Das stählerne Geheimnis

Hans Dominik

In seinem amerikanischen Stahlwerk stellt James William Roddington eine Vielzahl 100 Meter lange Rohre mit einem Innendurchmesser von einem Meter her. Er verkauft seine Fabriken an einen übermächtigen Konkurrenten, aber von seiner Stahlproduktion möchte er sich nicht trennen warum? Man munkelt von Riesenkanonen, die er in Auftrag hat. Roddington jedoch lässt die Rohre mit Schiffen zu einer Stelle des Pazifiks transportieren, an der der Meeresgrund durch ein Seebeben bei sagenhaften 15.000 Meter Tiefe liegt. Mithilfe schwimmender Plattformen und Riesenkräne formt er nun aus diesen Rohren eine gigantische Röhre, die vom Wasserspiegel bis zum Grund des Pazifiks ja sogar noch Hunderte Meter unter diesen reicht. Roddington selbst und seine Mitstreiter fahren nun per in der Röhre eingebautem Aufzug hinab zum Meersgrund.

Das Vermächtnis des Inka

Karl May

Karl Hammer hat einen Auftrag in Buenos Aires erhalten: Er soll den jungen Anton Engelhardt zu seinem Vater nach Lima bringen. Dazu müssen sie die Anden überqueren. Unterwegs helfen sie Hauka, dem Abkömmling der Inka-Herrscher, das Vermächtnis seines Vaters in Besitz zu nehmen. Der junge Inka-Prinz Haukaropora ist für sein Volk das Symbol früherer Macht und die letzte Hoffnung, die einstige prachtvolle Größe wiederzuerlangen. Doch die Reise wird nicht so einfach wie gedacht...

Das Verrätertor. Roman

Edgar Wallace

Der wohlhabende Londoner Geschäftsmann Trayne hat einen raffinierten Plan ausgearbeitet, um die berühmten Kronjuwelen aus dem Tower zu rauben. Mühelos werden daraufhin die Juwelen erbeutet. Graham gerade aus dem Gefängnis entlassen. Da erfährt Graham von Dinah, dass Trayne ihn betrügen will. Graham schlägt Trayne nieder und flieht mit Dinah und den Juwelen. An Bord des Fluchtschiffs wird Graham versehentlich von Dinah getötet. Hope hat inzwischen die Polizei verständigt. Diese findet Trayne und erfährt von ihm, dass sich auf dem Schiff eine Zeitbombe befindet. Kurz bevor das Schiff explodiert, können die Juwelen gerettet werden.

[dasein: оборона присутності]

Ярина Чорногуз

Вибір без вибору. Пітьма і лють. Наша згода загинути. Наш вибір вбивати. Бій і порожнеча. Щоденні втрати. Втрати, які вже не рахують та не відчувають. Кордони землі, що впізнаєш навпомацки. Поезії Ярини Чорногуз 2014 це своєрідні листи з фронту, листи з передової, де ти не знайдеш опису бойових дій чи переліку звільнених територій. Це листи про біль від утрати загиблих побратимів та посестер, листи про буття на війні тут і зараз, про усвідомлення та прийняття власного вибору піти воювати, листи про те, як виглядатиме світ після всього цього болю, що розриває зсередини. Листи про те, як виглядає оборона твоєї присутності в цьому світі. Ці тексти Ярина написала за останні півтора року ротації на фронті, значна частина якої припала на повномасштабну війну. «У цій добірці, 2014 пише Ярина, 2014 промовлятиме моя пам2019ять про Скадовськ і морське узбережжя Херсонщини, Сєвєродонецьк та Маріуполь, висоти над Новоайдаром, дорогу Бахмут2012Лисичанськ і села по той бік Сіверського Дінця, річки, яка стала українським Стіксом. І всі вони відчуваються мені як ще одна мала батьківщина, бо вони переміряні нашими кроками вздовж і впоперек. Деякі вільні й оборонені, більшість з них 2014 ще належить звільнити».

