Understanding of data types for machine learning and data science

Understanding of data types for machine learning and data science

Machine learning (a subfield of artificial intelligence) aims to program computers to learn and grow as people do. Machine learning can automate almost any activity that can be solved using a pattern or set of rules developed with data. It is critical to have a solid understanding of different data types to clean and pre-process data in preparation for use with ML algorithms. For machines to recognize patterns in data, they must first be translated into a digital representation. This will allow us to select high-performance models that can quickly and accurately identify key patterns. Knowledge of different data formats allows selecting the most appropriate processing and conversion methods. In addition, it will allow us to perform first-order visualizations and discover previously unknown information.

Why machine learning datasets are so important

Data analysis with machine learning algorithms can self-improve over time, but only if it is fed high quality input. Real understanding of machine learning requires familiarity with the data on which it is based. The importance of this information requires accurate and secure storage and handling. Understanding the different types of data involved in this activity is critical to applying appropriate methods and providing accurate results. I’d like to look at the different forms of data used in machine learning.

Numerical data / quantitative data

Quantitative or numerical data includes things like body measurements and monthly phone bills. If you try to average numbers or arrange them in ascending or descending order, you will know that the data is numeric. There are two types of numerical information: discrete and continuous.

In the case of discrete data, the information is represented by “integers,” that is, numbers without any decimal places.

In the case of continuous data, the values ​​are represented as whole integers (or their decimal representations).

Qualitative data / categorical data

Defining adjectives are used to classify data. Categorical data is the information that usually defines categories. Categorical data helps a machine learning model speed up data processing by categorizing people or concepts with similar characteristics. To further analyze qualitative information, we may divide it into two categories: nominal and ordinal.

Data that has no numerical or ordinal value is called nominal data. There is no discernible pattern to this data, which instead contains random numbers distributed over several categories.

Numbers in ordinal data are helpfully presented, like a normal order based on their position on a scale.

If you compare ordinal data with nominal data, you will see that the latter lacks any order, while the former does. Ordinal data can only be used to see sequences, and is therefore useless for statistical purposes. We cannot perform any calculations on this data, but it is useful for monitoring purposes such as measuring customer satisfaction, pleasure, etc.

text data

When training machine learning models, text input consists of anything from a single word to an entire article. It contains textual material made up of many words that make sense when taken together. Recognizing that each word can have many meanings and associations with other words, as well as understanding the larger context and connections between different words within a phrase, is the most important quality.

time series data

This data is presented as a list of time-stamped sequential data points. Dates and times are used as indexes in time series data. The vast majority of the time, this information is collected regularly. Having a strong grasp and understanding of how time series data is used makes it easy to compare information over different periods, such as weeks, months, or years.


Generally speaking, this means compiling information from many sources. Tabular information includes multiple columns or properties that represent a unique data type.

structured data

There are two possible formats for this information: numbers and words. The structured data type can be assigned numeric values, but it cannot be used in mathematical calculations. Data of this type is often presented in tabular form. A common place to keep them is in the relational database.

Unstructured data

Unstructured data refers to information that has to be carefully organized in a certain way. It includes words on the page, music, photos, movies, etc.

comma data

Interval data is ordered numeric data, with 0 indicating complete lack of any numerical value. In this context, zero does not denote emptiness but rather has some value. It’s a fairly small range. Temperature in degrees Celsius, time in hours and minutes, SAT scores, credit scores, pH levels, etc.

Ratio data

Similar to interval data, only with absolute zero, this quantitative data type can be used to store numbers. Here, zero indicates complete absence, and the scale starts at zero.

image data

Images contain important information that can only be extracted by analyzing their spatial aspects and connections. The common form of this information is image files of various formats. Pictures of all the food in the supermarket, pictures of all the students in the university, etc. are examples of picture data.

video data

Videos in different formats make this kind of information self-explanatory. One feature that sets video data apart is the need to calculate the links between frames in the video with respect to location, movement of objects/people, etc., to effectively extract information from the movies.

Here are some of the most widely used machine learning datasets available today:

  1. Search through Google datasets
  2. Microsoft’s R&D department released the data
  3. UCI’s machine learning dataset repository
  4. government datasets


Working with data is essential because knowing what kind of data it is and how to use it effectively is essential to obtaining valuable results. Research, analysis, statistics, data visualization, and data science use multiple forms of data. The Company may use this information to analyze business, develop strategy, and create a data-driven decision-making process. Data analysis and visualization benefits from knowing which plots work well with different datasets.

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Dhanshree Shenwai is a consulting content writer at MarktechPost. She is a computer science engineer and works as a delivery manager for a leading global bank. She is well experienced in financial technology companies covering the financial, cards, payments and banking field with a keen interest in AI applications. She is passionate about exploring new technologies and developments in today’s evolving world.

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