Tabla de Contenidos: “…Transboundary water co-operation in the Jordan River Basin -- The risks of global warming to coral reef ecosystems -- Glossary -- A new vision of open knowledge systems for sustainability: Opportunities for social scientists -- Changing the conditions of change by learning to use the future differently -- Climate is culture -- Participatory water governance in Mercosur countries -- Open knowledge and learning for sustainability -- Winning environmental justice for the Lower Mekong Basin -- Global governance and sustainable development -- Bibliometric analysis of social science research into global environmental change -- Vulnerable and resilient children after disasters and gene–environment interplay -- Migration as an adaptation strategy to environmental change -- Basic statistics on the production of social science research -- Towards greater fairness in sharing the risks and burdens of global environmental change -- Integrated Research on Disaster Risk programme -- The role of the social sciences in adapting to climate change in northern Europe -- Climate change mitigation, a problem of injustice -- The politics of climate change and grassroots demands -- The paradoxes of climate change and migration -- Regional divides in global environmental change research capacity -- Climate change education and Education for Sustainable Development -- A functional risk society? …”

Tabla de Contenidos: “…Chapter 7: SAS® Software Engineers the Processing Environment for You -- Architecture -- The SAS platform -- Service-Oriented Architecture and microservices -- Differences between SOA and microservices -- SAS server versus a SAS grid -- In-database processing -- In-database procedures -- Additonal in-database processing SAS offerings -- SAS Scoring Accelerator -- SAS Code Accelerator -- In-memory processing -- SAS High-Performance Analytics Server -- SAS LASR Analytics Server -- SAS Cloud Analytics Server -- Dedicated hardware for in-memory processing -- Open platform and open source -- Running SAS from an iPython Jupyter Notebook -- SAS running in a cloud -- A public cloud -- A private cloud -- A hybrid cloud -- Running SAS processing outside the SAS platform -- The SAS Embedded Process -- The SAS Event Stream Processing engine -- SAS Viya the newest part of the SAS platform -- SAS Viya programming -- SAS Viya-based solutions -- Summary -- Chapter 8: Why SAS Programmers Love SAS -- Why SAS programmers love SAS -- Examples of why SAS programmers love SAS -- Additional coding examples -- The COMPARE procedure -- The OPTIONS procedure -- Analytics is a great career -- Analytics Center of Excellence -- The executive sponsor -- The data scientist -- The data manager -- The business analyst -- The ACE leader -- Where should an ACE be located? …”

Tabla de Contenidos: “…Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Book 1 Defining Data Science -- Chapter 1 Considering the History and Uses of Data Science -- Considering the Elements of Data Science -- Considering the emergence of data science -- Outlining the core competencies of a data scientist -- Linking data science, big data, and AI -- Understanding the role of programming -- Defining the Role of Data in the World -- Enticing people to buy products -- Keeping people safer -- Creating new technologies -- Performing analysis for research -- Providing art and entertainment -- Making life more interesting in other ways -- Creating the Data Science Pipeline -- Preparing the data -- Performing exploratory data analysis -- Learning from data -- Visualizing -- Obtaining insights and data products -- Comparing Different Languages Used for Data Science -- Obtaining an overview of data science languages -- Defining the pros and cons of using Python -- Defining the pros and cons of using R -- Learning to Perform Data Science Tasks Fast -- Loading data -- Training a model -- Viewing a result -- Chapter 2 Placing Data Science within the Realm of AI -- Seeing the Data to Data Science Relationship -- Considering the data architecture -- Acquiring data from various sources -- Performing data analysis -- Archiving the data -- Defining the Levels of AI -- Beginning with AI -- Advancing to machine learning -- Getting detailed with deep learning -- Creating a Pipeline from Data to AI -- Considering the desired output -- Defining a data architecture -- Combining various data sources -- Checking for errors and fixing them -- Performing the analysis -- Validating the result -- Enhancing application performance…”

“…Who This Book Is For If you are a Data Scientist or a Machine Learning expert, then this book is a very useful read in training your advanced machine learning and deep learning models. …”