Dasz radę, Marcelko

Marcin Pałasz

Marcelka po wypadku chodzi o kuli i musi dużo ćwiczyć, żeby wyzdrowieć.  Nie jest jej łatwo, tym bardziej że jej klasę zdominowały dwie dziewczyny, które dokuczają jej i się z niej wyśmiewają. Dziewczynka marzy o tym, żeby mieć psa, ale mama mówi: „Nie ma mowy”, nawet kiedy ciocia znajduje na ulicy małego psiaka i musi go oddać do schroniska. Jednak pan doktor twierdzi, że taki psiak pomógłby Marcelce  w leczeniu. I tak oto dziewczynka staje się panią psa Rozrabiaka i już nie jest samotna. A że dla swojego Rozrabiaka zrobi wszystko, poradzi sobie i z rehabilitacją, i z głupimi żartami niesympatycznych koleżanek.

Dasz radę. O bezpiecznym wychodzeniu z uzależnień

Bohdan T. Woronowicz, Karolina Prewęcka

Współautor tej książki oraz wielu innych publikacji z dziedziny uzależnień, niezwykle ceniony psychoterapeuta, dr n. med., specjalista psychiatra Bohdan Tadeusz Woronowicz podczas wieloletniej praktyki pomógł rzeszom pacjentów. Wiele mu też zawdzięcza Ruch Anonimowych Alkoholików w Polsce. Jak pisze dr Woronowicz we wstępie, o przebiegu leczenia i o swoim wychodzeniu z choroby opowiedziały w tej książce osoby znające problem z alkoholem z autopsji. Wśród nich są więc postaci zarówno mniej, jak i bardziej znane, ludzie z dużym dorobkiem życiowym, ale również tacy, którzy stracili wszystko i dobrze wiedzą, czym jest bezdomność. Warto sięgnąć po Dasz radę zawiera bezcenne wskazówki, zachęca do pójścia do specjalisty, a przede wszystkim zdejmuje piętno z uzależnionego, ukazując go jako człowieka udręczonego chorobą i potrzebującego pomocy. Nowe, uzupełnione i zaktualizowane wydanie książki, która po raz pierwszy ukazała się w 2014 roku.

Data Acquisition using LabVIEW. Click here to enter text

Behzad Ehsani

NI LabVIEW's intuitive graphical interface eliminates the steep learning curve associated with text-based languages such as C or C++. LabVIEW is a proven and powerful integrated development environment to interact with measurement and control hardware, analyze data, publish results, and distribute systems. This hands-on tutorial guide helps you harness the power of LabVIEW for data acquisition. This book begins with a quick introduction to LabVIEW, running through the fundamentals of communication and data collection. Then get to grips with the auto-code generation feature of LabVIEW using its GUI interface. You will learn how to use NI-DAQmax Data acquisition VIs, showing how LabVIEW can be used to appropriate a true physical phenomenon (such as temperature, light, and so on) and convert it to an appropriate data type that can be manipulated and analyzed with a computer. You will also learn how to create Distribution Kit for LabVIEW, acquainting yourself with various debugging techniques offered by LabVIEW to help you in situations where bugs are not letting you run your programs as intended.By the end of the book, you will have a clear idea how to build your own data acquisition system independently and much more.

Data Analysis and Business Modeling with Excel 2013. Manage, analyze, and visualize data with Microsoft Excel 2013 to transform raw data into ready to use information

Hernan D Rojas, David Rojas

Data Analysis for Business Decisions. A Laboratory Manual

Mercury Learning and Information, Andres Fortino

This manual is for business analysts to enhance their statistical analysis skills, with case studies focusing mainly on Excel. It covers basic descriptive techniques, linear regression, forecasting, t-Test, chi-square, A/B testing, text data analysis, and Big Data management. Companion files include solution spreadsheets, sample files, and data sets.The course starts with data shaping and cleaning, installing the Analysis ToolPak, and descriptive statistics. It progresses through histograms, scatter plots, Pareto analysis, correlation, linear and multivariate regression, and forecasting. Advanced topics include inferential statistics, contingency analysis, and A/B testing. The final chapters cover text analytics, big data sets, and data visualization.These techniques are crucial for informed business decisions. This book guides users from basic to advanced analysis, blending theory with practical skills. Companion files enhance learning, making this manual essential for mastering statistical analysis in business.

