What is a dataset and why is it so important in data analysis?

Data analytics is one of the most promising sciences for the future. To process the vast amount of information generated in the world every second, it is essential to use applications such as a dataset, a key tool for managing and processing these resources.

Although data analytics is not a recent phenomenon, it has gained particular relevance with the rise of the internet, which significantly increased data production and transmission. As a result, new work systems and specific applications are constantly being developed to facilitate the work of professionals specialized in data management.

Among these stands out the so-called dataset. What exactly is this solution, which is key to data analysis? How does it work and how does it facilitate activities related to information analysis?

Dataset: definition and role in data analysis

As mentioned above, a dataset is a tool or even a working method for managing generated and collected information. It can also be defined as:

• A set of stored information.
• Structured to facilitate manipulation.
• A collection of data categorized in an organized way.

These developments have a significant impact on the practice of data analytics. Professionals in this field use datasets for their usefulness in efficiently managing and storing large volumes of information. These are some of the advantages of datasets in data analysis:

1. They facilitate the location of specific information.
2. They optimize data analysis tasks.
3. They streamline the process of identifying patterns, building models, and generating statistics, among others.

These features of using datasets in big data make activities such as the following easier, both for data scientists and other professionals in the field:

• Making informed decisions, using information that enables highly accurate predictions.
• Analyzing trends and patterns in specific sectors, areas, and even related to products or services under study.
• Developing predictive models: this supports the decision-making mentioned above. It helps predict user behavior, as well as applications in fields such as medicine or occupational risk prevention, among others.
• Optimizing processes. Results from specific workflows or ways of operating can be studied and, based on them, improvements can be implemented, trends consolidated, or processes adjusted.

Types of datasets

These developments have been created to meet the needs of professionals who handle the vast amounts of data and information generated daily. For this reason, there are different types of datasets, among which the following five stand out, with varying levels of complexity and completeness:

1. Tabular datasets: organize data in tables, with rows as entries or records and columns representing each field’s characteristics. They are the most commonly used and the least complex.
2. Text datasets: include unstructured data in text form. This includes resources such as emails, addresses, phone numbers, news articles, social media comments, and platform posts, etc.
3. Time series datasets: are based on data recorded at specific time intervals.
4. Image and video datasets: are used to generate visual patterns and for object classification, among other uses.
5. Audio datasets: are used in voice recognition applications and for analyzing sounds and audio.

The different types of datasets help streamline the work of big data specialists. To better understand these types and how each one works, it is important to have specialized training, such as the Master in Big Data & Analytics at EAE Barcelona. This program includes knowledge and subjects that go beyond understanding the solutions and tools that are essential today for managing information in a global and digital environment characterized by productivity.

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Dataset sources

Datasets work with data, so it is important to identify some of the sources used to obtain these resources and documentation. In the field of data analytics, databases such as the following are used:

• Research platforms that conduct studies and surveys generating valuable information.
• Universities and educational institutions.
• Government institutions and organizations that provide open and accessible data for everyone.
• Online data portals organized by categories and fields of activity.
• Big data companies that collect and sell pre-processed datasets.

Examples of these dataset sources include Google Dataset Search or companies such as Nielsen and Statista, which conduct studies, surveys, and research and provide the results to users who need them. It is important for professionals in the field to know where to obtain this knowledge in order to boost their employability and meet the challenges demanded by companies and institutions.