Dataanalyse: generelt

DELTA 2.0 er en ny og helt opdateret udgave af Delta, der i ti år har været brugt i matematiklærernes grund-, efter- og videreuddannelse.DELTA 2.0 er del af serien MATEMATIK FOR LÆRERSTUDERENDE.Ud over DELTA 2.0 består serien af fem bøger med fokus på matematisk indhold: to om Tal, algebra og funktioner, to om Geometri og en om Stokastik, hvortil kommer en bog om elever med særlige behov i matematikundervisningen. Bøgerne i serien er et ambitiøst forsøg på at sammentænke de matematikfaglige aspekter af læreruddannelsen med fagdidaktiske og professionsrettede overvejelser.DELTA 2.0 er seriens almene fagdidaktik. Der er også fagdidaktiske overvejelser i de øvrige bøger i serien, men de er knyttet til specifikt matematisk indhold. DELTA 2.0 behandler mere generelle matematikdidaktiske problemstillinger som læringsteoretiske overvejelser i forbindelse med matematik, centrale aspekter af det at undervise i matematik og digitale teknologier som værktøj til at støtte elevers faglige læring af matematik. Det er en gennemgående idé i DELTA 2.0 at formulere matematikdidaktiske fokuspunkter, som i særlig grad er vigtige for lærere at reflektere over.DELTA 2.0 henvender sig ikke blot til lærerstuderende, men kan også anvendes i matematiklærernes efter- og videreuddannelse, fx diplom-, kandidat- eller masteruddannelser. Desuden vil matematiklærere kunne finde inspiration til udvikling af egenundervisning.

Kvalitative analyseprocesser giver konkret indblik i, hvordan du kan analysere empirisk materiale. Bogen starter der, hvor mange metodebøger stopper: Når du har dit empiriske materiale i hus, hvad gør du så? Analysearbejdet er helt centralt for kvalitativ forskning – det er gennem analyserne, at resultaterne fremkommer. Alligevel er det ofte ret dunkelt, hvordan analysen egentlig er foretaget. Denne bog sætter spot på selve analyseprocessen, de analytiske greb, som er anvendt, og hvordan teoretisk begrebsarbejde har guidet analysen undervejs. På den måde inviterer bogen læseren ind i forskerens værksted og retter fokus på selve analyseprocessen med dens kringlede proces, trin og vildveje, tilrettelæggelse og strategier. Bogen er inddelt i hermeneutiske, poststrukturalistiske og dialektiske analyseperspektiver. Det er imidlertid en væsentlig pointe, at det teoretiske afsæt ikke på nogen entydig måde giver én bestemt analysemetode. Derfor præsenterer bogen forskellige analysetilgange under hver af de tre overskrifter og viser, hvordan man på forskellige måder kan arbejde sig igennem den analytiske proces. Forfatterne tilbyder desuden anvisninger i form af modeller, analytikker og spørgsmål, som man kan lade sig inspirere af, når man skal planlægge og gennemføre sine egne analyser. Kvalitative analyseprocesser henvender sig primært til studerende, der skal analysere kvalitativt empirisk materiale som led i en lang eller mellemlang videregående uddannelse eller et ph.d.-forløb. Den er især anvendelig indenfor det pædagogisk-psykologiske felt og beslægtede områder. De beskrevne analyseprocesser har dog samme relevans udover disse felter, og bogen kan derfor anvendes af alle, som ønsker at blive klædt bedre på til at gennemføre teoretisk informerede kvalitative analyser.

MULTIMODAL INTERAKTIONSANALYSE giver mulighed for at undersøge sociale interaktioner i detaljen og dermed fremanalysere, hvordan sociale relationer, identitet og mening skabes og tillægges mening det være sig i mødelokalet, i butikken, i klasseværelset, i det offentlige rum eller om middagsbordet.Metoden er kendetegnet ved at fokusere ikke kun på det talte ord, men på alle de forskelige modaliteter, der medvirker til at skabe mening i social interaktion, såsom tale, gestikulation, blikretning, mimik og genstande. For at kunne observere og analysere dette er det nødvendigt at indsamle data gennem videoetnografisk metode.Denne bog giver en samlet forskningsbaseret introduktion til de væsentligste aspekter i et projektforløb, der indebærer videoetnografisk dataindsamling og multimodal interaktionsanalyse. Bogen gennemgår således:Hvad multimodal interaktionsanalyse kan bruges til Tilgangens afsæt i den såkaldte EMCA-tradition. Tilrettelæggelse og gennemførelse af videoetnografisk dataindsamling Bearbejdning af det indsamlede videomateriale Udarbejdelse af multimodale interaktionsanalyser Videobaseret feedback til deltagere i projektet.

Generative modeling is one of the hottest topics in AI. Its now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models.Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, youll understand how to make your models learn more efficiently and become more creative.Discover how variational autoencoders can change facial expressions in photosBuild practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generationCreate recurrent generative models for text generation and learn how to improve the models using attentionUnderstand how generative models can help agents to accomplish tasks within a reinforcement learning settingExplore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

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.

Make any team or business data driven with this practical guide to overcoming common challenges and creating a data culture. Businesses are increasingly focusing on their data and analytics strategy, but a data-driven culture grounded in evidence-based decision making can be difficult to achieve. Be Data Driven outlines a step-by-step roadmap to building a data-driven organization or team, beginning with deciding on outcomes and a strategy before moving onto investing in technology and upskilling where necessary. This practical guide explains what it means to be a data-driven organization and explores which technologies are advancing data and analytics. Crucially, it also examines the most common challenges to becoming data driven, from a foundational skills gap to issues with leadership and strategy and the impact of organizational culture. With case studies of businesses who have successfully used data, Be Data Driven shows managers, leaders and data professionals how to address hurdles, encourage a data culture and become truly data driven.