Data Analysis Foundations with Python. Master Data Analysis with Python: From Basics to Advanced Techniques

Cuantum Technologies LLC

Embark on a comprehensive journey through data analysis with Python. Begin with an introduction to data analysis and Python, setting a strong foundation before delving into Python programming basics. Learn to set up your data analysis environment, ensuring you have the necessary tools and libraries at your fingertips. As you progress, gain proficiency in NumPy for numerical operations and Pandas for data manipulation, mastering the skills to handle and transform data efficiently.Proceed to data visualization with Matplotlib and Seaborn, where you'll create insightful visualizations to uncover patterns and trends. Understand the core principles of exploratory data analysis (EDA) and data preprocessing, preparing your data for robust analysis. Explore probability theory and hypothesis testing to make data-driven conclusions and get introduced to the fundamentals of machine learning. Delve into supervised and unsupervised learning techniques, laying the groundwork for predictive modeling.To solidify your knowledge, engage with two practical case studies: sales data analysis and social media sentiment analysis. These real-world applications will demonstrate best practices and provide valuable tips for your data analysis projects.

Data Analysis with IBM SPSS Statistics. Implementing data modeling, descriptive statistics and ANOVA

Ken Stehlik-Barry, Anthony Babinec

SPSS Statistics is a software package used for logical batched and non-batched statistical analysis. Analytical tools such as SPSS can readily provide even a novice user with an overwhelming amount of information and a broad range of options for analyzing patterns in the data. The journey starts with installing and configuring SPSS Statistics for first use and exploring the data to understand its potential (as well as its limitations). Use the right statistical analysis technique such as regression, classification and more, and analyze your data in the best possible manner. Work with graphs and charts to visualize your findings. With this information in hand, the discovery of patterns within the data can be undertaken. Finally, the high level objective of developing predictive models that can be applied to other situations will be addressed. By the end of this book, you will have a firm understanding of the various statistical analysis techniques offered by SPSS Statistics, and be able to master its use for data analysis with ease.

Data Analysis with Python. A Modern Approach

David Taieb

Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects.Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence.

Data Analysis with R. A comprehensive guide to manipulating, analyzing, and visualizing data in R - Second Edition

Tony Fischetti

Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples.Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility.This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.

Data Analysis with R. Click here to enter text

Tony Fischetti, Tony Fischetti

Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. With over 7,000 user contributed packages, it’s easy to find support for the latest and greatest algorithms and techniques.Starting with the basics of R and statistical reasoning, Data Analysis with R dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples.Packed with engaging problems and exercises, this book begins with a review of R and its syntax. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with “messy data”, large data, communicating results, and facilitating reproducibility.This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.

Data Analysis with Stata. Explore the big data field and learn how to perform data analytics and predictive modelling in STATA

Prasad Kothari, Rahul Sharma

Data Analytics for Marketing. A practical guide to analyzing marketing data using Python

Guilherme Diaz-Bérrio

Most marketing professionals are familiar with various sources of customer data that promise insights for success. There are extensive sources of data, from customer surveys to digital marketing data. Moreover, there is an increasing variety of tools and techniques to shape data, from small to big data. However, having the right knowledge and understanding the context of how to use data and tools is crucial.In this book, you’ll learn how to give context to your data and turn it into useful information. You’ll understand how and where to use a tool or dataset for a specific question, exploring the what and why questions to provide real value to your stakeholders. Using Python, this book will delve into the basics of analytics and causal inference. Then, you’ll focus on visualization and presentation, followed by understanding guidelines on how to present and condense large amounts of information into KPIs. After learning how to plan ahead and forecast, you’ll delve into customer analytics and insights. Finally, you’ll measure the effectiveness of your marketing efforts and derive insights for data-driven decision-making.By the end of this book, you’ll understand the tools you need to use on specific datasets to provide context and shape your data, as well as to gain information to boost your marketing efforts.

