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Intro to Data - 01-03 - Overview
The purpose of this course is to learn about data as a foundation for data science. It might seem obvious, but half of data science is data, so as you can imagine, it’s really important to have a thorough understanding of data to be successful with data science. This course was designed to provide you with that foundational knowledge. As an overview of this course: First, we’ll learn about data.
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Intro to Data - 02-01 - Overview
Hello again and welcome back to this introductory course on data for data science. I’m Matthew Renze data science consultant, author, and public speaker. In this module, we’ll learn about data. We’ll learn what it is, and why it’s important for data science. What is data? Or technically, the more grammatically correct question is: “What are data?” More importantly, what is the purpose of data? And why is it important or useful to us in data science?
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Intro to Data - 02-02 - Data
When we think of data we might imagine: a bunch of ones-and-zeros sitting inside of a computer, the stats from our favorite sports team, or the medical records at our local hospital. But what exactly are data? Data are a collection of symbols that describe observations of the world around us. They record facts about the natural world that we live in. These include descriptions of the qualities of things in our world.
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Intro to Data - 02-03 - Information
Information is everywhere. We have information on the menus at our restaurants, in the books in our libraries, and on street signs while we’re driving. But what exactly is information in the context of data science? Information is something that reduces uncertainty about our world. It is the answer to questions like who, what, where, how many, or how much. Essentially, information provides clarity about the world we live in.
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Intro to Data - 02-05 - Purpose
What is the purpose of collecting data, creating information, and acquiring knowledge? Essentially, what makes data so important in data science? Data, on it’s own, is useless. However, it can be a stepping stone to achieve a goal or an objective of some kind. In order to achieve our goal we need to transform data into something that is actionable. We need to transform our data into actionable insight.
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Intro to Data - 02-06 - Process
Let’s take a look at a simple example of intelligent data-driven decision making in action. Imagine that we’re an investor. We’re considering making an investment in apples (the edible kind not the iPod kind). Our goal, obviously, is to make a profit. However, we want to make our investment using a data-driven decision- making process. First, we learn that the price of apples has been holding steady for the past year at $2 per kilogram (which is about $2 for 6 apples).
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Intro to Data - 03-02 - Types of Data
What types of data exist in data science and how do we classify them? In data science, there are two main types of data: categorical data and numerical data. These are the two most common types of data you will encounter in data science and the most common way of classifying or grouping the various types of data. You’ll encounter them quite frequently in data science, so it’s important that you clearly understand the distinction between the two.
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Intro to Data - 03-03 - Nominal Data
The first type of categorical data that we encounter in data science are nominal data. Nominal data are a type of categorical data. That is, they are used to represent named qualities. However, nominal data have no natural rank order to them (they differ by their name alone). For example, the colors red, green, and yellow all describe the color of apples. However, no one color is greater than or less than another color.
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Intro to Data - 03-04 - Ordinal Data
The second type of categorical data that we encounter in data science are ordinal data. Ordinal data are a type of categorical data. That is, they describe named qualities of things. However, ordinal data do have a natural rank order to them. So they can be sorted in order by their rank. For example, we could group apples into small, medium, and large sizes. Medium apples are larger than small apples, and large apples are larger than medium apples, so they do have a natural rank order.
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Intro to Data - 03-05 - Interval Data
The first type of numerical data that we encounter in data science are interval data. Interval data are a type of numerical data. That is, they represent measured quantities of things. Interval data allow for a degree of difference between two values (i.e. we can add or subtract the values in meaningful ways). However, interval scales have an arbitrary zero point on their scale (i.e. the place were zero appears on the scale was chosen for convenience not because it represents a true absence of the thing being measured.