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Data Science for Dummies.
Showing you how data science can help you gain in-depth insight into your business, this practical book is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. --
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Somerset :
John Wiley & Sons, Incorporated,
2017.
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| Edición: | 2nd ed. |
| Colección: | --For dummies.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Title Page; Copyright Page; Table of Contents; Foreword; 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; Chapter 1 Wrapping Your Head around Data Science; Seeing Who Can Make Use of Data Science; Analyzing the Pieces of the Data Science Puzzle; Collecting, querying, and consuming data; Applying mathematical modeling to data science tasks; Deriving insights from statistical methods; Coding, coding, coding
- it's just part of the game; Applying data science to a subject area.
- Communicating data insightsExploring the Data Science Solution Alternatives; Assembling your own in-house team; Outsourcing requirements to private data science consultants; Leveraging cloud-based platform solutions; Letting Data Science Make You More Marketable; Chapter 2 Exploring Data Engineering Pipelines and Infrastructure; Defining Big Data by the Three Vs; Grappling with data volume; Handling data velocity; Dealing with data variety; Identifying Big Data Sources; Grasping the Difference between Data Science and Data Engineering; Defining data science; Defining data engineering.
- Comparing data scientists and data engineersMaking Sense of Data in Hadoop; Digging into MapReduce; Stepping into real-time processing; Storing data on the Hadoop distributed file system (HDFS); Putting it all together on the Hadoop platform; Identifying Alternative Big Data Solutions; Introducing massively parallel processing (MPP) platforms; Introducing NoSQL databases; Data Engineering in Action: A Case Study; Identifying the business challenge; Solving business problems with data engineering; Boasting about benefits; Chapter 3 Applying Data-Driven Insights to Business and Industry.
- Benefiting from Business-Centric Data ScienceConverting Raw Data into Actionable Insights with Data Analytics; Types of analytics; Common challenges in analytics; Data wrangling; Taking Action on Business Insights; Distinguishing between Business Intelligence and Data Science; Business intelligence, defined; The kinds of data used in business intelligence; Technologies and skillsets that are useful in business intelligence; Defining Business-Centric Data Science; Kinds of data that are useful in business-centric data science.
- Technologies and skillsets that are useful in business-centric data scienceMaking business value from machine learning methods; Differentiating between Business Intelligence and Business-Centric Data Science; Knowing Whom to Call to Get the Job Done Right; Exploring Data Science in Business: A Data-Driven Business Success Story; Part 2 Using Data Science to Extract Meaning from Your Data; Chapter 4 Machine Learning: Learning from Data with Your Machine; Defining Machine Learning and Its Processes; Walking through the steps of the machine learning process.