Make informed decisions using the numbers around you to work smarter and live better.How often have you heard it said, or even said yourself, 'I'm not a numbers person?' Well, Dr Selena Fisk believes we no longer have a choice. Data iseverywhere. Smart watches track our steps and heart rate, social media platforms recommend people we might know and products we might like, and map applications suggest when we should leave home depending on the traffic.When you get off the phone to a customer service representative, you are asked to take a survey. Why? Because the data from the survey drives business decisions. Numbers are all around us and can help us make better decisions.The good news is that anyone can become a numbers person. I'm Not a Numbers Person shows you how to collect data in your working life, how to interpret it, present it visually and understand the story that it tells. These stories will be powerful for decision-making and for driving growth and productivity in your organisation.Whether you're a solopreneur, a small business owner, an emerging leader, or in an executive leadership role, this book is a must-have guide to understanding data and making better decisions in the 21st century.

Explore IoT, data analytics, and machine learning to solve cyber-physical problems using the latest capabilities of managed services such as AWS IoT Greengrass and Amazon SageMakerKey Features:Accelerate your next edge-focused product development with the power of AWS IoT GreengrassDevelop proficiency in architecting resilient solutions for the edge with proven best practicesHarness the power of analytics and machine learning for solving cyber-physical problemsBook Description:The Internet of Things (IoT) has transformed how people think about and interact with the world. The ubiquitous deployment of sensors around us makes it possible to study the world at any level of accuracy and enable data-driven decision-making anywhere. Data analytics and machine learning (ML) powered by elastic cloud computing have accelerated our ability to understand and analyze the huge amount of data generated by IoT. Now, edge computing has brought information technologies closer to the data source to lower latency and reduce costs.This book will teach you how to combine the technologies of edge computing, data analytics, and ML to deliver next-generation cyber-physical outcomes. You'll begin by discovering how to create software applications that run on edge devices with AWS IoT Greengrass. As you advance, you'll learn how to process and stream IoT data from the edge to the cloud and use it to train ML models using Amazon SageMaker. The book also shows you how to train these models and run them at the edge for optimized performance, cost savings, and data compliance.By the end of this IoT book, you'll be able to scope your own IoT workloads, bring the power of ML to the edge, and operate those workloads in a production setting.What You Will Learn:Build an end-to-end IoT solution from the edge to the cloudDesign and deploy multi-faceted intelligent solutions on the edgeProcess data at the edge through analytics and MLPackage and optimize models for the edge using Amazon SageMakerImplement MLOps and DevOps for operating an edge-based solutionOnboard and manage fleets of edge devices at scaleReview edge-based workloads against industry best practicesWho this book is for:This book is for IoT architects and software engineers responsible for delivering analytical and machine learning-backed software solutions to the edge. AWS customers who want to learn and build IoT solutions will find this book useful. Intermediate-level experience with running Python software on Linux is required to make the most of this book.

Examine the working details for real-world Java programs used for system integration with IBM Software, applying various API libraries (as used by Banking and Insurance companies). This book includes the step-by-step procedure to use the IBM FileNet Case Manager 5.3.3 Case Builder solution and the similar IBM System, IBM Business Automation Workflow to create an Audit System. You'll learn how to implement the workflow with a client Java Message Service (JMS) java method developed with Workflow Custom Operations System Step components. Using IBM Cognos Analytics Version 11.2, you'll be able to create new views for IBM Case Manager Analytics for custom time dimensions. The book also explains the SQL code and procedures required to create example Online Analytical Processing (OLAP) cubes with multi-level time dimensions for IBM Case Manager analytics.IBM Software Systems Integration features the most up to date systems software procedures using tested API calls.What You Will LearnReview techniques for generating custom IBM JMS codeCreate a new custom view for a multi-level time dimensionSee how a java program can provide the IBM FileNet document management API calls for content store folder and document replicationConfigure Java components for content engine eventsWho This Book Is ForIT consultants, Systems and Solution Architects.

Dealing with artificial intelligence, this book delineates AI¿s role in model discovery for dynamical systems. With the implementation of topological methods to construct metamodels, it engages with levels of complexity and multi-scale hierarchies hitherto considered off limits for data science.

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 latencyUnderstand the critical components of a trading systemOptimize your systems with high-level programming techniquesBook Description:The 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 Learn:Understand the architecture of high-frequency trading systemsBoost system performance to achieve the lowest possible latencyLeverage the power of Python programming, C++, and Java to build your trading systemsBypass your kernel and optimize your operating systemUse static analysis to improve code developmentUse C++ templates and Java multithreading for ultra-low latencyApply your knowledge to cryptocurrency tradingWho this book is for:This 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.

This volume analyses the rising inclusion of new and emerging technologies in business. It measures the effectiveness and challenges of these tools in various aspects of strategy and decision making within small and big businesses.

An introduction to the Python programming language and its most popular tools for scientists, engineers, students, and anyone who wants to use Python for research, simulations, and collaboration.Doing Science with Python introduces readers to the most popular coding tools for scientific research, such as Anaconda, Spyder, Jupyter Notebooks, and JupyterLab, as well as dozens of important Python libraries for working with data, including NumPy, matplotlib, and pandas. No prior programming experience is required! You'll be guided through setting up a professional coding environment, then get a crash course on programming with Python, and explore the many tools and libraries ideal for working with data, designing visualizations, simulating natural events, and more. In the book's applied projects, you'll use these tools to write programs that perform tasks like counting tree rings, creating an interactive science slideshow, and simulating the foraging patterns of animals.