{"id":45853,"date":"2024-11-18T17:52:39","date_gmt":"2024-11-18T12:22:39","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/types-of-data\/"},"modified":"2024-11-18T17:52:39","modified_gmt":"2024-11-18T12:22:39","slug":"types-of-data","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/types-of-data\/","title":{"rendered":"Types Of Data - Nominal, Ordinal, Discrete and Continuous"},"content":{"rendered":"\n<p><em>There are multiple types of data used in statistics, data science, and machine learning, each requiring different analysis methods. While working on this data<\/em>, t<em>here are two types of data<\/em><span style=\"box-sizing: border-box; margin: 0px; padding: 0px;\"><em>:<\/em><\/span> \"Qualitative\" and \"Quantitative\" data, which are further classified into <em>four primary types:<\/em><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><em><strong>Nominal<\/strong><\/em><\/li>\n\n\n\n<li><em><strong>Ordinal<\/strong><\/em><\/li>\n\n\n\n<li><em><strong>Discrete<\/strong><\/em><\/li>\n\n\n\n<li><em><strong>Continuous<\/strong><\/em><\/li>\n<\/ul>\n\n\n\n<p><em>Advanced classification also includes\u00a0<strong>Interval and Ratio data<\/strong>, which determine what statistical operations are valid on your dataset.<\/em><\/p>\n\n\n\n<p>This data is so important for us that it becomes important to handle and store it properly, without any errors. For example, a <a href=\"https:\/\/www.aegissofttech.com\/data-warehousing-consulting.html\">Data Warehousing Consulting<\/a> firm is responsible for designing, implementing, and maintaining systems that ensure data is securely stored. So understanding these types of data becomes essential.<\/p>\n\n\n\n<p>With a staggering <a href=\"https:\/\/financesonline.com\/how-much-data-is-created-every-day\/\">2.5 quintillion<\/a> bytes of data generated daily, businesses rely heavily on data analytics for crucial insights. This data fuels campaign creation, <a href=\"https:\/\/www.mygreatlearning.com\/academy\/learn-for-free\/courses\/strategic-management#gl_blog_id=45853\">strategy designing<\/a>, product launches, and experimentation for better results.<\/p>\n\n\n\n    <div class=\"courses-cta-container\">\n        <div class=\"courses-cta-card\">\n            <div class=\"courses-cta-header\">\n                <div class=\"courses-learn-icon\"><\/div>\n                <span class=\"courses-learn-text\">Start Your Data Analytics Journey<\/span>\n            <\/div>\n            <p class=\"courses-cta-title\">\n                <a href=\"https:\/\/www.mygreatlearning.com\/online-data-analytics-essentials-program\" class=\"courses-cta-title-link\">Data Analytics Essentials<\/a>\n            <\/p>\n            <p class=\"courses-cta-description\">Learn the fundamentals of data analytics and start making smarter, data-driven decisions.<\/p>\n            <div class=\"courses-cta-stats\">\n                <div class=\"courses-stat-item\">\n                    <div class=\"courses-stat-icon courses-user-icon\"><\/div>\n                    <span>Duration: 3 months<\/span>\n                <\/div>\n                <div class=\"courses-stat-item\">\n                    <div class=\"courses-stat-icon courses-star-icon\"><\/div>\n                    <span>Ratings: 4.74<\/span>\n                <\/div>\n            <\/div>\n            <a href=\"https:\/\/www.mygreatlearning.com\/online-data-analytics-essentials-program\" class=\"courses-cta-button\">\n                Start Learning today\n                <div class=\"courses-arrow-icon\"><\/div>\n            <\/a>\n        <\/div>\n    <\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"what-are-the-different-types-of-data\"><strong>What are the different types of data?<\/strong><\/h2>\n\n\n\n<p>The two main types of data are:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Qualitative Data<\/li>\n\n\n\n<li>Quantitative Data<\/li>\n<\/ul>\n\n\n<figure class=\"wp-block-image size-full zoomable\" data-full=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2023\/06\/types-of-data-1024x555-1.png\"><img decoding=\"async\" width=\"1024\" height=\"555\" src=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2023\/06\/types-of-data-1024x555-1.png\" alt=\"Types of Data\" class=\"wp-image-90905\" srcset=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2023\/06\/types-of-data-1024x555-1.png 