“…Programming Skills for Data Science brings together all the foundational skills you need to get started, even if you have no programming or data science experience. …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: First Steps -- Introducing data science and Python -- Installing Python -- Python 2 or Python 3? …”

Tabla de Contenidos: “…Data Science for Marketing Analytics: Achieve your marketing goals with the data analytics power of Python…”

Tabla de Contenidos: “…Hands-On Data Science for Marketing: Improve your marketing strategies with machine learning using Python and R…”

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 -- Defining the Sexiest Job of the 21st Century -- 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 -- Creating the Data Science Pipeline -- Preparing the data -- Performing exploratory data analysis -- Learning from data -- Visualizing -- Obtaining insights and data products -- 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 -- Why Python? …”

Tabla de Contenidos: “…Introduction -- Data Science Is OSEMN -- Obtaining Data -- Scrubbing Data -- Exploring Data -- Modeling Data -- Interpreting Data -- Intermezzo Chapters -- What Is the Command Line? …”

Tabla de Contenidos: “…Intro -- Title Page -- Copyright Page -- Table of Contents -- Foreword -- Introduction -- About This Book -- Foolish Assumptions -- How This Book Is Organized -- Icons Used In This Book -- Beyond The Book -- Where To Go From Here -- Part 1: Optimizing Your Data Science Investment -- Chapter 1: Framing Data Science Strategy -- Establishing the Data Science Narrative -- Capture -- Maintain -- Process -- Analyze -- Communicate -- Actuate -- Sorting Out the Concept of a Data-driven Organization -- Approaching data-driven -- Being data obsessed -- Sorting Out the Concept of Machine Learning -- Defining and Scoping a Data Science Strategy -- Objectives -- Approach -- Choices -- Data -- Legal -- Ethics -- Competence -- Infrastructure -- Governance and security -- Commercial/business models -- Measurements -- Chapter 2: Considering the Inherent Complexity in Data Science -- Diagnosing Complexity in Data Science -- Recognizing Complexity as a Potential -- Enrolling in Data Science Pitfalls 101 -- Believing that all data is needed -- Thinking that investing in a data lake will solve all your problems -- Focusing on AI when analytics is enough -- Believing in the 1-tool approach -- Investing only in certain areas -- Leveraging the infrastructure for reporting rather than exploration -- Underestimating the need for skilled data scientists -- `Navigating the Complexity -- Chapter 3: Dealing with Difficult Challenges -- Getting Data from There to Here -- Handling dependencies on data owned by others -- Managing data transfer and computation across-country borders -- Managing Data Consistency Across the Data Science Environment -- Securing Explainability in AI -- Dealing with the Difference between Machine Learning and Traditional Software Programming -- Managing the Rapid AI Technology Evolution and Lack of Standardization…”

“…It’s ideal whether you’re a non-programmer with no data science experience, or a data scientist switching to R from other software such as SAS or Excel. …”

“…To make organizational change work, you need to base it on science, not intuition. Despite hundreds of books on change, failure rates remain sky high. …”

Materias: “…Data structures (Computer science)…”

Materias: “…Physical Sciences & Mathematics…”

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Materias: “…Science Experiments…”

“…R being the most widely used programming language when used with data science can be a powerful combination to solve complexities involved with varied data sets in the real world. …”

“…This webcast will provide an introduction and practical information about how you can use science to understand and improve the tools you build and use everyday. …”

Tabla de Contenidos: “…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 I Getting Started with Python for Data Science; Chapter 1 Discovering the Match between Data Science and Python; Defining the Sexiest Job of the 21st Century; Considering the emergence of data science; Outlining the core competencies of a data scientist; Linking data science and big data; Understanding the role of programming; Creating the Data Science Pipeline; Preparing the data; Performing exploratory data analysis…”

Materias: “…Engineering & Applied Sciences…”

Materias:

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