“…With the rise of deep learning, it has become a popular choice of tool among data scientists and machine learning experts. Java Deep Learning Projects starts with an overview of deep learning concepts and then delves into advanced projects. …”

Tabla de Contenidos: “…-- Interview with Wayne Thompson, Chief Data Scientist at SAS Institute -- Key Takeaways -- Notes -- Further Reading -- Chapter 2: Unstructured Data Analytics: The Next Frontier of Analytics Innovation -- Introduction -- What Is UDA? …”

“…Who This Book Is For If you are a budding data scientist, or a data analyst with a basic knowledge of R, and want to get into the intricacies of data mining in a practical manner, this is the book for you. …”

“…Learning Python is written by Mark Lutz, author of Programming Python and Python Pocket Reference ; and David Ascher, a vision scientist and Python user. This book starts with a thorough introduction to the elements of Python: types, operators, statements, classes, functions, modules, and exceptions. …”

“…No prior knowledge is required. Aspiring data scientist, R users trying to learn Python and vice versa What You Will Learn Learn the nature of data through software with preliminary concepts right away in R Read data from various sources and export the R output to other software Perform effective data visualization with the nature of variables and rich alternative options Do exploratory data analysis for useful first sight understanding building up to the right attitude towards effective inference Learn statistical inference through simulation combining the classical inference and modern computational power Delve deep into regression models such as linear and logistic for continuous and discrete regressands for forming the fundamentals of modern statistics Introduce yourself to CART ? …”

Tabla de Contenidos: “…Cover -- Copyright -- Contributors -- Table of Contents -- Preface -- Section 1: AWS Data Engineering Concepts and Trends -- Chapter 1: An Introduction to Data Engineering -- Technical requirements -- The rise of big data as a corporate asset -- The challenges of ever-growing datasets -- The role of the data engineer as a big data enabler -- Understanding the role of the data engineer -- Understanding the role of the data scientist -- Understanding the role of the data analyst -- Understanding other common data-related roles -- The benefits of the cloud when building big data analytic solutions -- Hands-on - creating and accessing your AWS account -- Creating a new AWS account -- Accessing your AWS account -- Summary -- Chapter 2: Data Management Architectures for Analytics -- Technical requirements -- The evolution of data management for analytics -- Databases and data warehouses -- Dealing with big, unstructured data -- Cloud-based solutions for big data analytics -- A deeper dive into data warehouse concepts and architecture -- Dimensional modeling in data warehouses -- Understanding the role of data marts -- Distributed storage and massively parallel processing -- Columnar data storage and efficient data compression -- Feeding data into the warehouse - ETL and ELT pipelines -- An overview of data lake architecture and concepts -- Data lake logical architecture -- The storage layer and storage zones -- Catalog and search layers -- Ingestion layer -- The processing layer -- The consumption layer -- Data lake architecture summary -- Bringing together the best of data warehouses and data lakes -- The data lake house approach -- New data lake table formats -- Federated queries across database engines -- Hands-on - using the AWS Command Line Interface (CLI) to create Simple Storage Service (S3) buckets -- Accessing the AWS CLI…”

Tabla de Contenidos: “…Helping trusted messengers find their voice on climate change / Edward Maibach -- 60. From climate scientist to climate communicator: a process of evolution / Michael E. …”

Tabla de Contenidos: “…-- Step-by-step installation -- Installing the necessary packages -- Package upgrades -- Scientific distributions -- Anaconda -- Leveraging conda to install packages -- Enthought Canopy -- WinPython -- Explaining virtual environments -- Conda for managing environments -- A glance at the essential packages -- NumPy -- SciPy -- pandas -- pandas-profiling -- Scikit-learn -- Jupyter -- JupyterLab -- Matplotlib -- Seaborn -- Statsmodels -- Beautiful Soup -- NetworkX -- NLTK -- Gensim -- PyPy -- XGBoost -- LightGBM -- CatBoost -- TensorFlow -- Keras -- Introducing Jupyter -- Fast installation and first test usage -- Jupyter magic commands -- Installing packages directly from Jupyter Notebooks -- Checking the new JupyterLab environment -- How Jupyter Notebooks can help data scientists -- Alternatives to Jupyter -- Datasets and code used in this book -- Scikit-learn toy datasets -- The MLdata.org and other public repositories for open source data -- LIBSVM data examples -- Loading data directly from CSV or text files -- Scikit-learn sample generators -- Summary -- Chapter 2: Data Munging -- The data science process -- Data loading and preprocessing with pandas -- Fast and easy data loading -- Dealing with problematic data -- Dealing with big datasets -- Accessing other data formats -- Putting data together -- Data preprocessing -- Data selection -- Working with categorical and textual data -- A special type of data - text -- Scraping the web with Beautiful Soup -- Data processing with NumPy -- NumPy's n-dimensional array -- The basics of NumPy ndarray objects -- Creating NumPy arrays -- From lists to unidimensional arrays…”