1024w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2023\/06\/types-of-data-1024x555-1-300x163.png 300w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2023\/06\/types-of-data-1024x555-1-768x416.png 768w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2023\/06\/types-of-data-1024x555-1-150x81.png 150w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"quick-reference-types-of-data-in-statistics\"><strong>Quick Reference: Types of Data in Statistics<\/strong><\/h3>\n\n\n\n<div style=\"overflow-x: auto; margin: 20px 0; border-radius: 8px; box-shadow: 0 4px 6px rgba(0,0,0,0.05);\">\n    <style>\n        .custom-wp-table-compact {\n            width: 100%;\n            border-collapse: collapse;\n            font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, \"Helvetica Neue\", Arial, sans-serif;\n            font-size: 13px; \/* Compact font size *\/\n            line-height: 1.4;\n            text-align: left;\n            background-color: #ffffff;\n            border-radius: 8px;\n            overflow: hidden;\n            white-space: nowrap; \/* Keeps columns neat for data tables *\/\n        }\n        .custom-wp-table-compact thead tr {\n            background-color: #0ea5e9; \/* Vibrant Sky Blue *\/\n            color: #ffffff;\n        }\n        .custom-wp-table-compact th {\n            padding: 8px 12px;\n            font-weight: 600;\n            letter-spacing: 0.3px;\n        }\n        .custom-wp-table-compact td {\n            padding: 6px 12px;\n            color: #334155;\n            border-bottom: 1px solid #e2e8f0;\n        }\n        \/* White Rows *\/\n        .custom-wp-table-compact tbody tr:nth-of-type(odd) {\n            background-color: #ffffff; \n        }\n        \/* Light Sky Blue Rows *\/\n        .custom-wp-table-compact tbody tr:nth-of-type(even) {\n            background-color: #f0f9ff; \n        }\n        \/* Hover Effect *\/\n        .custom-wp-table-compact tbody tr:hover {\n            background-color: #e0f2fe; \n            transition: background-color 0.2s ease-in-out;\n        }\n        \/* First Column Emphasis *\/\n        .attribute-name {\n            color: #0369a1; \/* Darker blue *\/\n            font-weight: 600;\n        }\n        \/* Check & Cross Colors *\/\n        .check-mark {\n            color: #16a34a; \/* Green *\/\n            font-weight: bold;\n        }\n        .cross-mark {\n            color: #dc2626; \/* Red *\/\n            font-weight: bold;\n        }\n    <\/style>\n\n    <table class=\"custom-wp-table-compact\">\n        <thead>\n            <tr>\n                <th>Attribute<\/th>\n                <th>Nominal<\/th>\n                <th>Ordinal<\/th>\n                <th>Interval<\/th>\n                <th>Ratio<\/th>\n                <th>Discrete<\/th>\n                <th>Continuous<\/th>\n            <\/tr>\n        <\/thead>\n        <tbody>\n            <tr>\n                <td class=\"attribute-name\">Has Order?<\/td>\n                <td class=\"cross-mark\">\u2717<\/td>\n                <td class=\"check-mark\">\u2713<\/td>\n                <td class=\"check-mark\">\u2713<\/td>\n                <td class=\"check-mark\">\u2713<\/td>\n                <td class=\"check-mark\">\u2713<\/td>\n                <td class=\"check-mark\">\u2713<\/td>\n            <\/tr>\n            <tr>\n                <td class=\"attribute-name\">Arithmetic Ops?<\/td>\n                <td class=\"cross-mark\">\u2717<\/td>\n                <td class=\"cross-mark\">\u2717<\/td>\n                <td>+ \/ \u2212<\/td>\n                <td>All<\/td>\n                <td>All (integers)<\/td>\n                <td>All<\/td>\n            <\/tr>\n            <tr>\n                <td class=\"attribute-name\">True Zero?<\/td>\n                <td>N\/A<\/td>\n                <td>N\/A<\/td>\n                <td class=\"cross-mark\">\u2717<\/td>\n                <td class=\"check-mark\">\u2713<\/td>\n                <td class=\"check-mark\">\u2713<\/td>\n                <td class=\"check-mark\">\u2713<\/td>\n            <\/tr>\n            <tr>\n                <td class=\"attribute-name\">Best Chart<\/td>\n                <td>Pie, bar<\/td>\n                <td>Bar, stacked<\/td>\n                <td>Line, histogram<\/td>\n                <td>Scatter, ratio<\/td>\n                <td>Bar, frequency<\/td>\n                <td>Histogram, line<\/td>\n            <\/tr>\n            <tr>\n                <td