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Data Science for Dummies

Paperback Engels 2017 9781119327639
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Discover how data science can help you gain in-depth insight into your business - the easy way!

Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. 'Data Science For Dummies' is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus.

While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here’s what to expect:

- Provides a background in big data and data engineering before moving on to data science and how it's applied to generate value
- Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL
- Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things
- Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate

It's a big, big data world out there—let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.


Aantal pagina's:365
Hoofdrubriek:IT-management / ICT


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Part I: Getting Started With Data Science
1. Wrapping Your Head around Data Science
2. Exploring Data Engineering Pipelines and Infrastructure
3. Applying Data-Driven to Business and Industry

Part II: Using Data Science to Extract Meaning from Your Data
4. Machine learning: learning from data with your machine
5. Math, probability, and statistical Modeling
6. Using clustering to subdivide data
7. Modeling with instances
8. Building models that operate Internet-of-Things Devices

Part III: Creating Data Visualizations that Clearly Communicate Meaning
9. Following the Principles of Data Visualization Design
10. Using D3.js for Data Visualization
11. Web-Based Applications for Visualization Design
12. Exploring Best Practices in Dashboard Design
13. Making Maps from Spatial Data

Part IV: Computing for Data Science
14. Using Python for Data Science
15. Using Open Source R for Data Science
16. Using SQL in Data Science
17. Software Applications for Data Science

Part V: Applying Domain Expertise to Solve Real-World Problems Using Data Science
18. Data Science in Journalism: Nailing down the five Ws (and an H)
19. Delving into Environmental Data Science
20. Data Science for Driving Growth in E-Commerce
21. Using Data Science to Describe and Predict Criminal Activity

Part VI: The Part of Tens
22. Ten Phenomenal Resources for Open Data
23. Ten Free Data Science Tools and Applications


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