{"id":1912,"date":"2022-11-22T10:44:10","date_gmt":"2022-11-22T10:44:10","guid":{"rendered":"https:\/\/pickl.ai\/blog\/?p=1912"},"modified":"2024-07-09T09:31:12","modified_gmt":"2024-07-09T09:31:12","slug":"what-is-data-analytics-in-data-science","status":"publish","type":"post","link":"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/","title":{"rendered":"What is Data Analytics in Data Science?"},"content":{"rendered":"<p><b>Summary: <\/b><span style=\"font-weight: 400;\">Data Science and Data Analytics are both crucial for data-driven decision making, but they have distinct roles. This table breaks down the key differences, highlighting the focus (uncovering patterns vs. answering questions) and methodology (exploratory vs. structured) of each field.<\/span><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Introduction\" >Introduction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Why_is_Data_Analytics_Important\" >Why is Data Analytics Important?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Types_of_Data_Analytics\" >Types of Data Analytics<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Descriptive_Analytics\" >Descriptive Analytics<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Diagnostic_Analytics\" >Diagnostic Analytics<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Predictive_Analytics\" >Predictive Analytics<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Prescriptive_Analytics\" >Prescriptive Analytics<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Application_of_Data_Analytics\" >Application of Data Analytics<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Steps_of_Application_of_Data_Analytics\" >Steps of Application of Data Analytics\u00a0<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Step_1_%E2%80%93_Defining_the_Scope_of_the_Analysis\" >Step 1 \u2013 Defining the Scope of the Analysis\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Step_2_%E2%80%93_Identifying_Data_Needs_and_Their_Sources\" >Step 2 \u2013 Identifying Data Needs and Their Sources<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Step_3_%E2%80%93_Preparing_Data_Sources_for_Analysis\" >Step 3 \u2013 Preparing Data Sources for Analysis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Step_4_%E2%80%93_Cleaning_Data_for_Analysis\" >Step 4 \u2013 Cleaning Data for Analysis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Step_5_%E2%80%93_Performing_Data_Analysis\" >Step 5 \u2013 Performing Data Analysis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Step_6_%E2%80%93_Presenting_the_Analysis_Results\" >Step 6 \u2013 Presenting the Analysis Results<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Data_Analyst_vs_Data_Scientist_Salary\" >Data Analyst vs Data Scientist Salary<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Techniques_in_Data_Analytics\" >Techniques in Data Analytics<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Regression_Analysis\" >Regression Analysis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Factor_Analysis\" >Factor Analysis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Cohort_Analysis\" >Cohort Analysis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Monte_Carlo_Simulations\" >Monte Carlo Simulations<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Time_Series_Analysis\" >Time Series Analysis<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Data_Analytics_Tools\" >Data Analytics Tools<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#R_Programming_Language\" >R Programming Language<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Python_Programming_Language\" >Python Programming Language<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#SAS_Programming_Language\" >SAS Programming Language<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-27\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#SQL\" >SQL<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-28\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Machine_Learning_Tools\" >Machine Learning Tools<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-29\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Parting_Thoughts\" >Parting Thoughts!!<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-30\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Frequently_Asked_Questions\" >Frequently Asked Questions<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-31\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Is_Data_Analytics_a_Stepping_Stone_to_Data_Science\" >Is Data Analytics a Stepping Stone to Data Science?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-32\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#How_Does_Data_Analytics_Contribute_to_Data_Science_Projects\" >How Does Data Analytics Contribute to Data Science Projects?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-33\" href=\"https:\/\/www.pickl.ai\/blog\/what-is-data-analytics-in-data-science\/#Can_Data_Scientists_Perform_Data_Analytics_Tasks\" >Can Data Scientists Perform Data Analytics Tasks?<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2 id=\"introduction\"><span class=\"ez-toc-section\" id=\"Introduction\"><\/span><b>Introduction<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">One of the most common questions that occur in the field of Data Science is \u2013 What is Data Analytics in Data Science?<\/span><b>\u00a0<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Data Analytics is the process of analysing data to understand it and make decisions. Businesses use it to improve processes, understand customer behavior, and decide on marketing campaigns.