class=\"attribute-name\">Statistical Test<\/td>\n                <td>Chi-square<\/td>\n                <td>Mann-Whitney<\/td>\n                <td>t-test, ANOVA<\/td>\n                <td>t-test, regression<\/td>\n                <td>Poisson, Binomial<\/td>\n                <td>ANOVA, regression<\/td>\n            <\/tr>\n        <\/tbody>\n    <\/table>\n<\/div>\n\n\n\n<p>Now, let's understand this in detailFavorite holiday destination<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"1-qualitative-or-categorical-data\">1. <strong>Qualitative or Categorical Data<\/strong><\/h3>\n\n\n\n<p>Qualitative or Categorical Data is a type of data that can\u2019t be measured or counted in the form of numbers. <br><br>These types of data are sorted by category, not by number. That\u2019s why it is also known as Categorical Data. These data consist of audio, images, symbols, or text. The gender of a person, i.e., male, female, or others, is qualitative data.<\/p>\n\n\n\n<p>Qualitative data tells about the perception of people. This data helps market researchers understand the customers\u2019 tastes and then design their ideas and strategies accordingly.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"the-other-examples-of-qualitative-data-are\"><strong>The other examples of qualitative data are :<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What language do you speak<\/li>\n\n\n\n<li>Opinion on something (agree, disagree, or neutral)<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"the-qualitative-data-are-further-classified-into-two-parts\"><strong>The Qualitative data are further classified into two parts :<\/strong><\/h4>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"a-nominal-data\">A. <strong>Nominal Data<\/strong><\/h3>\n\n\n\n<p>Nominal Data is used to label variables without any order or quantitative value. The color of hair can be considered nominal data, as one color can\u2019t be compared with another color.<\/p>\n\n\n\n<p>The name \u201cnominal\u201d comes from the Latin name \u201cnomen,\u201d which means \u201cname.\u201d With the help of nominal data, we can\u2019t do any numerical tasks or can\u2019t give any order to sort the data.<\/p>\n\n\n\n<p><strong>Use Case in Data Science <\/strong><br>In machine learning, nominal data is encoded using\u00a0<strong>one-hot encoding<\/strong>\u00a0(e.g., converting product categories like [Electronics, Clothing, Food] into binary columns) before feeding it into models like Decision Trees or Logistic Regression. Always remember that for nominal data, only the\u00a0<strong>mode<\/strong>\u00a0is used.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"examples-of-nominal-data\"><strong>Examples of Nominal Data :<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Color of hair (Blonde, red, Brown, Black, etc.)<\/li>\n\n\n\n<li>Marital status (Single, Widowed, Married)<\/li>\n\n\n\n<li>Nationality (Indian, German, American)<\/li>\n\n\n\n<li>Gender (Male, Female, Others)<\/li>\n\n\n\n<li>Eye Color (Black, Brown, etc.)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"b-ordinal-data\">B. <strong>Ordinal Data <\/strong><\/h3>\n\n\n\n<p>Ordinal data have natural ordering where a number is present in some kind of order by their position on the scale. These data are used for observation like customer satisfaction, happiness, etc., but we can\u2019t do any arithmetical tasks on them.&nbsp;<\/p>\n\n\n\n<p>Ordinal data is qualitative data for which their values have some kind of relative position. These kinds of data can be considered \u201cin-between\u201d qualitative and quantitative data. <br><br>The ordinal data only shows the sequences and cannot use for <a href=\"https:\/\/www.mygreatlearning.com\/blog\/what-is-statistical-analysis\/\">statistical analysis<\/a>. Compared to nominal data, ordinal data have some kind of order that is not present in nominal data.&nbsp;&nbsp;<\/p>\n\n\n\n<p><strong>Use Case in Data Science <\/strong><br>In Natural Language Processing (NLP), sentiment analysis models often produce ordinal output, Positive > Neutral > Negative. This ordinal nature means you can rank results, but can't assume the gap between Positive and Neutral is equal to the gap between Neutral and Negative. For ordinal data, both\u00a0<strong>mode and median<\/strong>\u00a0are valid, but not the <strong>mean<\/strong>.