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Intro to Data - 04-04 - Scalar Examples
In data science, we have several common scalar data types. We’re going to learn about the ones that you’re most likely to encounter. First, we have categorical data types. We typically encounter three categorical data types in data science. First, we have a character, which represents a single letter, digit, or symbol. We can string together a sequence of characters (called a character string) to represent words, numbers, and bodies of text.
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Intro to Data - 04-06 - Composite Examples
In data science, we have several common composite data types. We’re going to learn about the ones that you’re most likely to encounter. First, we have the composite data types that represent homogenous data. First, we have a vector, also known as an array, which is a one- dimensional sequence of homogenous data. Vectors are used to store a list of elements that are all of the same data type.
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Intro to Data - 05-02 - Tabular Data
Tabular data are the most common form of structured data that we use for analysis in data science. But what are tabular data and how do we organize our data in this way? Tabular data are data organized into a table. The table provides the data with structure. A table, is a two-dimensional grid of data. However, unlike a matrix, which we saw earlier, all of the elements in a table do not need to be all of the same data type Rather, all data in each column must be the same data type, which we refer to as homogenous data.
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Intro to Data - 05-04 - Variables
The world is in a constant state of change; things vary from one observation to the next. But how do we record these variations across observations in data science? A variable is placeholder for a value that changes. We call them “variables” because their values “vary” across each observation. In data science, we store variables on the columns of a table. Columns are the vertical groups of data that are contained within the table.
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Intro to Data - 05-06 - Queries
How do we extract information from tabular data? The answer, is a query! A query is computer representation of a question we want answer using a table of data. They allow us to ask questions of the data and return answers as results. Queries are created using programming languages. More specifically, we use a special type of programming language called a query language. The most popular query language is Structured Query Language (or SQL for short).
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Intro to Data - 05-07 - Summary
In this module, we learned about tabular data and how we can extract information from tables of data using queries. First, we learned that tabular data are data that are organized into tables consisting of rows and columns. Next, we learned that observations are records of observable phenomena, which are stored on the rows. Then, we learned that variables contain values that vary across each observation, which are stored on the columns.
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Intro to Data - 06-01 - Overview
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 the life cycle of data the journey of data as we move from data collection to action. How do we get from data collection to action in data science? What does the journey of data look like through these various stages?
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Intro to Data - 06-02 - Collection
The first step in the data lifecycle is data collection. We collect data about our world in a two-step process: First, we observe a phenomenon that exists in the natural world. This includes sensing the various qualities of the things we’re observing and measuring their quantities as well. Next, we record this observation using a symbolic representation. In data science, this typically involves encoding the observation in a computer as a binary representation.
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Intro to Data - 06-05 - Analysis
The fourth step in the data lifecycle is data analysis. Once we’ve processed our data, we want to analyze them to create new information that we can act upon. There can be many reasons to perform a data analysis. For example: - to provide support for or against decisions that we need to make, - to explain observations and behaviors that we see occurring, - and to discover new information from patterns that exist in the data.
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Intro to Data - 07-01 - Next Steps
Welcome to the final module of this introductory course on data for data science. I’m Matthew Renze, data science consultant, author and public speaker. Now let’s wrap things up for this course so that we can get started on our next course and begin applying this knowledge in the real world. You might be wondering: Where should I go next? What should I do to apply this knowledge I just learned?