{"id":259740,"date":"2024-02-21T06:31:03","date_gmt":"2024-02-21T06:31:03","guid":{"rendered":"https:\/\/imarticus.org\/blog\/?p=259740"},"modified":"2024-07-26T11:54:34","modified_gmt":"2024-07-26T11:54:34","slug":"data-visualisation-techniques-and-best-practices","status":"publish","type":"post","link":"https:\/\/imarticus.org\/blog\/data-visualisation-techniques-and-best-practices\/","title":{"rendered":"Data Visualisation Techniques and Best Practices"},"content":{"rendered":"<h2 dir=\"ltr\" data-node-text-align=\"justify\" data-pm-slice=\"1 1 []\"><span data-text-color-mark=\"rgb(14, 16, 26)\">What is data visualization?<\/span><\/h2>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Data visualisation is the art of representing data through visual elements. These <\/span><strong><span data-text-color-mark=\"rgb(14, 16, 26)\">data visualisation techniques<\/span><\/strong><span data-text-color-mark=\"rgb(14, 16, 26)\"> include charts, graphs, maps, and much more. In today&#8217;s data-driven world, where information is overloaded, data visualisation is a game-changer.\u00a0<\/span><\/p>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">For instance, you have to analyse the sales data for a retail company. Traditionally, you will study a massive table with numbers. But <\/span><strong><span data-text-color-mark=\"rgb(14, 16, 26)\">data visualisation tools<\/span><\/strong><span data-text-color-mark=\"rgb(14, 16, 26)\"> enable you to represent the same data through appealing bar charts, graphs etc. <\/span><strong><span data-text-color-mark=\"rgb(14, 16, 26)\">Data visualisation techniques<\/span><\/strong><span data-text-color-mark=\"rgb(14, 16, 26)\"> help to see beyond raw numbers and assist in &#8211;<\/span><\/p>\n<ul dir=\"ltr\">\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Identifying patterns and trends<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Areas of growth or decline,<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Highlights potential risks and opportunities,<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Make informed choices,\u00a0<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Uncover hidden patterns, correlations, etc.<\/span><\/p>\n<\/li>\n<\/ul>\n<h2 dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Understanding data visualisation techniques<\/span><\/h2>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">There are different <\/span><a href=\"https:\/\/imarticus.org\/blog\/knowledge-center-analytics-top-10-data-visualisation-tools\/\"><strong><span data-text-color-mark=\"rgb(14, 16, 26)\">data visualisation tools <\/span><\/strong><\/a><span data-text-color-mark=\"rgb(14, 16, 26)\">and <\/span><strong><span data-text-color-mark=\"rgb(14, 16, 26)\">data visualisation techniques<\/span><\/strong><span data-text-color-mark=\"rgb(14, 16, 26)\"> for data visualisation. Some of the top <\/span><strong><span data-text-color-mark=\"rgb(14, 16, 26)\">data visualisation techniques<\/span><\/strong><span data-text-color-mark=\"rgb(14, 16, 26)\"> are:<\/span><\/p>\n<h3 dir=\"ltr\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Bar charts<\/span><\/h3>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Perfect for comparing categorical data and showing frequency or distribution. For instance, the sales performance of different products. Here, each bar represents a product&#8217;s revenue.<\/span><\/p>\n<h3 dir=\"ltr\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Line Graphs<\/span><\/h3>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Great for displaying trends and patterns over time. For instance, stock market trends throughout the year.<\/span><\/p>\n<h3 dir=\"ltr\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Pie Charts<\/span><\/h3>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Ideal for illustrating parts of a whole or percentages. For example, to represent the market share of different smartphone brands in a city.<\/span><\/p>\n<h3 dir=\"ltr\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Scatter Plots<\/span><\/h3>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Excellent for visualising the relationship between two continuous variables. For example, the connection between advertising expenditure and sales.<\/span><\/p>\n<h3 dir=\"ltr\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Heatmaps<\/span><\/h3>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Effective for displaying the density or size of values across a grid. For instance, population density across different districts of a city.<\/span><\/p>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Some other <\/span><strong><span data-text-color-mark=\"rgb(14, 16, 26)\">data visualisation techniques<\/span><\/strong><span data-text-color-mark=\"rgb(14, 16, 26)\"> include:<\/span><\/p>\n<ul dir=\"ltr\">\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Line Chart<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Histogram<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Box Plot<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Area Chart<\/span><\/p>\n<\/li>\n<\/ul>\n<h2 dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Best practices for data visualisation <\/span><\/h2>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">The following are the best practices for effective <a href=\"https:\/\/imarticus.org\/blog\/visualisation-of-multivariate-data\/\">data visualisation<\/a> &#8211;<\/span><\/p>\n<h3 dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Preparing data for visualisation<\/span><\/h3>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Preparing data for visualisation ensures data accuracy and integrity. It allows the users to draw reliable insights. Preparing data helps in:<\/span><\/p>\n<ul dir=\"ltr\">\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\"><a href=\"https:\/\/www.tableau.com\/learn\/articles\/what-is-data-cleaning#:~:text=Data%20cleaning%20is%20the%20process,to%20be%20duplicated%20or%20mislabeled.\">Data cleaning<\/a>, i.e., removing errors, duplicates, and inconsistencies, making the data reliable.<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Filtering the specific subsets of data to focus on relevant information.\u00a0<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Converting data into a suitable format to enhance visualisation.<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Dealing with missing values to ensures complete and meaningful analysis.