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"examples-of-ordinal-data\"><strong>Examples of Ordinal Data :<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>When companies ask for feedback, experience, or satisfaction on a scale of 1 to 10<\/li>\n\n\n\n<li>Letter grades in the exam (A, B, C, D, etc.)<\/li>\n\n\n\n<li>Ranking of people in a competition (First, Second, Third, etc.)<\/li>\n\n\n\n<li>Economic Status (High, Medium, and Low)<\/li>\n\n\n\n<li>Education Level (Higher, Secondary, Primary)<\/li>\n<\/ul>\n\n\n\n<p><strong>Must Read:<\/strong> '<a href=\"https:\/\/www.mygreatlearning.com\/blog\/what-is-data-science\/\">What is Data Science? \u2013 The Complete Guid<\/a>e' to transform data into valuable insights by understanding the types of data in data science<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"difference-between-nominal-and-ordinal-data\"><strong>Difference between Nominal and Ordinal Data<\/strong><\/h3>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table><tbody><tr><td><strong>Nominal Data<\/strong><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong>Ordinal Data<\/strong><\/td><\/tr><tr><td>Nominal data can\u2019t be quantified, neither they have any intrinsic ordering<\/td><td class=\"has-text-align-left\" data-align=\"left\">Ordinal data gives some kind of sequential order by their position on the scale<\/td><\/tr><tr><td>Nominal data is qualitative data or categorical data<\/td><td class=\"has-text-align-left\" data-align=\"left\">Ordinal data is said to be \u201cin-between\u201d qualitative data and quantitative data<\/td><\/tr><tr><td>They don\u2019t provide any quantitative value, neither can we perform any arithmetical operation<\/td><td class=\"has-text-align-left\" data-align=\"left\">They provide sequence and can assign numbers to ordinal data but cannot perform the arithmetical operation<\/td><\/tr><tr><td>Nominal data cannot be used to compare with one another<\/td><td class=\"has-text-align-left\" data-align=\"left\">Ordinal data can help to compare one item with another by ranking or ordering<\/td><\/tr><tr><td><strong>Examples:<\/strong> Eye color, housing style, gender, hair color, religion, marital status, ethnicity, etc<\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong>Examples:<\/strong> Economic status, customer satisfaction, education level, letter grades, etc&nbsp;<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Also read \"<a href=\"https:\/\/www.mygreatlearning.com\/blog\/what-is-data-analytics\/\">What is Data Analytics<\/a>\" to grasp the fundamentals of extracting value from data.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"c-interval-data\"><strong>C. Interval Data<\/strong><\/h3>\n\n\n\n<p>Numerical scale with equal intervals between values but\u00a0<em>no true zero<\/em>. You can add\/subtract but not multiply\/divide meaningfully.<br>\u2022 <strong>Examples<\/strong>: Temperature in Celsius\/Fahrenheit, IQ scores, calendar years, Likert scale ratings (treated as interval in research)<br>\u2022 Cannot say \"80\u00b0C is twice as hot as 40\u00b0C\" (no true zero)<br>\u2022 Statistical tests: t-tests, ANOVA, Pearson correlation<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"2-quantitative-data\">2. <strong>Quantitative Data<\/strong><\/h3>\n\n\n\n<p>Quantitative data is a type of data that can be expressed in numerical values, making it countable and including statistical data analysis. These kinds of data are also known as Numerical data. <br><br>It answers questions like \u201chow much,\u201d \u201chow many,\u201d and \u201chow often.\u201d For example, the price of a phone, the computer\u2019s RAM, the height or weight of a person, etc., fall under quantitative data.\u00a0<\/p>\n\n\n\n<p>Quantitative data can be used for statistical manipulation. These data can be represented on a wide variety of graphs and charts, such as bar graphs, histograms, scatter plots, boxplots, pie charts, line graphs, etc.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"examples-of-quantitative-data\"><strong>Examples of Quantitative Data :<\/strong>&nbsp;<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Height or weight of a person or object<\/li>\n\n\n\n<li>Room Temperature<\/li>\n\n\n\n<li>Scores and Marks (Ex, 59, 80, 60, etc.)