Characterizing and Collecting Data
In the previous chapter, we focused on general concepts and ideas around probability and statistics, but how does this translate to the data within your organization or for your project?
In this chapter, we will cover different types of data you might find within your organization, methods for collecting and processing that data to apply the statistical techniques covered in the previous chapter, and more advanced machine learning and deep learning techniques we will cover in later chapters.
Before we dive into topics such as the different categories of data and methods for collecting, storing, and processing data, we need to ask a fundamental question:
Initially, this might seem like a trivial and obvious question, but many data science projects start on the wrong foot by not properly evaluating the feasibility of achieving business results with the data available.
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