Data Analytics Made Easy. Analyze and present data to make informed decisions without writing any code

Andrea De Mauro, Francesco Marzoni, Andrew J. Walter

Data Analytics Made Easy is an accessible beginner’s guide for anyone working with data. The book interweaves four key elements:Data visualizations and storytelling – Tired of people not listening to you and ignoring your results? Don’t worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience.Automating your data workflows – Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You’ll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components.Machine learning – Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You’ll not only be able to understand data scientists’ machine learning models; you’ll be able to challenge them and build your own.Creating interactive dashboards – Follow the book’s simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results.

Data Analytics. Master the Art of Data Analytics with Essential Tools and Techniques

Mercury Learning and Information, Christopher Greco

Data analytics is becoming increasingly important in our daily lives. This book offers a comprehensive view of data analytics skills, starting with a primer on statistics and progressing to the application of these methods. The text includes various formulas and algorithms used in data analytics, which can be applied in any software to achieve desired results. Through numerous demonstrations, it provides clear instruction on how to incorporate data analytics into critical thinking.The book covers a range of methods and techniques, supplemented with case studies specific to project managers, systems engineers, and cybersecurity professionals. Each profession can practice data analytics relevant to their fields. The main objective is to refresh statistical knowledge necessary for building data analytics models and to foster analytical thinking essential across these professions.From introducing statistics and data to reviewing central tendency measures and probability, the book moves to more complex topics like effect size, analysis methods, and data presentation. By the end of the course, readers will be well-versed in data analytics, ready to apply these skills effectively in their respective fields, enhancing decision-making and analytical thinking.

Data Analytics Using Splunk 9.x. A practical guide to implementing Splunk’s features for performing data analysis at scale

Dr. Nadine Shillingford

Splunk 9 improves on the existing Splunk tool to include important features such as federated search, observability, performance improvements, and dashboarding. This book helps you to make the best use of the impressive and new features to prepare a Splunk installation that can be employed in the data analysis process.Starting with an introduction to the different Splunk components, such as indexers, search heads, and forwarders, this Splunk book takes you through the step-by-step installation and configuration instructions for basic Splunk components using Amazon Web Services (AWS) instances. You’ll import the BOTS v1 dataset into a search head and begin exploring data using the Splunk Search Processing Language (SPL), covering various types of Splunk commands, lookups, and macros. After that, you’ll create tables, charts, and dashboards using Splunk’s new Dashboard Studio, and then advance to work with clustering, container management, data models, federated search, bucket merging, and more.By the end of the book, you’ll not only have learned everything about the latest features of Splunk 9 but also have a solid understanding of the performance tuning techniques in the latest version.

Data Augmentation with Python. Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data

Duc Haba

Data is paramount in AI projects, especially for deep learning and generative AI, as forecasting accuracy relies on input datasets being robust. Acquiring additional data through traditional methods can be challenging, expensive, and impractical, and data augmentation offers an economical option to extend the dataset.The book teaches you over 20 geometric, photometric, and random erasing augmentation methods using seven real-world datasets for image classification and segmentation. You’ll also review eight image augmentation open source libraries, write object-oriented programming (OOP) wrapper functions in Python Notebooks, view color image augmentation effects, analyze safe levels and biases, as well as explore fun facts and take on fun challenges. As you advance, you’ll discover over 20 character and word techniques for text augmentation using two real-world datasets and excerpts from four classic books. The chapter on advanced text augmentation uses machine learning to extend the text dataset, such as Transformer, Word2vec, BERT, GPT-2, and others. While chapters on audio and tabular data have real-world data, open source libraries, amazing custom plots, and Python Notebook, along with fun facts and challenges.By the end of this book, you will be proficient in image, text, audio, and tabular data augmentation techniques.

