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Intro to Data - 03-07 - Summary
In this module, we learned about the various types of data that we encounter in data science. First, we learned that data are divided conceptually into two main types: categorical data and numerical data. Next, we learned about nominal data, named categories without a natural rank order. Then, we learned about ordinal data, named categories with a natural rank order. Next, we learned about interval data, numerical measurements with an arbitrary zero point.
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Intro to Data - 04-07 - Summary
In this module, we learned about data types and how we represent and store data in a computer. First, we learned about data types, binary representations of data used by computer systems. Next, we learned about scalar data types, representations of data that store a single value. Then, we saw examples of common scalar data types, including: characters, integers, dates, and times. Next, we learned about composite data types, representations that store data as a group of related values.
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Intro to Data - 06-07 - Repeat
The final stage in the data lifecycle is to repeat the process. As I mentioned in the previous step, it’s important that we observe the outcome of our actions to see whether they made a positive impact, a negative impact, or led to no change at all. We want to use this information as feedback to drive the next iteration of the process. Feedback is very important in data science – it tells us whether we’re steering the ship in the right direction or headed towards a giant cliff.
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Intro to Data - 05-05 - Relationships
Everything is related in some way, shape, or form. So how do we represent relationships in data science? In data science, we want each table to contain a single type of observation or type of entity. For example, we want to keep a list of our patients in one table and a list of our doctors a separate table. We want each table to only contain data that are related to one another in a highly cohesive way.
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Intro to Data - 07-02 - Course Summary
Finally, let’s summarize the key concepts that we learned in the course. In this course, we learned about data as a foundation for data science. First, we learned that data are a collection of facts that describe observations of the world around us. Next, we learned that there are various types of data including categorical and numerical data. Then, we learned that data can be represented and stored in computers using data types.
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Intro to Data - 04-03 - Scalar Data Types
The most basic building block of data in data science is a scalar data type. Scalar data types store a single unit of data. This can be a letter, a number, a date, a time, or something else. We refer to them as scalar data types because a scalar variable in mathematics can hold one and only one value at a time. Scalar data types are also the most basic unit of storage for data in a computer.
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Intro to Data - 06-03 - Storage
The second step in the data lifecycle is data storage. Once we’ve collected and recorded an observation as data, we need to store it so that it can be retrieved for future analysis. As data are being recorded by sensors, these data are first recorded temporarily in a type of memory called volatile storage. Volatile storage means that the data are lost when the device loses power. As a result, we need to transfer our data somewhere more permanent, so that they will be available anytime we need them.
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Intro to Data - 05-03 - Observations
Data science is founded upon making observations of the world around us. But what are observations and how do we record them in tabular data? An observation is a recording of the qualities and quantities of an observable phenomenon in the natural world. This includes what we can see, hear, feel, or measure with sensors. In data science, we record observations on the rows of a table. The rows are the horizontal groups of data that are contained within the table.
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Intro to Data - 04-05 - Composite Data Types
Building upon scalar data types, we also have composite data types in data science. A composite data type is a logical container used to organize related data. It contains a set of scalar data types organized in a specific way. Composite data types allow us to store and access information effectively. They provide methods for accessing individual scalar values and performing operations on groups of scalar values.
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Intro to Data - 06-08 - Summary
In this module, we learned about the data life cycle - the journey of data from inception to action. First, we learned about data collection and how we record observations of our world. Next, we learned about data storage, and the various types of data repositories we encounter. Then, we learned about data processing, and the steps we perform to prepare our data for analysis. Next, we learned about data analysis, and the many tools we can use to analyze our data.