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Data Analytics is an important tool for any business that wants to improve how it processes <\/span><a href=\"https:\/\/en.wikipedia.org\/wiki\/Data\"><span style=\"font-weight: 400;\">data<\/span><\/a><span style=\"font-weight: 400;\"> and use it to make better business decisions. However, many organizations struggle with implementing Data Analytics solutions because they don\u2019t fully understand what it is or the benefits it can provide.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In this post, I\u2019ll explain what <\/span><a href=\"https:\/\/pickl.ai\/blog\/data-analytics-tutorial-mastering-types-of-statistical-sampling\/\"><span style=\"font-weight: 400;\">Data Analytics<\/span><\/a><span style=\"font-weight: 400;\"> is and how it can be used to help your organization make better business decisions.<\/span><\/p>\n<h2 id=\"why-is-data-analytics-important\"><span class=\"ez-toc-section\" id=\"Why_is_Data_Analytics_Important\"><\/span><b>Why is Data Analytics Important?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone size-full wp-image-11296\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca3.jpg\" alt=\"Why is Data Analytics Important?\" width=\"1000\" height=\"333\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca3.jpg 1000w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca3-300x100.jpg 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca3-768x256.jpg 768w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca3-110x37.jpg 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca3-200x67.jpg 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca3-380x127.jpg 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca3-255x85.jpg 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca3-550x183.jpg 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca3-800x266.jpg 800w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca3-150x50.jpg 150w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Data comes in many forms, including transaction data, social media content, website visits, and other sources. Organizations collect this data from many different sources and use it for different purposes.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example, companies may collect information from customers when they sign up for a service or make a purchase. This information is used to provide them with services and support related to their purchase or account.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">However, analysing the data collected isn\u2019t always easy, and businesses don\u2019t always know what to do with the data once they have it. That\u2019s where Data Analytics comes in. Data Analytics helps organizations analyse their data and make smarter decisions.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">There are several benefits of using Data Analytics in your business. It can help identify problems with your business processes and allow you to find better ways to accomplish your goals.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It can also be used to identify patterns, trends, and growth opportunities so that you can focus your resources on these areas to grow your business. Finally, it can be used to reduce risks and improve security.<\/span><\/p>\n<p><b>Click to know more: <\/b><a href=\"https:\/\/pickl.ai\/blog\/top-data-analyst-interview-questions-and-answers\/\"><b>Data Analyst Interview Questions and Answers<\/b><\/a><\/p>\n<h2 id=\"types-of-data-analytics\"><span class=\"ez-toc-section\" id=\"Types_of_Data_Analytics\"><\/span><b>Types of Data Analytics<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-11300\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca2.jpg\" alt=\"Types of Data Analytics\" width=\"1000\" height=\"333\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca2.jpg 1000w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca2-300x100.jpg 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca2-768x256.jpg 768w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca2-110x37.jpg 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca2-200x67.jpg 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca2-380x127.jpg 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca2-255x85.jpg 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca2-550x183.jpg 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca2-800x266.jpg 800w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/ca2-150x50.jpg 150w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Analysing data can help companies make smarter business decisions and help them become more competitive in the marketplace.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">By analysing data about the effectiveness of their marketing campaigns, for example, businesses can discover which of their strategies are working and which ones are not. This can help them decide where they should focus their efforts to maximize their return on investment.<\/span><\/p>\n<h3 id=\"descriptive-analytics\"><span class=\"ez-toc-section\" id=\"Descriptive_Analytics\"><\/span><b>Descriptive Analytics<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">This type of analysis typically involves examining the past in an attempt to predict future outcomes. Businesses often use descriptive analytics to help them better understand their customers and their buying habits.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example, a retailer might analyse customer data to determine which products perform best or what times of the day are most popular with customers. Companies use this information to enhance their marketing and operations strategies, driving sales and increasing customer satisfaction.<\/span><\/p>\n<h3 id=\"diagnostic-analytics\"><span class=\"ez-toc-section\" id=\"Diagnostic_Analytics\"><\/span><b>Diagnostic Analytics<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>This analysis aims to identify the root cause of problems for effective resolution. It also uncovers new opportunities that the company might pursue.