Gaining key insights and analyzing data is one of the most prioritized tasks in businesses, organizations, and science. It can be used to understand the repercussions of their decisions by going through the accumulated data and searching for patterns. Since data is so vital, it is only logical to have it stored in an organized form. This is where datasets enter the picture. 

In the simplest words, a dataset is a collection of information on a specific topic, mostly in tabular form. It is the foundation of data analytics, data sciences and machine learning. 

A real-world scenario is the medical records stored in the computer system of a hospital. A person’s medical history is a dataset. 

Datasets can be divided into two categories – structured and unstructured. Regarding the example of the hospital records again, an individual’s demographic information, procedures, past diagnosis, medications, allergies, family history, and immunizations are examples of structured data. 

At the same time, handwritten notes or transcripts, genomic data, medical and diagnostic images, and clinical reports are the unstructured datasets. The contrast between the two is clear, the structured dataset is formatted in rows and columns, easily accessible, more manageable, and lends itself well to quantitative and statistical analysis. Whereas unstructured dataset are in the form of pictures or text, are harder to manage, and need to be analyzed using advanced approaches before accessibility. 

Datasets are mostly used by businesses, organizations, and governments to make informed decisions and in fields like machine learning to train algorithms. Numerical data is such a huge part of any business or organization that making sound decisions based on it leads to growth. For example, a data scientist working for a business can look for the preferred product in their inventory. Similarly, the product can be associated with the age group of the customers so the business can focus on innovating related products for this specific group of people which will reduce the risk of unsuccessful launch. This is just one of the several approaches to the use of datasets.

Dataset Types

There exist various types of datasets, including but not limited to:

• Numerical datasets

This is what we also refer to as quantitative data. It is expressed in the form of numbers, measurements, and counts.

For example, a dataset having demographic information like age, income, etc.

• Partitioned datasets

A dataset is split into multiple partitions based on dimensions. This partitioning can be based on various relevant factors like time frame, geographic region etc.

An example is a dataset of stock prices partitioned into monthly subsets for easier analysis.

• Image datasets

The data in this dataset is in the form of digital images. Image recognition, object detection, image segmentation and image classification are some of the applications of the image dataset.

• Categorial datasets

Also known as qualitative data, it stands for the characteristics of an object. These attributes are normally non-numeric and hold labels, categories, or descriptions.

Survey responses with multiple-choice questions are an example of categorial datasets.

• Bivariate datasets

Two types of related datasets are present in a bivariate dataset. These datasets are typically used to analyze the relationship between the two variables and their interactions with each other.

Height and weight, price and demand, temperature and ice cream sales etc. are all examples of bivariate datasets.

• Multivariate datasets

Multiple types of related datasets are present in a multivariate dataset. More than two variables and their interaction with one another are analyzed and studied. 

Demographic information, financial data etc. are the representatives of multivariate datasets.

Dataset Sources

There is a platitude of datasets available on the internet on many different topics. Websites like the two mentioned below provide many different datasets for analysis.

• Kaggle

Kaggle is one of the most resourceful platforms of datasets for data scientists, and analysts. Datasets are available on a wide range of topics of distinct domains. Users can search for datasets based on different criteria such as topic, popularity, or date added. These datasets can be downloaded in different formats like CSV, JSON, Excel spreadsheets and more, according to need.

• UCI Machine Learning Repository

The UCI Machine Learning repository was founded by the University of California, Irvine. It is a huge repository of datasets with an abundant range of domains and topics. This repository also provides tools for the analysis of data and usually its datasets come with metadata so the users can understand the characteristics of the dataset.

The most common formats to store datasets are CSV and Excel. Although JSON is also used to store and transport datasets.

Conclusion

Datasets are the backbone of data-driven decision-making. They serve as repositories of valuable information across various fields. From structured numerical data to unstructured images and text, datasets come in different forms and play a crucial role for data analysts and researchers so they can extract insights and patterns. Whether it’s analyzing medical records in a hospital or predicting stock prices in the financial market, datasets enable organizations and individuals to make informed choices and drive innovation.