Dataanalyse: generelt

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

Studienarbeit aus dem Jahr 2018 im Fachbereich BWL - Sonstiges, Note: 1,0, FOM Hochschule für Oekonomie & Management gemeinnützige GmbH, Stuttgart, Veranstaltung: Quantitative Datenanalyse, Sprache: Deutsch, Abstract: Analyse des Anteils von Biolebensmitteln am Gesamtmarkt Deutschland mit der mathematischen Methode Regressionsanalyse. Beschreibung der Formeln und deren Anwendung auf die Daten. Daten sind aus Statista.Die Autorin möchte mit dieser Seminararbeit eine Analyse des deutschen Lebensmittelmarktes über einen Zeitraum von zehn Jahren als Stichprobenanalyse vornehmen. Ziel der Betrachtung ist es, mit der Regressionsanalyse herauszuarbeiten, dass es keine Korrelation zwischen dem Umsatz des Gesamtmarktes und dem Umsatz der biologisch erzeugten Lebensmittel innerhalb des Betrachtungszeitraumes gibt. Mit der Box-Plot-Methode wird nach Sondereffekten gesucht. Hierzu wurden Primärliteratur und statistische, rein quantitative Daten beschafft und analysiert.

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.

Get to grips with solving real-world NLP problems, such as dependency parsing, information extraction, topic modeling, and text data visualizationKey Features:Analyze varying complexities of text using popular Python packages such as NLTK, spaCy, sklearn, and gensimImplement common and not-so-common linguistic processing tasks using Python librariesOvercome the common challenges faced while implementing NLP pipelinesBook Description:Python is the most widely used language for natural language processing (NLP) thanks to its extensive tools and libraries for analyzing text and extracting computer-usable data. This book will take you through a range of techniques for text processing, from basics such as parsing the parts of speech to complex topics such as topic modeling, text classification, and visualization.Starting with an overview of NLP, the book presents recipes for dividing text into sentences, stemming and lemmatization, removing stopwords, and parts of speech tagging to help you to prepare your data. You'll then learn ways of extracting and representing grammatical information, such as dependency parsing and anaphora resolution, discover different ways of representing the semantics using bag-of-words, TF-IDF, word embeddings, and BERT, and develop skills for text classification using keywords, SVMs, LSTMs, and other techniques. As you advance, you'll also see how to extract information from text, implement unsupervised and supervised techniques for topic modeling, and perform topic modeling of short texts, such as tweets. Additionally, the book shows you how to develop chatbots using NLTK and Rasa and visualize text data.By the end of this NLP book, you'll have developed the skills to use a powerful set of tools for text processing.What You Will Learn:Become well-versed with basic and advanced NLP techniques in PythonRepresent grammatical information in text using spaCy, and semantic information using bag-of-words, TF-IDF, and word embeddingsPerform text classification using different methods, including SVMs and LSTMsExplore different techniques for topic modeling such as K-means, LDA, NMF, and BERTWork with visualization techniques such as NER and word clouds for different NLP toolsBuild a basic chatbot using NLTK and RasaExtract information from text using regular expression techniques and statistical and deep learning toolsWho this book is for:This book is for data scientists and professionals who want to learn how to work with text. Intermediate knowledge of Python will help you to make the most out of this book. If you are an NLP practitioner, this book will serve as a code reference when working on your projects.

Getting early indication of potential contractor performance risks and contract execution issues is critical for proactive acquisition management. When contractors are in danger of not meeting contractual performance goals, Department of the Air Force (DAF) acquisition management may not be fully aware of the shortfall until, for example, a schedule deadline is missed, government testing indicates poor system's technical performance, or costs exceed expectations. Concerns continue to be raised about cost and schedule growth in acquisition and experts postulate about a lack of knowledge about the status of acquisition programs. In this report, the authors focus on metrics to identify emerging execution problems earlier than traditional acquisition oversight systems to enable more-proactive risk and performance management. They summarize their findings, which include a taxonomy of contractor relative risks, leading indicators of performance, relevant data sources, risk measures and equations, and a prototype that implements some of these findings using real data sources. This research should be of interest to acquisition professionals and leadership who are searching for ways to improve acquisition performance through early identification of potential relative contractor risks and execution problems to inform active program management and mitigation of risks. The prototype should be of interest to acquisition officials (from program managers to milestone decision authorities) to help them access more data in an easy-to-understand way so they can focus their limited time on areas that require increased management attention. This approach should be useful during any phase of the acquisition process.

From facial recognition-capable of checking us onto flights or identifying undocumented residents-to automated decision systems that inform everything from who gets loans to who receives bail, each of us moves through a world determined by data-empowered algorithms. But these technologies didn't just appear: they are part of a history that goes back centuries, from the birth of eugenics in Victorian Britain to the development of Google search.Expanding on the popular course they created at Columbia University, Chris Wiggins and Matthew Jones illuminate the ways in which data has long been used as a tool and a weapon in arguing for what is true, as well as a means of rearranging or defending power. By understanding the trajectory of data-where it has been and where it might yet go-Wiggins and Jones argue that we can understand how to bend it to ends that we collectively choose, with intentionality and purpose.