<\/p>\n<p><span style=\"font-weight: 400;\">For instance, a company might use diagnostic analytics to find out why its products aren\u2019t selling as well as they expected and discover ways they can address the issue before it becomes a bigger problem.<\/span><\/p>\n<h3 id=\"predictive-analytics\"><span class=\"ez-toc-section\" id=\"Predictive_Analytics\"><\/span><b>Predictive Analytics<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">This type of analysis uses historical information to make predictions about the future. <\/span>It determines which products are most likely to succeed and predicts the timing of certain events.<\/p>\n<p><span style=\"font-weight: 400;\">For example, a company may use predictive analytics to help determine when the best time to launch a new product will be so they can take advantage of the most lucrative sales opportunities.<\/span><\/p>\n<h3 id=\"prescriptive-analytics\"><span class=\"ez-toc-section\" id=\"Prescriptive_Analytics\"><\/span><b>Prescriptive Analytics<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">This type of analysis identifies the best course of action to take to achieve the desired result. Companies frequently use it to optimize business processes and systems, improving efficiency and eliminating waste.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example, a business might use prescriptive analytics to identify potential improvements they can make to their supply chain to improve delivery times and reduce expenses.<\/span><\/p>\n<p><b>Difference between Data Science and Data Analytics with Example<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Data Science and Data Analytics, while seemingly similar, have distinct approaches. This table highlights these differences, highlighting the focus, methodology, and desired outcomes of each field.<\/span><\/p>\n<p><a href=\"https:\/\/pickl.ai\/blog\/top-15-data-analytics-tools-for-data-analysts\/\"><b>Click to know more: Data Analytics Tools for Data Analysts\u00a0<\/b><\/a><\/p>\n<h2 id=\"application-of-data-analytics\"><span class=\"ez-toc-section\" id=\"Application_of_Data_Analytics\"><\/span><b>Application of Data Analytics<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Apply analytical methods to business transactions for the purpose of optimizing performance, reducing costs, and increasing revenue. This may include examining operational processes to identify inefficiencies and opportunities for improvements, reviewing business records to ensure compliance and identify fraudulent activity. The following steps for Data analysis can be considered important:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Developing the process\/structure of the Analytic solution.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Identifying data needs and their sources.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Preparing data sources for analysis.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Cleaning data for analysis.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Analysing the data to extract insights and produce actionable information.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Reporting the results and providing recommendations.<\/span><\/li>\n<\/ul>\n<h2 id=\"steps-of-application-of-data-analytics\"><span class=\"ez-toc-section\" id=\"Steps_of_Application_of_Data_Analytics\"><\/span><b>Steps of Application of Data Analytics\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-11302\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/Ca1.jpg\" alt=\"Steps of Application of Data Analytics \" width=\"1000\" height=\"333\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/Ca1.jpg 1000w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/Ca1-300x100.jpg 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/Ca1-768x256.jpg 768w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/Ca1-110x37.jpg 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/Ca1-200x67.jpg 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/Ca1-380x127.jpg 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/Ca1-255x85.jpg 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/Ca1-550x183.jpg 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/Ca1-800x266.jpg 800w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2022\/11\/Ca1-150x50.jpg 150w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Data surrounds us, but how do we extract its true value? Data Analytics offers a powerful roadmap to transform raw data into actionable insights. This process unfolds in a series of steps, from defining the question or problem to be addressed to visualizing the results for clear communication.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">By following these steps, data can be transformed from a jumbled collection of numbers to a source of knowledge that drives informed decision-making.<\/span><\/p>\n<h3 id=\"step-1-defining-the-scope-of-the-analysis\"><span class=\"ez-toc-section\" id=\"Step_1_%E2%80%93_Defining_the_Scope_of_the_Analysis\"><\/span><b>Step 1 \u2013 Defining the Scope of the Analysis\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The first step involves defining the scope of the analysis and developing the structure and process for the analytic solution. This may involve developing a model of the business process being analysed and identifying how the process will be optimized using analytic solutions.<\/span><\/p>\n<h3 id=\"step-2-identifying-data-needs-and-their-sources\"><span class=\"ez-toc-section\" id=\"Step_2_%E2%80%93_Identifying_Data_Needs_and_Their_Sources\"><\/span><b>Step 2 \u2013 Identifying Data Needs and Their Sources<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Next, an analyst has to identify and capture the relevant transactional and operational data that is required to support the analysis. The type of data required will vary depending on the specific analysis being performed by the analyst.