Data analytics is changing the world and shows no signs of slowing down. If you’re curious about the world of data analytics, you may be wondering what exactly is a data set and what it is used for.
What Is a Data Set?
The term data set refers to a collection of data records that are related to each other in some way. Data sets are stored with specific names that can be used to retrieve the data at a later time.
Data sets are put together by data analysts by finding and cleaning the data and then categorizing it into relevant collections that can be used by organizations to measure different metrics. For example, a shop might use a data set to study their sales and customers, or a multinational company may find a data set useful for analyzing marketing or financial metrics. Scientists regularly use data sets to analyze things like climate and research findings. Even things like medical or insurance records are data sets. It would be hard to find a field that didn’t use some type of data set!
Why do data sets matter? They make it easier to conduct analysis and perform mathematical operations because when data is in a set, it’s categorized. They also help make sense of an overwhelming amount of numbers and information.
Data analysts perform a variety of techniques on data sets to extract valuable insights. They might find the mean, or the average, such as the average number of hours of television watched. Or they may want to know the range, to know how far the data extends.
The Difference: Data Sets vs Databases
You may be thinking that a data set is another name for a database. Isn’t a database a collection of data? While this is true, typically, databases are much larger and broader than data sets. While data sets are related to one specific topic, databases hold a greater amount of information about a number of different data sets.
In general, data sets need to be stored in a computer system so they can later be accessed, updated and manipulated. A database provides the structure and the space for a data set to be stored and worked with. Data analysts learn how to work with databases using language such as SQL, which allows them to query and update the data in an organized way.
Now that it’s clear what a data set is and how it’s different from a database, diving deeper we see that there are a variety of types of data sets that a data analyst must choose from when storing data.
Types of Data Sets:
Sequential vs. Partitioned
Firstly, it’s important to distinguish between sequential and partitioned data sets. A sequential data set is data that is stored and retrieved consecutively. Data that needs to be used in sequence, such as an alphabetical list, would be best stored in this way.
A partitioned data set is more like a library, where the overarching structure holding the data is called a directory. The components inside the directory are called members, each one holding a smaller data set. Data partitioning is particularly useful when working with very large data tables to break them up into more manageable parts.
Permanent vs. Temporary
Permanent data sets existed before a task begins and they won’t be automatically deleted after working with the data. These data sets need to be saved into a library on a computer to be accessed later.
On the other hand, temporary data sets are only used during a specific task, or “life cycle.” They may be used to pass some type of data from one step to another. These data sets only exist during the current session, and once the session is closed, the temporary data set will be deleted.
Other types of data sets
Numerical data, also known as quantitative data, is expressed in numbers instead of in what we know as natural language. This is the type of data that’s used to perform mathematical operations.
Bivariate data sets contain only two variables. The interesting thing about bivariate data is being able to find the relationship between the two variables; for example, a data set about height of basketball players and how many points they’ve scored.
Multivariate data sets contain at least three variables that are somehow related. You could study the color, size, and number of sales of a particular item of clothing using a multivariate data set.
Categorical data sets are about the characteristics or qualities of an object. For this reason, it’s also known as qualitative data. Categorical data can be broken down into two types. In a dichotomous data set, variables can have one of two values - true or false, for example. Polytomous data sets can have many possible values, such as color.
Correlation data sets involve relationships between variables that depend on each other. Correlations can be positive, negative or zero. Positive correlations show the related variables moving in the same direction, while in negative correlations, variables move in the opposite direction. If there’s no relationship shown, it can be called zero correlation.
You can take a look at our free Data Analytics Basics masterclasses if you want to know about these and more data analytics concepts, and start your journey in Data!
Common Data Sets You Use Every Day
We can see data sets all around us every day, from statistics reported on the news, to stock performance, to the scoring averages of our favorite sports teams, and many more. One data set that is commonly used across a wide range of industries, including healthcare, politics, and even marketing and advertising, is census data. Census data gives decision-makers key information about constituents or potential customers.
Another data we all rely on is weather and climate data. Meteorologists analyze climate data to come up with forecasts that allow us to plan trips and events, not to mention dress accordingly each day.
If you’re intrigued by how useful data sets are and want to try working with them yourself, you can get some hands-on practice with the following free data sets:
Housing Price Data looks at home sizes, prices, locations and other details. You can use this set to practice making regression models.
Die-hard football fan? Premier League Match is a data set exploring English Premier League football scores, teams and games.
If you’re curious about world health statistics, the World Health Organization supplies a multitude of data sets around a variety of public health issues.
FiveThirtyEight is another great source of data sets related to politics, sports, and more.
How to Learn More About Data Analytics
After getting a handle on what data sets are and how they’re absolutely everywhere, are you ready to learn the tools you need to work with data? Ironhack can take you from beginner to career-ready in the world of data analytics: check out our Data Analytics bootcamp!