Tabla de Contenidos: “…-- Importance of standardizing deliverables -- The art and science of designing insightful standardized reports -- Delving into parts that can be flexible but not compromised -- Mastering the research readout -- Tone and eye contact -- Summary -- Chapter 16: Data Visualization - the Power of Visuals to Help with Cognition and Decisions -- Data visualization for quick insights -- Understanding basic data visualization types -- Static versus interactive visualizations -- Static visualizations -- Interactive visualizations -- Understanding levels of detail -- Moving toward predictive and prescriptive analysis -- Predictive analysis -- When you don't have data scientists at your disposal -- Prescriptive analysis -- Summary -- Chapter 17: Heuristics - How We Measure Application Usability -- Getting to know application heuristics -- Exploring the Nielsen Norman Usability Heuristics Platform -- Visibility of system status -- Match between the system and the real world -- User control and freedom -- Consistency and standards -- Error prevention -- Recognition rather than recall -- Flexibility and efficiency of use -- Aesthetic and minimalist design -- Help users recognize, diagnose, and recover from errors -- Help and documentation -- Making use of other types of evaluation -- Cognitive walk-through -- Feature inspection -- Pluralistic walk-through -- Cognitive dimensions framework -- Wrapping up -- Summary -- In conclusion and thanks -- Index -- Other Books You May Enjoy…”

Tabla de Contenidos: “…-- Proxy-Optionen -- Protokolle wechseln -- Beispiel: FTP -- Beispiel: Message Interception -- Andere Protokolle -- Andere Beispiele für das Strangler Fig Pattern -- Verhaltensänderung während der Migration -- Pattern: UI Composition -- Beispiel: Page Composition -- Beispiel: Widget Composition -- Beispiel: Micro Frontends -- Wo wir es einsetzen -- Pattern: Branch by Abstraction -- Wie es funktioniert -- Als Fallback-Mechanismus -- Wo wir es einsetzen -- Pattern: Parallel Run -- Beispiel: Preisbildung von Kreditderivaten -- Beispiel: Homegate-Angebote -- Verifikationstechniken -- Spione einsetzen -- Scientist von GitHub -- Dark Launching und Canary Releasing -- Wo wir es einsetzen -- Pattern: Decorating Collaborator -- Beispiel: Loyalty-Programm -- Wo wir es einsetzen -- Pattern: Change Data Capture -- Beispiel: Loyalty-Karten ausgeben -- Change Data Capture implementieren -- Wo wir es einsetzen -- Zusammenfassung -- Kapitel 4: Die Datenbank aufteilen -- Pattern: Shared Database -- Hilfreiche Patterns -- Wo wir es einsetzen…”

Tabla de Contenidos: “…-- Language fit -- Different ways users can find you -- Categorizing channels -- Prioritizing channels to test -- Seeing What Excites Your Customers -- Onboarding versus Activation -- Use your own data first -- Get user feedback -- Landing That Repeat Customer -- Inspiring Users to Pull Out Their Wallets -- Turning Product Users into Product Proselytizers -- Part 3 Applying the Growth Hacking Process -- Chapter 6 Laying the Foundation for Growth -- Doing It Like a Scientist: Developing and Testing Hypotheses -- Identifying Your North Star Metric -- Revenue is not a North Star Metric -- Figuring out your product's North Star Metric -- Variations and exceptions to the rule -- Understanding How Your Product Grows Today -- A Simple Growth Equation -- Chapter 7 Identifying Potential Opportunities for Growth -- Narrowing Your Sights -- Choosing the Best Ideas to Test -- Internal data -- External inspiration…”

