Data analytics is changing the world and shows no signs of slowing down. And if you’re curious about the world of data analytics, you’ve probably heard of data sets and are wondering what exactly they are and what they do. Luckily for you, we’re here to help.
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 and 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.
Data sets are structured collections of data, which can be numerical, categorical, or textual. For example, the 'Iris' data set is widely used in machine learning for classification tasks. It consists of measurements of iris flowers and their respective species, helping to train algorithms in distinguishing between different varieties.
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? Well, 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.
According to a report by Gartner, 90% of corporate strategies will explicitly mention information as a critical asset by 2022. This highlights the increasing reliance on data sets for decision-making in businesses worldwide
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, databases are typically 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, let’s dive deeper and see that there are a variety of types of data sets that a data analyst must choose from when storing data.
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Understanding Different Types of Data Sets
Sequential vs. partitioned
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 exist before a task begins and 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 lifecycle. 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 the ability to reveal the relationship between two variables; for example, a bivariate data set about height of basketball players and how many points they’ve scored could yield interesting results.
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 related variables moving in the same direction, while negative correlations show variables moving in the opposite direction. If there’s no relationship shown, it’s called zero correlation.
You can take a look at our free Data Analytics Basics masterclasses if you want to know about these data analytics concepts (and more!) and start your journey into data!
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Common Data Sets with Everyday Use
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 much 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 to 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.
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!
Getting Started with Data Sets
Define the Purpose: Identify what you want to achieve with your data analysis.
Select the Right Data Set: Choose a data set that fits your needs. Examples: Kaggle for machine learning projects, UCI Machine Learning Repository for a variety of data sets.
Data Cleaning: Preprocess your data by handling missing values, filtering outliers, and normalizing values.
Exploratory Data Analysis: Use tools like Pandas and Matplotlib in Python to understand data distributions and identify patterns.
About the Author:
Juliette Carreiro is a tech writer, with two years of experience writing in-depth articles for Ironhack. Covering everything from career advice and navigating the job ladder, to the future impact of AI in the global tech space, Juliette is the go-to for Ironhack’s community of aspiring tech professionals.