<\/li>\n\n\n\n<li>Time<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"the-quantitative-data-are-further-classified-into-two-parts\"><strong>The Quantitative&nbsp;data are further classified into two parts :<\/strong><\/h4>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"a-discrete-data\">A. <strong>Discrete Data<\/strong><\/h3>\n\n\n\n<p>The term discrete means distinct or separate. The discrete data contain the values that fall under integers or whole numbers. The total number of students in a class is an example of discrete data. These data can\u2019t be broken into decimal or fraction values.<\/p>\n\n\n\n<p>The discrete data are countable and have finite values; their subdivision is not possible. These data are represented mainly by a bar graph, number line, or frequency table.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"examples-of-discrete-data\"><strong>Examples of Discrete Data :<\/strong>&nbsp;<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The number of customers who visited a store today is discrete (you can't have 5.7 customers), while the time each customer spent is continuous (4 minutes 32.6 seconds is valid).<\/li>\n\n\n\n<li>The total number of students present in a class<\/li>\n\n\n\n<li>Cost of a cell phone<\/li>\n\n\n\n<li>Number of employees in a company<\/li>\n\n\n\n<li>The total number of players who participated in a competition<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"b-continuous-data\">B. <strong>Continuous Data<\/strong><\/h3>\n\n\n\n<p>Continuous data are in the form of fractional numbers. It can be the version of an android phone, the height of a person, the length of an object, etc. Continuous data represents information that can be divided into smaller levels. The continuous variable can take any value within a range.&nbsp;<\/p>\n\n\n\n<p>The key difference between discrete and continuous data is that discrete data contains the integer or whole number. Still, continuous data stores the fractional numbers to record different types of data such as temperature, height, width, time, speed, etc.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"examples-of-continuous-data\"><strong>Examples of Continuous Data :<\/strong>&nbsp;<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Height of a person<\/li>\n\n\n\n<li>Speed of a vehicle<\/li>\n\n\n\n<li>\u201cTime taken\u201d to finish the work\u00a0<\/li>\n\n\n\n<li>Market share price<\/li>\n<\/ul>\n\n\n\n<p>Discover the must-reads for data enthusiasts: \"<a href=\"https:\/\/www.mygreatlearning.com\/blog\/data-science-books\/\">Top 25 Data Science Books<\/a>\"<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"c-ratio-data\"><strong>C. Ratio Data<\/strong><\/h3>\n\n\n\n<p>Like interval data but\u00a0<em>with a true zero<\/em>, enabling ratios.<br>\u2022 <strong>Examples<\/strong>: Height (cm), weight (kg), income (\u20b9), age, time duration, number of sales<br>\u2022 Can say \"\u20b980,000 salary is twice \u20b940,000\"<br>\u2022 <strong>Statistical tests<\/strong>: All parametric tests; coefficient of variation<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"difference-between-discrete-and-continuous-data\"><strong>Difference between Discrete and Continuous Data<\/strong><\/h3>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table><thead><tr><th><strong>Discrete Data<\/strong><\/th><th><strong>Continuous Data<\/strong><\/th><\/tr><\/thead><tbody><tr><td>Discrete data are countable and finite; they are whole numbers or integers<\/td><td>Continuous data are measurable; they are in the form of fractions or decimal<\/td><\/tr><tr><td>Discrete data are represented mainly by bar graphs<\/td><td>Continuous data are represented in the form of a histogram<\/td><\/tr><tr><td>The values cannot be divided into subdivisions into smaller pieces<\/td><td>The values can be divided into subdivisions into smaller pieces<\/td><\/tr><tr><td>Discrete data have spaces between the values<\/td><td>Continuous data are in the form of a continuous sequence<\/td><\/tr><tr><td><strong>Examples:<\/strong> Number of defective units in a batch (manufacturing), number of support tickets per day (SaaS), number of goals in a football match etc.<\/td><td><strong>Example:<\/strong> Blood pressure reading, stock price at a point in time, CPU temperature in a server.