Tabla de Contenidos: “…-- Data analysts -- Data engineers -- Data scientists -- Machine learning engineers -- Summary -- Sample questions -- Chapter 3: Spark Architecture and Transformations -- Spark architecture -- Execution hierarchy -- Spark components -- Spark driver -- SparkSession -- Cluster manager -- Spark executors -- Partitioning in Spark -- Deployment modes -- RDDs -- Lazy computation -- Transformations -- Summary -- Sample questions -- Answers -- Part 3: Spark Operations -- Chapter 4: Spark DataFrames and their Operations -- Getting Started in PySpark -- Installing Spark -- Creating a Spark session -- Dataset API -- DataFrame API -- Creating DataFrame operations -- Using a list of rows -- Using a list of rows with schema -- Using Pandas DataFrames -- Using tuples -- How to view the DataFrames -- Viewing DataFrames -- Viewing top n rows -- Viewing DataFrame schema -- Viewing data vertically…”

Tabla de Contenidos: “…Kirchheimer as a political scientist -- 6. At a distance: More correspondence and another meeting -- 7. …”

Tabla de Contenidos: “…Intro -- Foreword -- Contents -- 1 Introduction -- Research on and Deployment of Chemical Weapons in World War I -- 2 The Scientist as Expert: Fritz Haber and German Chemical Warfare During the First World War and Beyond -- Abstract -- References -- 3 From Berlin-Dahlem to the Fronts of World War I: The Role of Fritz Haber and His Kaiser Wilhelm Institute in German Chemical Warfare -- Abstract -- 1 The Run-up to Ypres -- 2 Ypres, 22 April, 1915, 1700 GMT -- 3 The Indispensable Fritz Haber -- 4 Haber's Kaiser Wilhelm Institute Under Military Command -- 5 Haber's Views on Chemical Warfare -- 6 The Legacy of Ypres -- Acknowledgements -- References -- Additional Open Access Information -- 4 Clara Immerwahr: A Life in the Shadow of Fritz Haber -- Abstract -- 1 Prolog -- 2 Clara Immerwahr's Background -- 3 The Scientific Work of Clara Immerwahr -- 4 Clara's Husband: Fritz Haber -- 5 Clara Haber's Suicide -- 6 The "Myth of Clara Immerwahr" -- 7 Epilog -- Acknowledgements -- References -- Addition to Open Access Information -- 5 France's Political and Military Reaction in the Aftermath of the First German Chemical Offensive in April 1915: The Road to Retaliation in Kind -- Abstract -- 1 Introduction -- 2 Retaliation in Kind: A Purely Military Decision -- 3 Between Eagerness and Constraints: Organizing the Chemical Response -- 4 Chemical War: Scientific War, Industrial War -- 5 Retaliation in Kind: Towards Total War -- References -- 6 Preparing for Poison Warfare: The Ethics and Politics of Britain's Chemical Weapons Program, 1915-1945 -- Abstract -- 1 Introduction -- 2 Ypres 1915 -- 3 Porton Down -- 4 Servants of the Realm -- 5 Crisis of Legitimacy -- 6 Collaboration -- 7 The Geneva Protocol -- 8 Foreboding -- 9 Ethical Relativism -- References…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Dedication -- Foreword -- Contributors -- Table of Contents -- Preface -- Part 1: Getting Started with Data Engineering with GCP -- Chapter 1: Fundamentals of Data Engineering -- Understanding the data life cycle -- Understanding the need for a data warehouse -- Start with knowing the roles of a data engineer -- A data engineer versus a data scientist -- The focus of data engineers -- Going through the foundational concepts for data engineering -- ETL concept in data engineering -- The difference between ETL and ELT -- What is not big data? …”

Tabla de Contenidos: “…The Agents and the Audiences of Popularization -- 2.3.1. Scientists and Science Writers -- 2.3.2. The Role of Science Editors -- 2.3.3. …”
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