We’ll go over the following:
- Dataset Definition
- Types of Datasets
- How to Create a Dataset
- Benefits of using a Dataset
- Use Cases for Dataset
- Dataset Example
A dataset, or data set, is a collection of data related to a particular topic, theme, or industry. Datasets include different types of information, such as numbers, text, images, videos, and audio, and can be stored in various formats, such as CSV, JSON, or SQL. So, a dataset typically involves structured data for a specific purpose and is related to the same subject.
You can use datasets to conduct market research, analyze competitors, compare prices, identify and study trends, or train machine learning models. These are just a few examples, and datasets are useful in various areas and situations.
Types of Datasets
Datasets can be classified in several ways. Here are some of the most important types of datasets.
Based on the Data Type
- Numerical datasets: Contain numbers and are used for quantitative analysis.
- Text datasets: Contain posts, text messages, and documents.
- Multimedia datasets: Contain images, videos, and audio files.
- Time-series datasets: Contain data collected over time to analyze trends and patterns.
- Spatial dataset: Contain geographically referenced information, such as GPS data.
Based on Data Structure
- Structured datasets: Organized in specific structures to make it easier to query and analyze data.
- Unstructured datasets: Don’t have a well-defined schema. They can include a variety of types of data.
- Hybrid datasets: Include both structured and unstructured data.
Numerical datasets: Involve only numbers.
Bivariate datasets: Involve two data variables.
Multivariate datasets: Involve three or more data variables.
Categorical datasets: Consist of categorical variables that can take only a limited set of values.
Correlation datasets: Contain data variables that relate to each other.
- Datasets for training ML: Used to train the model.
- Datasets for validation: Used to reduce overfitting and make the model more accurate.
- Dataset for testing: Used for testing the final output of the model to confirm its accuracy.
How to Create a Dataset
To understand datasets’ benefits, you must first know how they are produced. There are two ways to do it.
The first is to build a custom data parser to retrieve data from multiple sources. This task becomes easier with an advanced tool. In detail, Bright Data’s web scraping tool has built-in parsing features and proxy capabilities to extract data from the web anonymously.
The second option is to buy pre-existing datasets, saving you time and effort. Again, Bright Data offers a wide range of datasets available for download.
Benefits of Using a Dataset
Below are three most important benefits of using datasets.
The information contained in datasets can be used to support strategic decisions. In particular, datasets allow you to spot market trends, analyze customer behavior, identify patterns and relationships in the data, and measure performance. You can then leverage datasets to make evidence-based, data-driven decisions, helping your company understand where to allocate resources, how to develop new products, and how much to charge for new services. As a result, your competitive edge and ability to respond to market needs will improve.
Better User Experience
Datasets containing user reviews can help you understand how to improve the overall customer experience. For example, you can use this information to create personalized experiences, improve product design, adapt or add new features, and optimize user journeys. By providing a better user experience, you’ll be increasing customer satisfaction.
Saving Time and Cost
You can use a dataset to uncover time and cost-saving opportunities. For example, datasets can help identify inefficiencies in the development process, allowing you to streamline operations, reduce waste and save time. Similarly, datasets can be explored to uncover redundant processes, business areas spending more than needed, and inefficiencies in the supply chain, helping lower your costs.
Dataset Use Cases
Let’s dig into some of the most popular use cases for datasets.
Datasets containing product prices from different eCommerce websites help you find the best deals, track competitors, and monitor changes in pricing. Unfortunately, extracting data from eCommerce sites is not easy. For example, Amazon consists of pages with different structures and has implemented several anti-scraping techniques, such as CAPTCHAs. Bright Data offers an Amazon dataset that gives you immediate access to tens of millions of products, sellers, and reviews. Also, Bright Data’s solution for eCommerce data analysis provides actionable insights for investors, retailers, global brands, and analysts.
Social Media Monitoring
Social media datasets include public data extracted from Facebook, Reddit, and other social platforms. These datasets are useful for gathering information about a target audience or studying user behavior, preferences, and engagement. Also, social media datasets are important for finding influencers to partner with, performing sentiment analysis, and monitoring brands. Buy Bright Data’s social media datasets to access tons of data collected from several social media platforms.
The process of recruiting new people is long and complex. Finding the right candidate can take months. The problem is that platforms like LinkedIn do not allow people to filter and explore their data freely. Datasets containing the interest data can be analyzed as you desire, making everything easier. Bright Data offers a LinkedIn dataset containing complete data from many publicly available profiles.
Let’s take a look at a simple example to understand what a dataset looks like. Here are the first few lines of
As you can see, the dataset contains data on the price and number of avocados sold daily in major U.S. cities. These records can help you monitor the price of avocados, which is usually strongly correlated with a country’s level of inflation.
In detail, the dataset contains CSV data organized in records with the following columns:
Date: The day on which the data was collected.
Average price in USD: The average price of a single avocado in a city in USD.
Total Sold: The total number of avocados sold in a city in one day.
Small Avocados Sold: The number of #4046 avocados sold in a city in one day.
Large Avocados Sold: The number of #4225 avocados sold in a city in one day.
Extra Large Avocados Sold: The number of #4770 avocados sold in a city in one day.
City: The city where the data was collected.
In this article, you saw the definition of datasets, an example of a CSV dataset, and the different types of datasets available. In detail, you learned what benefits datasets can provide in various use cases. Also, you had the opportunity to explore the most common approaches to generating a dataset. These include collecting data from the web or buying a dataset tailored to your needs. These are both services offered by Bright Data, the best dataset provider on the market!