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Discrete data is typically visualized with a\u00a0<strong>bar chart<\/strong>\u00a0(gaps between bars show the data is countable). Continuous data uses a\u00a0<strong>histogram<\/strong>\u00a0(no gaps, showing a continuous range). This difference matters when choosing chart types in tools like Tableau, Power BI, or Python's Matplotlib<\/p>\n\n\n\n<p>Don't just follow trends, understand them! Check out our blog post on the <a href=\"https:\/\/www.mygreatlearning.com\/blog\/latest-data-science-trends\/\">Latest Data Science Trends<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"types-of-data-by-structure-structured-unstructured-and-semi-structured\"><strong>Types of Data by Structure: Structured, Unstructured, and Semi-Structured<\/strong><\/h2>\n\n\n\n<p>\u2022\u00a0<strong>Structured Data:<\/strong>\u00a0Organized in rows\/columns (SQL tables, Excel sheets). Easiest for ML and analysis. 20% of enterprise data.<br><span style=\"box-sizing: border-box; margin: 0px; padding: 0px;\">\u2022\u00a0<strong>Unstructured Data:<\/strong>\u00a0No predefined format, text, images, audio, or<\/span> video. Accounts for 80% of enterprise data (IBM, 2023). Requires NLP, CV, or deep learning to process.<br><span style=\"box-sizing: border-box; margin: 0px; padding: 0px;\">\u2022\u00a0<strong>Semi-structured Data:<\/strong>\u00a0Has tags or markers but no strict schema, J<\/span>SON, XML, emails, or NoSQL databases.<\/p>\n\n\n\n<p>For a more detailed understanding, check out- <a href=\"https:\/\/www.mygreatlearning.com\/blog\/structured-and-unstructured-data\/\">Structured and Unstructured Data: Definitions and Differences<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"conclusion\">Conclusion<\/h2>\n\n\n\n<p>We've explored what are the different types of data and their importance in analysis and decision-making. Understanding these data types is key to using them effectively for research, <a href=\"https:\/\/www.mygreatlearning.com\/academy\/learn-for-free\/courses\/statistical-analysis\">statistical analysis<\/a>, <a href=\"https:\/\/www.mygreatlearning.com\/academy\/learn-for-free\/courses\/data-visualization-with-power-bi\">data visualization<\/a>, and extracting meaningful insights. <br><br>If you're interested in learning these concepts in more detail and want to pursue a career in data science, check out Great Learning's <a href=\"https:\/\/www.mygreatlearning.com\/academy\/learn-for-free\/courses\/data-science-foundations\">Free Data Science Foundations Course<\/a>. <br><br>It's designed to provide you with essential skills and knowledge to kickstart your journey in this field. Additionally, our <a href=\"https:\/\/www.mygreatlearning.com\/data-science\/courses\">Data Science Course<\/a> offers advanced training for those looking to dive deeper into the world of data science. <br><br>Visit our website to find the right course for you and take the first step towards a rewarding career in data science. Explore <strong><a href=\"https:\/\/www.mygreatlearning.com\/academy\" target=\"_blank\" rel=\"noreferrer noopener\">free courses with certificate<\/a><\/strong> to gain valuable skills, enhance your resume, and kickstart your journey in the ever-growing field of data science.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"faqs\"><strong>FAQs<\/strong><\/h2>\n\n\n\n<div class=\"schema-faq wp-block-yoast-faq-block\"><div class=\"schema-faq-section\" id=\"faq-question-1715144140864\"><strong class=\"schema-faq-question\">Can ordinal data be converted into nominal data for analysis purposes?<\/strong> <p class=\"schema-faq-answer\">Yes, in certain scenarios, ordinal data can be transformed into nominal data for specific analyses. For instance, if analyzing customer satisfaction levels on a scale of \"very dissatisfied\" to \"very satisfied,\" these ordinal rankings can be converted into nominal categories such as \"low,\" \"medium,\" and \"high\" satisfaction.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1715144165019\"><strong class=\"schema-faq-question\">Can data types influence the choice of machine learning algorithms?<\/strong> <p class=\"schema-faq-answer\">Yes, data types play a significant role in selecting the appropriate machine learning algorithm. For instance, decision trees are well-suited for handling both nominal and ordinal data, while algorithms like linear regression are more suitable for continuous data.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1715144180484\"><strong class=\"schema-faq-question\">How does the interpretation of ordinal data differ between qualitative and quantitative studies?