Tabla de Contenidos: “…Frontmatter -- INHALTSVERZEICHNIS -- Dank -- I Einleitung -- Forschungsstand -- Periodisierung -- Quellen -- Methoden -- II Theoretische Grundlagen -- 1 Science Fiction als Genre und Modus -- 2 Bestandteile radiofoner Science Fiction -- III Entstehungsphase (1935–1945) -- 1 Zwischen Golden Age und Geistiger Landesverteidigung -- 2 Erste utopische Sendungen im Beromünster-Spielplan -- 3 Absagen mit Gattungshinweisen -- 4 Synchronisierter Sound des Unwirklichen -- 5 Fazit: Aufblenden und Schneiden -- IV Konsolidierungsphase (1946–1965) -- 1 Aufschwung zur Zeit des Kalten Krieges -- 2 Allmählicher Einzug der Science Fiction ins Programm -- 3 Ablehnungen vor dem Hintergrund wachsender Spannungen -- 4 Zwischen konventionellem Erzählen und akusmatischen Klängen -- 5 Fazit: Überblenden und Filtern -- V Diversifikationsphase (1966–1985) -- 1 Ausbau im Zeichen medienpolitischen Wandels -- 2 Erweiterung des Science-Fiction-Programms -- 3 Zurückweisungen im Zeichen eines veränderten Genrebewusstseins -- 4 Neuartige Klänge mit Wiedererkennungswert -- 5 Fazit: Amplifizieren und Einfärben -- VI Das Unbekannte vertraut machen -- 1 Neues mit Bekanntem darstellen: Das Novum im Wandel der Zeit -- 2 Ambivalente Programmpolitik zwischen Anknüpfen und Abgrenzen -- 3 Science Fiction hören: Erkenntnisse aus dem Sound des Utopischen -- Abbildungs- und Abkürzungsverzeichnis -- Bibliographie…”

Materias: “…Science Periodicals…”

Materias:

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 -- Part 1 Getting Started with Data Science and Python -- Chapter 1 Discovering the Match between Data Science and Python -- Understanding Python as a Language -- Viewing Python's various uses as a general-purpose language -- Interpreting Python -- Compiling Python -- Defining Data Science -- Considering the emergence of data science -- Outlining the core competencies of a data scientist -- Linking data science, big data, and AI -- Creating the Data Science Pipeline -- Understanding Python's Role in Data Science -- Considering the shifting profile of data scientists -- Working with a multipurpose, simple, and efficient language -- Learning to Use Python Fast -- Loading data -- Training a model -- Viewing a result -- Chapter 2 Introducing Python's Capabilities and Wonders -- Working with Python -- Contributing to data science -- Getting a taste of the language -- Understanding the need for indentation -- Working with Jupyter Notebook and Google Colab -- Performing Rapid Prototyping and Experimentation -- Considering Speed of Execution -- Visualizing Power -- Using the Python Ecosystem for Data Science -- Accessing scientific tools using SciPy -- Performing fundamental scientific computing using NumPy -- Performing data analysis using pandas -- Implementing machine learning using Scikit-learn -- Going for deep learning with Keras and TensorFlow -- Performing analysis efficiently using XGBoost -- Plotting the data using Matplotlib -- Creating graphs with NetworkX -- Chapter 3 Setting Up Python for Data Science -- Working with Anaconda -- Using Jupyter Notebook -- Accessing the Anaconda Prompt -- Installing Anaconda on Windows -- Installing Anaconda on Linux…”

Tabla de Contenidos: “…Renewed giving for science : the re-emergence of philanthropic support for the scientific enterprise -- Tracing the past : the historical role of foundations in science and technology -- Typologies of science philanthropy : linking philanthropic approaches with responsible research and innovation -- Individuals, institutions, and networks : contemporary examples of science philanthropy -- Case studies in science philanthropy network-building : the Rockefeller Foundation Searchlight Network and the Sloan Digital Sky Survey -- Reflections from the field : perspectives on the societal responsibility of science philanthropy -- Novel modes of support : new approaches to science philanthropy -- Opportunities and challenges ahead : lessons learned and scenarios for the future of science philanthropy…”

Materias:

“…Explore financial data science using SAS. Financial Data Science with SAS provides readers with a comprehensive explanation of the theoretical and practical implementation of the various types of analytical techniques and quantitative tools that are used in the financial services industry. …”

“…Journal of the Romanian Regional Science Association…”

“…Effective Data Science Infrastructure teaches you to build data pipelines and project workflows that will supercharge data scientists and their projects. …”

“…Social science journal…”

Materias: “…Materials science…”

“…Effective Data Science Infrastructure teaches you to build data pipelines and project workflows that will supercharge data scientists and their projects. …”