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In some cases, the relevant data may be contained in relational databases, spreadsheets, or flat files. In other cases, it may be necessary to develop customized applications to store and analyse the data.<\/span><\/p>\n<h3 id=\"step-3-preparing-data-sources-for-analysis\"><span class=\"ez-toc-section\" id=\"Step_3_%E2%80%93_Preparing_Data_Sources_for_Analysis\"><\/span><b>Step 3 \u2013 Preparing Data Sources for Analysis<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Once the data has been identified and captured, it must be prepared so that it can be used in the analysis. In many cases, the data will need to be restructured and filtered so that it conforms to the standard formats used by the analytical tools that are used to perform the analysis.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This step may require the help of subject matter experts who can help make sure that the data is formatted properly for use in a given analysis. The structured and cleansed data can then be used by the analyst in the subsequent steps of the Data Analysis process.<\/span><\/p>\n<h3 id=\"step-4-cleaning-data-for-analysis\"><span class=\"ez-toc-section\" id=\"Step_4_%E2%80%93_Cleaning_Data_for_Analysis\"><\/span><b>Step 4 \u2013 Cleaning Data for Analysis<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Once the data has been prepared, it may need to be cleaned before it is used in the analysis. This may involve removing any unwanted records that are included as part of the source data so that only the desired records are used in the analysis.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example, if the data is being analysed using an advanced analytics tool, there may be records that do not contain the desired information because they contain values that are considered outliers.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In this case, the records containing these unwanted values can be removed from the data set before it is used to perform the analysis.<\/span><\/p>\n<h3 id=\"step-5-performing-data-analysis\"><span class=\"ez-toc-section\" id=\"Step_5_%E2%80%93_Performing_Data_Analysis\"><\/span><b>Step 5 \u2013 Performing Data Analysis<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The final step in the process is to perform the actual analysis using the data and preparing the results for presentation. Depending on the type of analysis being performed, the analysis may be performed manually or using an automated tool such as a spreadsheet application or an advanced analytics tool.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In some cases, it may be necessary to combine the results of multiple analyses in order to conduct a complete analysis of the data. For example, the data obtained from the network intrusion detection system can be combined with the results of other analyses to identify the root cause of the breach.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The data can then be presented to management so that they can identify the steps that need to be taken to address the risk and prevent future incidents from occurring.<\/span><\/p>\n<h3 id=\"step-6-presenting-the-analysis-results\"><span class=\"ez-toc-section\" id=\"Step_6_%E2%80%93_Presenting_the_Analysis_Results\"><\/span><b>Step 6 \u2013 Presenting the Analysis Results<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Once the analysis is complete, the results must be presented so that the results can be understood by management and other stakeholders in the organization. The results of the analysis should be presented in a clear and straightforward manner that avoids using complicated terminology or excessive detail.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This will make it easier for non-technical members of the organization to understand the results and take appropriate actions based on the findings. Recommendations should also be presented along with a plan of action that will outline the steps that need to be taken based on the findings.<\/span><\/p>\n<h2 id=\"data-analyst-vs-data-scientist-salary\"><span class=\"ez-toc-section\" id=\"Data_Analyst_vs_Data_Scientist_Salary\"><\/span><b>Data Analyst vs Data Scientist Salary<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">As the data revolution unfolds, two in-demand roles have emerged: Data Analysts and Data Scientists. Both are essential for extracting knowledge from data, but their career paths diverge. This section highlights the key differences between Data Analysts and Data Scientists:<\/span><\/p>\n<p><b>Data Analyst Salary<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Average:<\/b><span style=\"font-weight: 400;\"> The average Data Analyst salary in India ranges<\/span><a href=\"https:\/\/www.ambitionbox.com\/profile\/data-analyst-salary#:~:text=Data%20Analyst%20salary%20in%20India%20ranges%20between%20%E2%82%B9%201.8%20Lakhs,salary%20of%20%E2%82%B9%206.4%20Lakhs.\"> <span style=\"font-weight: 400;\">from\u00a0 \u20b9 1.8 Lakhs to \u20b9 13.0 Lakhs<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Factors Affecting Salary:<\/b><span style=\"font-weight: 400;\"> Experience, location, industry, specific skills (e.g., machine learning expertise), and the size of the company can all significantly impact a Data Analyst&#8217;s salary.