A dataset refers to a collection of data that has been collected and organized for the purpose of analysis, machine learning, research, and other similar activities. A dataset usually consists of a collection of records or samples, and each record contains a specific set of attributes or variables defined by the researcher or organization that created the dataset.

Datasets can be collected from various sources such as databases, text files, special formats such as CSV (Comma-Separated Values) and JSON (JavaScript Object Notation), and even from sensors and devices related to the Internet of Things (IoT). Datasets are usually used to train machine learning algorithms, and by analyzing the data, they extract useful patterns and information to perform specific tasks such as prediction, decision making, or inference.

What are the types of datasets?

Types of datasets can be divided based on a set of different features and characteristics. Among the common types of datasets, the following should be mentioned:

Numeric Dataset

Numeric Dataset contains a set of numbers that are used for computational problems and numerical analysis. In this type of dataset, each sample or record usually contains a series of numbers that represent different characteristics. These numbers can be continuous values ​​(such as real numbers or integers) or discrete values ​​(such as counting products or categories). For example, suppose that an e-commerce company has a numeric dataset that contains information about customer orders. Each record in this dataset may include features such as order amount, number of products purchased, discount received, order date and time, and other features related to the order. This data can be real numbers (eg $120.50) or integers (eg 5 products).

The use of numerical datasets in data analysis and related issues provide us with brilliant advantages. Using computational and analytical algorithms, patterns, relationships and useful information can be identified in these datasets. For example, by using the numerical dataset of customer orders, it is possible to recognize customer purchasing patterns, predict purchasing behavior, investigate the effect of discounts on the order amount, and make strategic decisions based on data.

In general, numerical datasets provide an infrastructure to perform data-based calculations, analysis, inferences, and predictions, and are widely used in many fields such as engineering science, natural science, social science, and finance.

Categorical Dataset

Categorical dataset contains qualitative or categorical variables that are used as labels or categories for samples. In this type of dataset, features are defined as specific and limited categories and their values ​​belong to a finite set of categories or labels. For example, suppose an insurance company has a categorical dataset that contains information about customers' car insurance policies. Each record in this dataset may include attributes such as vehicle type and features, year of manufacture, traffic area being driven (urban, suburban) and driving history (no accident, low accident, high accident). These features are defined as categories for each sample, and their values ​​belong to a set of limited categories.

The use of classification dataset in data analysis and related issues provides possibilities. By using categorization algorithms and category analysis, it is possible to identify patterns, relationships, and common features among categories. For example, by using the car insurance classification dataset, it is possible to examine the behavioral patterns between car types, the influence of the year of manufacture on the insurance price, the relationship between traffic area and driving history, and help strategic decisions in the field of car insurance. Classification datasets are widely used in various fields such as social sciences, marketing, text classification, pattern recognition, and data-based decision making. These datasets allow us to identify and understand patterns and common features between categories.

Time-Series Dataset

Time-Series Dataset contains a set of data specified in chronological order. In this type of dataset, each sample or record has a numerical value or set of numbers at a given time. This data may be collected regularly or irregularly over time.

For example, suppose a financial company has a time dataset that contains daily stock price information. Each record in this dataset may contain attributes such as date, market price, trading volume, and other information related to the security on that day. This data is dated and we can examine the patterns, changes and trends of security prices over time.

The use of temporal dataset provides possibilities in data analysis and related issues. These datasets allow us to observe temporal changes in a data series, identify various patterns, seasonalities, and trends, and make time-based predictions. For example, using a stock market price temporal dataset, we can detect daily, weekly, or seasonal patterns, analyze sudden changes and special events, and use forecasting models to estimate future prices.

Temporal datasets are used in various fields such as weather and meteorology, finance, traffic, health, etc. These datasets allow us to identify trends and patterns that repeat over time and make data-driven analysis and decisions based on them.

Spatial Dataset

Spatial Dataset includes a set of data that includes spatial information. This type of dataset shows the spatial relationship between data and usually includes information such as longitude and latitude for each sample.

For example, suppose a travel company has a location dataset that contains the location information of places of interest in a city. Each record in this dataset may contain attributes such as place name, latitude and longitude, type of place (eg park, museum, restaurant, etc.) and other information related to that place. Using this spatial dataset, we can view landmarks on a map, analyze spatial patterns and distribution, and exploit spatial information for issues related to travel, housing, and geography. Using these datasets, we can identify spatial patterns, geographic distribution, and spatial relationships. In addition, we can study spatial distribution prediction and other spatial issues by using spatial analysis such as spatial clustering analysis. Spatial datasets are used in various fields such as geography, environment, business and transportation. These datasets allow us to understand spatial patterns and distributions and use them for place-related decisions and planning.

