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Hands-On Data Science and Python Machine Learning

You're reading from   Hands-On Data Science and Python Machine Learning Perform data mining and machine learning efficiently using Python and Spark

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787280748
Length 420 pages
Edition 1st Edition
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Concepts
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Author (1):
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Frank Kane Frank Kane
Author Profile Icon Frank Kane
Frank Kane
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Table of Contents (11) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Statistics and Probability Refresher, and Python Practice 3. Matplotlib and Advanced Probability Concepts 4. Predictive Models 5. Machine Learning with Python 6. Recommender Systems 7. More Data Mining and Machine Learning Techniques 8. Dealing with Real-World Data 9. Apache Spark - Machine Learning on Big Data 10. Testing and Experimental Design

Data warehousing overview

Next, we're going to talk a little bit about data warehousing. This is a field that's really been upended recently by the advent of Hadoop, and some big data techniques and cloud computing. So, a lot of big buzz words there, but concepts that are important for you to understand.

Let's dive in and explore these concepts! Let's talk about ELT and ETL, and data warehousing in general. This is more of a concept, as opposed to a specific practical technique, so we're going to talk about it conceptually. But, it is something that's likely to come up in the setting of a job interview. So, let's make sure you understand these concepts.

We'll start by talking about data warehousing in general. What is a data warehouse? Well, it's basically a giant database that contains information from many different sources and ties...

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