Design and implement a results-driven data strategy with this five-stage guide to leveraging existing business assets and creating value through data projects.

This book covers state of the art interdisciplinary research on key disruptive and interrelated technologies such as Big Data, Edge computing, IoT and Cloud computing. The authors address the challenges from a distributed system perspective, with clear contributions in theory and applications.

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.

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.

The New York Times best-selling Freakonomics was a worldwide sensation, selling over four million copies in thirty-five languages and changing the way we look at the world. Now, Steven D. Levitt and Stephen J. Dubner return with SuperFreakonomics, and fans and newcomers alike will find that the freakquel is even bolder, funnier, and more surprising than the first. Four years in the making, SuperFreakonomics asks not only the tough questions, but the unexpected ones: What's more dangerous, driving drunk or walking drunk? Why is chemotherapy prescribed so often if it's so ineffective? Can a sex change boost your salary? SuperFreakonomics challenges the way we think all over again, exploring the hidden side of everything with such questions as: How is a street prostitute like a department-store Santa? Why are doctors so bad at washing their hands? How much good do car seats do? What's the best way to catch a terrorist? Did TV cause a rise in crime? What do hurricanes, heart attacks, and highway deaths have in common? Are people hard-wired for altruism or selfishness? Can eating kangaroo save the planet? Which adds more value: a pimp or a Realtor? Levitt and Dubner mix smart thinking and great storytelling like no one else, whether investigating a solution to global warming or explaining why the price of oral sex has fallen so drastically. By examining how people respond to incentives, they show the world for what it really is ? good, bad, ugly, and, in the final analysis, super freaky. Freakonomics has been imitated many times over ? but only now, with SuperFreakonomics, has it met its match.

In vielen Alltagssituationen spielt der Zufall eine Rolle - in der Stochastik versuchen wir mit mathematischen Mitteln, Strukturen in dieser scheinbaren Regellosigkeit zu erkennen. Dieses Buch liefert die Werkzeuge, um den Gesetzmaigkeiten auf die Spur zu kommen. Dafur wird, ausgehend von der elementaren beschreibenden Statistik, die Wahrscheinlichkeitstheorie bis hin zum Zentralen Grenzwertsatz entwickelt. Bei aller mathematischen Stringenz bleibt das Buch dabei durch seine vielen Beispiele anschaulich. So wird z.B. ein klassisches Anwendungsgebiet der Stochastik, die mathematische Statistik, ausfuhrlich behandelt. Ein weiterer Schwerpunkt liegt in der Einfuhrung in aktuelle stochastische Fragestellungen. Das Themenspektrum reicht von der Informationstheorie, uber die Finanzmathematik, die Theorie der Markovketten bis hin zur stochastischen Optimierung.

The sequencing of the genomes of humans and other organisms is inspiring the developmentofnew statisticalandbioinformatics tools that we hope canmodify the current understanding of human diseases and therapies. As our knowledge about the human genome increases so does our belief that to fully grasp the mechanisms of diseases we need to understand their genetic basis and the p- teomicsbehind them and to integratethe knowledgegeneratedin thelaboratory in clinical settings. The new genetic and proteomic data has brought forth the possibility of developing new targets and therapies based on these ?ndings, of implementing newly developed preventive measures, and also of discovering new research approaches to old problems. To fully enhance our understanding of disease processes, to develop more and better therapies to combat and cure diseases, and to develop strategies to prevent them, there is a need for synergy of the disciplines involved, medicine, molecular biology, biochemistry and computer science, leading to more recent ?elds such as bioinformatics and biomedical informatics. The 6th International Symposium on Biological and Medical Data Analysis aimed to become a place where researchersinvolved in these diversebut incre- ingly complementary areas could meet to present and discuss their scienti?c results. The papers in this volume discuss issues from statistical models to arc- tectures and applications to bioinformatics and biomedicine. They cover both practical experience and novel research ideas and concepts.

Exploratory data analysis (EDA) is about detecting and describing patterns, trends, and relations in data, motivated by certain purposes of investigation. As something relevant is detected in data, new questions arise, causing specific parts to be viewed in more detail. So EDA has a significant appeal: it involves hypothesis generation rather than mere hypothesis testing.The authors describe in detail and systemize approaches, techniques, and methods for exploring spatial and temporal data in particular. They start by developing a general view of data structures and characteristics and then build on top of this a general task typology, distinguishing between elementary and synoptic tasks. This typology is then applied to the description of existing approaches and technologies, resulting not just in recommendations for choosing methods but in a set of generic procedures for data exploration. Professionals practicing analysis will profit from tested solutions - illustrated in many examples - for reuse in the catalogue of techniques presented. Students and researchers will appreciate the detailed description and classification of exploration techniques, which are not limited to spatial data only. In addition, the general principles and approaches described will be useful for designers of new methods for EDA.