Image-based datasets

Image-based datasets include collections of digital images that are used as input data in image analysis and processing. These datasets may contain 2D images in different formats such as JPEG or PNG.

In image-based datasets, each image is considered as a sample and can contain multi-channel information such as color, light intensity, depth, etc. Also, each image can have different sizes and dimensions and is usually formed using pixels (pixels). These datasets are used in machine vision, pattern recognition, object recognition, face analysis, text recognition, robotics and many other artificial intelligence applications. For example, in machine vision, image-based datasets allow us to identify patterns and image features and use them for image classification, object recognition, face recognition, and other related tasks.

To use image-based datasets, there is usually a need for image preprocessing methods such as resizing, feature extraction, and data normalization.

Ordered datasets

Ordered datasets include sets of data that have a specific order between their elements. In these datasets, the semantic and related order of the data elements is important and usually the data is used as a time series or sequence.

Sequential dataset elements can be numeric, textual, time variables or any other data type. Some common examples of ordinal datasets include time series of atmospheric observations, data related to the trajectory of an object, data related to the sales history of a product, etc. The use of sequential datasets in data analysis and planning is usually done in order to extract patterns, predict events, analyze trends and identify common behaviors. By using various analysis such as temporal analysis, temporal prediction models, contextual inference methods and other related methods, sequential datasets can be used to derive useful information and provide better decisions.

For example, in the investment field, sequential datasets of a company's stock price over time can help us analyze price patterns, predict market trends, and make better investment decisions.

Partitioned datasets

Partitioned datasets include sets of data that are meaningfully divided into separate parts. In these datasets, the data is divided into different groups or segments based on a certain criterion, such as features, tags, time, or any other criterion. Partitioned datasets allow us to put related data in one section and perform various operations and analyzes independently on each section. This segmentation is done randomly, similar documents, time or any other criterion that differentiates and separates the data.

Using segmented datasets is very useful in data analysis and machine learning. By dividing the dataset into separate sections, we can analyze the patterns and common features in each section and apply different models to each section. This can significantly improve the accuracy and efficiency of machine learning models and algorithms. For example, in examining the effectiveness of a treatment method, the dataset can be divided into two experimental and control sections, and then the treatment method is applied to the experimental section and the results are compared with the control section. This method allows us to examine the effect of the treatment method without interfering with other possible factors in the dataset and evaluate the results reliably.

Bivariate datasets

Bivariate datasets include sets of data that are based on two variables or different characteristics, and the relationship between these two variables is investigated. In these datasets, each data contains two values ​​for two different characteristics, and the relationship and changes between these two variables are analyzed. Bivariate datasets can be displayed as pairs of data, so that each pair of data includes the value of the two variables under consideration. These variables can generally be of any data type, such as numeric, categorical, binary, etc.

Bivariate datasets can help us to investigate the relationship and interaction between two variables and to identify common patterns and mechanisms. Using statistical analysis and data mining, we can analyze the relationships between data quantitatively and qualitatively. This analysis can include calculation of correlation coefficient, regression, analysis of differences between groups and other methods used in bivariate analysis.

For example, in a bivariate dataset, we can examine the relationship between age and income. By analyzing the data, we can see if there is a relationship between age and income and how these two variables interact. This information can help with decisions related to marketing, demographic analysis and other areas of communication.

Multivariate datasets

Multivariate datasets include sets of data based on more than two variables or features. In these datasets, each data contains values ​​for several different variables, and relationships and patterns between these variables are examined.

Multivariate datasets are actually a data matrix that displays a row for each data and a column for each attribute or variable. These variables can be of any data type, such as numeric, categorical, binary, etc. By analyzing these data, we are able to examine and analyze patterns, interactions and relationships between variables.

Multivariate datasets allow us to simultaneously analyze multiple variables and explore complex relationships and interactions between them. This analysis may include calculation of mean, variance, correlation, factor analysis, dimension reduction and other methods used in multivariate analysis.

For example, in a multivariate dataset, we can examine the relationship between age, income, and education level. By analyzing the data, we can see if there is a relationship between these three variables and how these variables interact. This information can be used in social impact analysis, predictive modeling and other fields related to multivariate data.

The presented classifications are only a few examples of dataset types, although in practice it is possible to have a combination of these classifications and other features. Also, in many cases, datasets can contain a combination of different types of data, such as datasets that contain both numerical and image data.