Data Center Virtualization Certification: VCP6.5-DCV Exam Guide. Everything you need to achieve 2V0-622 certification – with exam tips and exercises

Andrea Mauro, Paolo Valsecchi

This exam guide enables you to install, configure, and manage the vSphere 6.5 infrastructure in all its components: vCenter Server, ESXi hosts, and virtual machines, while helping you to prepare for the industry standard certification.This data center book will assist you in automating administration tasks and enhancing your environment’s capabilities. You will begin with an introduction to all aspects related to security, networking, and storage in vSphere 6.5. Next, you will learn about resource management and understand how to back up and restore the vSphere 6.5 infrastructure. As you advance, you will also cover troubleshooting, deployment, availability, and virtual machine management. This is followed by two mock tests that will test your knowledge and challenge your understanding of all the topics included in the exam.By the end of this book, you will not only have learned about virtualization and its techniques, but you’ll also be prepared to pass the VCP6.5-DCV (2V0-622) exam.

Data Cleaning and Exploration with Machine Learning. Get to grips with machine learning techniques to achieve sparkling-clean data quickly

Michael Walker

Many individuals who know how to run machine learning algorithms do not have a good sense of the statistical assumptions they make and how to match the properties of the data to the algorithm for the best results.As you start with this book, models are carefully chosen to help you grasp the underlying data, including in-feature importance and correlation, and the distribution of features and targets. The first two parts of the book introduce you to techniques for preparing data for ML algorithms, without being bashful about using some ML techniques for data cleaning, including anomaly detection and feature selection. The book then helps you apply that knowledge to a wide variety of ML tasks. You’ll gain an understanding of popular supervised and unsupervised algorithms, how to prepare data for them, and how to evaluate them. Next, you’ll build models and understand the relationships in your data, as well as perform cleaning and exploration tasks with that data. You’ll make quick progress in studying the distribution of variables, identifying anomalies, and examining bivariate relationships, as you focus more on the accuracy of predictions in this book.By the end of this book, you’ll be able to deal with complex data problems using unsupervised ML algorithms like principal component analysis and k-means clustering.

Data Cleaning with Power BI. The definitive guide to transforming dirty data into actionable insights

Gus Frazer

Microsoft Power BI offers a range of powerful data cleaning and preparation options through tools such as DAX, Power Query, and the M language. However, despite its user-friendly interface, mastering it can be challenging. Whether you're a seasoned analyst or a novice exploring the potential of Power BI, this comprehensive guide equips you with techniques to transform raw data into a reliable foundation for insightful analysis and visualization.This book serves as a comprehensive guide to data cleaning, starting with data quality, common data challenges, and best practices for handling data. You’ll learn how to import and clean data with Query Editor and transform data using the M query language. As you advance, you’ll explore Power BI’s data modeling capabilities for efficient cleaning and establishing relationships. Later chapters cover best practices for using Power Automate for data cleaning and task automation. Finally, you’ll discover how OpenAI and ChatGPT can make data cleaning in Power BI easier.By the end of the book, you will have a comprehensive understanding of data cleaning concepts, techniques, and how to use Power BI and its tools for effective data preparation.

Data Democratization with Domo. Bring together every component of your business to make better data-driven decisions using Domo

Jeff Burtenshaw

Domo is a power-packed business intelligence (BI) platform that empowers organizations to track, analyze, and activate data in record time at cloud scale and performance.Data Democratization with Domo begins with an overview of the Domo ecosystem. You’ll learn how to get data into the cloud with Domo data connectors and Workbench; profile datasets; use Magic ETL to transform data; work with in-memory data sculpting tools (Data Views and Beast Modes); create, edit, and link card visualizations; and create card drill paths using Domo Analyzer. Next, you’ll discover options to distribute content with real-time updates using Domo Embed and digital wallboards. As you advance, you’ll understand how to use alerts and webhooks to drive automated actions. You’ll also build and deploy a custom app to the Domo Appstore and find out how to code Python apps, use Jupyter Notebooks, and insert R custom models. Furthermore, you’ll learn how to use Auto ML to automatically evaluate dozens of models for the best fit using SageMaker and produce a predictive model as well as use Python and the Domo Command Line Interface tool to extend Domo. Finally, you’ll learn how to govern and secure the entire Domo platform.By the end of this book, you’ll have gained the skills you need to become a successful Domo master.