<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Addressing outliers to avoid uneven interpretations, ensuring fairness by removing extreme values.<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Scaling data to a common range for fair comparisons. It&#8217;s like putting data on the same measuring scale.\u00a0<\/span><\/p>\n<\/li>\n<\/ul>\n<h3 dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Choosing the visualisations<\/span><\/h3>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Selecting appropriate visualisations based on data characteristics ensures clear and meaningful representation. Thus, choosing the right visualisations is crucial. So, here are the factors to consider:<\/span><\/p>\n<ul dir=\"ltr\">\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Different data types need different visualisations. For instance, categorical data may be best represented using bar charts. While trends over time are well-suited for line graphs. Thus, data type should also be considered.<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Consider the audience who will be viewing the visualisation. Choose visualisations that resonate with your audience and convey the message effectively.<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Ensure responsive and interactive visualisation, allowing users to gain insights on their own. It should not have unnecessary complexity.<\/span><\/p>\n<\/li>\n<\/ul>\n<h3 dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Design principles<\/span><\/h3>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Design principles enhance the effectiveness and impact of data visualisations. The design principles play a significant role in improving user experience. Thus, keep the following pointers in mind:<\/span><\/p>\n<ul dir=\"ltr\">\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Focus only on the necessary information. Data visualisation should have sufficient highlights that can be clear to your audience.<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Use clear labels, titles, and legends to guide your audience. This makes it easy for them to interpret the data.<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Maintain consistency in your design choices throughout the data visualisations. Colour schemes, fonts, and styles should create a cohesive and harmonious presentation.<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Use colours purposefully to highlight essential elements and distinguish different categories or groups. Contrasting colours can be used for comparisons.<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Choose appropriate font styles (bold, italics) to draw attention to important information.<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Logically organise the data visualisations to ensure the flow of the information.<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Limit the use of unnecessary effects that may distract users from the data.<\/span><\/p>\n<\/li>\n<\/ul>\n<h3 dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Iterative design and feedback\u00a0<\/span><\/h3>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Iterative designing is the process of refining data visualisations. This approach helps to improve visualisations through repeated design cycles, testing, feedback, etc., making the data more accurate and insightful. The feedback can be from stakeholders, experts, or even end-users. Iterative design and feedback incorporation improve the usability and relevance of visualisations.<\/span><\/p>\n<h2 dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Master data visualization<\/span><\/h2>\n<p dir=\"ltr\" data-node-text-align=\"justify\"><span data-text-color-mark=\"rgb(14, 16, 26)\">Data visualisation is about more than just about pretty pictures. It&#8217;s about letting the data speak visually. So, whether you are a graduate or a working professional, you can learn the art of data visualisation. With <\/span><span data-text-color-mark=\"#2E16E6\">a job-oriented curriculum <a href=\"https:\/\/imarticus.org\/postgraduate-program-in-data-science-analytics\/\"><strong>Data Science Course<\/strong><\/a> by industry experts offering a job guarantee, this live training program offers everything<\/span><span data-text-color-mark=\"rgb(14, 16, 26)\">. From the fundamentals to real-world projects, this program on\u00a0<\/span><a href=\"https:\/\/imarticus.org\/\" target=\"_blank\" rel=\"noopener noreferrer nofollow\" data-factors-click-bind=\"false\"><span data-text-color-mark=\"#4a6ee0\">Imarticus Learning,<\/span><\/a><span data-text-color-mark=\"rgb(14, 16, 26)\">\u00a0a leading platform for learning, is a game changer! So, enrol in this Postgraduate Program In Data Science And Analytics today and become an expert tomorrow.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>What is data visualization? Data visualisation is the art of representing data through visual elements. These data visualisation techniques include charts, graphs, maps, and much more. In today&#8217;s data-driven world, where information is overloaded, data visualisation is a game-changer.\u00a0 For instance, you have to analyse the sales data for a retail company. Traditionally, you will [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":259741,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_mo_disable_npp":"","_lmt_disableupdate":"","_lmt_disable":"","footnotes":""},"categories":[23],"tags":[],"class_list":["post-259740","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics"],"acf":[],"aioseo_notices":[],"modified_by":"Imarticus Learning","_links":{"self":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/259740","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/comments?post=259740"}],"version-history":[{"count":3,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/259740\/revisions"}],"predecessor-version":[{"id":265401,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/259740\/revisions\/265401"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/media\/259741"}],"wp:attachment":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/media?parent=259740"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/categories?post=259740"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/tags?post=259740"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}