java will help you grasp the topics covered in this book more easily.Table of Contents- Fundamentals of a High-Frequency Trading System- The Critical Components of a Trading System- Understanding the Trading Exchange Dynamics- HFT System Foundations - From Hardware to OS- Networking in Motion- HFT Optimization - Architecture and Operating System- HFT Optimization - Logging, Performance, and Networking- C++ - The Quest for Microsecond Latency- Java and JVM for Low-Latency Systems- Python - Interpreted but Open to High Performance- High Frequency FPGA and Crypto

Racially or ethnically motivated violent extremism (REMVE) and extremists (REMVEs) present some of the most pressing threats to the United States. REMVE also has been identified as the White identity terrorist movement (WITM). REMVEs are among the most lethal domestic violent extremists, and they are the most likely to commit mass-casualty attacks. These movements are characterized by a broad ideological orientation toward xenophobic, anti-Semitic, racist, and misogynistic sentiment. For this report, the authors reviewed the relevant literature on REMVE networks and collected and analyzed social media data from six social networks (Twitter, Reddit, Gab, Ruqqus, Telegram, and Stormfront) to produce a global network map of the digital REMVE space. That network map evaluates each network's construction, connectivity, geographic location, references to prominent organizations, and proclivity to violence. The authors also reviewed ten countries' experiences with REMVE to sketch out an understanding of the REMVE space in these countries and how REMVEs in those countries relate to those in the United States.

Why laws focused on data cannot effectively protect people-and how an approach centered on human rights offers the best hope for preserving human dignity and autonomy in a cyberphysical world.Ever-pervasive technology poses a clear and present danger to human dignity and autonomy, as many have pointed out. And yet, for the past fifty years, we have been so busy protecting data that we have failed to protect people. In Beyond Data, Elizabeth Renieris argues that laws focused on data protection, data privacy, data security and data ownership have unintentionally failed to protect core human values, including privacy. And, as our collective obsession with data has grown, we have, to our peril, lost sight of what's truly at stake in relation to technological development-our dignity and autonomy as people. Far from being inevitable, our fixation on data has been codified through decades of flawed policy. Renieris provides a comprehensive history of how both laws and corporate policies enacted in the name of data privacy have been fundamentally incapable of protecting humans. Her research identifies the inherent deficiency of making data a rallying point in itself-data is not an objective truth, and what's more, its "entirely contextual and dynamic" status makes it an unstable foundation for organizing. In proposing a human rights-based framework that would center human dignity and autonomy rather than technological abstractions, Renieris delivers a clear-eyed and radically imaginative vision of the future. At once a thorough application of legal theory to technology and a rousing call to action, Beyond Data boldly reaffirms the value of human dignity and autonomy amid widespread disregard by private enterprise at the dawn of the metaverse.