<\/strong> <p class=\"schema-faq-answer\">In qualitative studies, ordinal data may be interpreted based on the relative order or ranking of categories, providing insights into preferences or perceptions. In quantitative studies, ordinal data may be treated as numerical values for statistical analysis, enabling comparisons and correlations.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1715144200437\"><strong class=\"schema-faq-question\">Can data types influence the choice of data preprocessing techniques?<\/strong> <p class=\"schema-faq-answer\">Absolutely! Data preprocessing techniques such as encoding, scaling, or imputation can vary based on the data type. For instance, nominal data may require one-hot encoding, while continuous data might benefit from normalization or standardization.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1715144221326\"><strong class=\"schema-faq-question\">Can you explain how missing data handling differs for nominal and continuous variables?<\/strong> <p class=\"schema-faq-answer\">Handling missing data differs for nominal and continuous variables. While nominal data may involve simple techniques like mode imputation (replacing missing values with the most frequent category), continuous data may require advance methods such as mean or median imputation or predictive modelling approaches<\/p> <\/div> <\/div>\n","protected":false},"excerpt":{"rendered":"<p>There are multiple types of data used in statistics, data science, and machine learning, each requiring different analysis methods. While working on this data, there are two types of data: \"Qualitative\" and \"Quantitative\" data, which are further classified into four primary types: Advanced classification also includes\u00a0Interval and Ratio data, which determine what statistical operations are [&hellip;]<\/p>\n","protected":false},"author":41,"featured_media":82848,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[9],"tags":[36804],"content_type":[36248],"class_list":["post-45853","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science","tag-data-analytics","content_type-career-guide"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - 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For instance, if analyzing customer satisfaction levels on a scale of \"very dissatisfied\" to \"very satisfied,\" these ordinal rankings can be converted into nominal categories such as \"low,\" \"medium,\" and \"high\" satisfaction.","inLanguage":"en-US"},"inLanguage":"en-US"},{"@type":"Question","@id":"https:\/\/www.mygreatlearning.com\/blog\/types-of-data\/#faq-question-1715144165019","position":2,"url":"https:\/\/www.mygreatlearning.com\/blog\/types-of-data\/#faq-question-1715144165019","name":"Can data types influence the choice of machine learning algorithms?","answerCount":1,"acceptedAnswer":{"@type":"Answer","text":"Yes, data types play a significant role in selecting the appropriate machine learning algorithm. For instance, decision trees are well-suited for handling both nominal and ordinal data, while algorithms like linear regression are more suitable for continuous data.","inLanguage":"en-US"},"inLanguage":"en-US"},{"@type":"Question","@id":"https:\/\/www.mygreatlearning.com\/blog\/types-of-data\/#faq-question-1715144180484","position":3,"url":"https:\/\/www.mygreatlearning.com\/blog\/types-of-data\/#faq-question-1715144180484","name":"How does the interpretation of ordinal data differ between qualitative and quantitative studies?","answerCount":1,"acceptedAnswer":{"@type":"Answer","text":"In qualitative studies, ordinal data may be interpreted based on the relative order or ranking of categories, providing insights into preferences or perceptions. 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For instance, nominal data may require one-hot encoding, while continuous data might benefit from normalization or standardization.","inLanguage":"en-US"},"inLanguage":"en-US"},{"@type":"Question","@id":"https:\/\/www.mygreatlearning.com\/blog\/types-of-data\/#faq-question-1715144221326","position":5,"url":"https:\/\/www.mygreatlearning.com\/blog\/types-of-data\/#faq-question-1715144221326","name":"Can you explain how missing data handling differs for nominal and continuous variables?","answerCount":1,"acceptedAnswer":{"@type":"Answer","text":"Handling missing data differs for nominal and continuous variables. 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