<\/span><\/li>\n<\/ul>\n<p><b>Data Scientist Salary<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Average:<\/b><span style=\"font-weight: 400;\"> Data Scientists typically command higher salaries compared to Data Analysts. The average Data Scientist salary in India can range from<\/span> <span style=\"font-weight: 400;\">\u00a0<\/span><a href=\"https:\/\/www.ambitionbox.com\/profile\/data-scientist-salary\"><span style=\"font-weight: 400;\">\u20b9 3.9 Lakhs to \u20b9 28.0 Lakhs<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Factors Affecting Salary:<\/b><span style=\"font-weight: 400;\"> Similar to Data Analysts, experience, location, industry, and skillset all play a crucial role in determining a Data Scientist&#8217;s salary. Additionally, factors like possessing a postgraduate degree in a relevant field (e.g., statistics, computer science) and strong leadership or communication skills can further influence earning potential.<\/span><\/li>\n<\/ul>\n<h2 id=\"techniques-in-data-analytics\"><span class=\"ez-toc-section\" id=\"Techniques_in_Data_Analytics\"><\/span><b>Techniques in Data Analytics<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">There are various analytical methods that can be used for performing Data Analysis. These Data Analytics techniques can be explained as follows:<\/span><\/p>\n<h3 id=\"regression-analysis\"><span class=\"ez-toc-section\" id=\"Regression_Analysis\"><\/span><b>Regression Analysis<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">This is a statistical technique that is used to model the relationship between two or more variables. It is a technique used to examine trends in data and predict outcomes based on existing trends. For example, it can be used to determine the impact of a marketing campaign on product sales.<\/span><\/p>\n<h3 id=\"factor-analysis\"><span class=\"ez-toc-section\" id=\"Factor_Analysis\"><\/span><b>Factor Analysis<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">It can be used to measure the level of association between different variables in a dataset. Factor analysis can be used to identify key factors that drive customer satisfaction in a particular company. It can also be used to identify the factors that are likely to impact consumer purchasing decisions in the future.<\/span><\/p>\n<h3 id=\"cohort-analysis\"><span class=\"ez-toc-section\" id=\"Cohort_Analysis\"><\/span><b>Cohort Analysis<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">This is a statistical technique that can be used to analyse the data from different groups of customers or patients in order to understand which factors influence their buying decisions.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example, a company can use cohort analysis to determine which factors are important to the customer age group that is more likely to buy its products.<\/span><\/p>\n<h3 id=\"monte-carlo-simulations\"><span class=\"ez-toc-section\" id=\"Monte_Carlo_Simulations\"><\/span><b>Monte Carlo Simulations<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">This technique is a type of stochastic simulation that can be used to create estimates by simulating many different scenarios and analysing the results of each scenario to predict the likelihood of a particular outcome occurring.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example, it can be used to determine if the company is at risk of failure due to a potential market decline. It can also be used to evaluate the effectiveness of a new product launch or marketing campaign.<\/span><\/p>\n<h3 id=\"time-series-analysis\"><span class=\"ez-toc-section\" id=\"Time_Series_Analysis\"><\/span><b>Time Series Analysis<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">This is a type of statistical analysis that can be used to analyse patterns over time. It can be used to identify seasonal sales patterns that affect customer demand for a particular product.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It can also be used to predict market trends in order to determine whether demand for a product is likely to increase or decrease in the near future.<\/span><\/p>\n<h2 id=\"data-analytics-tools\"><span class=\"ez-toc-section\" id=\"Data_Analytics_Tools\"><\/span><b>Data Analytics Tools<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">From wrangling messy data to crafting compelling visualizations, the Data Analytics tools empower Data Analysts to uncover hidden patterns, answer complex questions, and communicate results effectively. Let&#8217;s delve into the treasure chest of data analytics tools and explore how they can unleash the power of data!<\/span><\/p>\n<h3 id=\"r-programming-language\"><span class=\"ez-toc-section\" id=\"R_Programming_Language\"><\/span><b>R Programming Language<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">R is an open-source programming language that can be used for a wide range of Data Analytics applications. It is widely used by Data Scientists for performing statistical analysis. It can analyse large datasets quickly and perform sophisticated Data Analysis tasks with ease.<\/span><\/p>\n<h3 id=\"python-programming-language\"><span class=\"ez-toc-section\" id=\"Python_Programming_Language\"><\/span><b>Python Programming Language<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Python is another popular programming language that can be used for a variety of applications including Data Analytics. It is a general-purpose language that is easy to learn and can be used to perform complex Data Analysis tasks in a relatively short time. Python can be used in both interactive and batch modes for Data Analytics purposes.<\/span><\/p>\n<h3 id=\"sas-programming-language\"><span class=\"ez-toc-section\" id=\"SAS_Programming_Language\"><\/span><b>SAS Programming Language<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">SAS stands for Statistical Analysis System and is a software suite that can be used for performing advanced analytics such as data mining and predictive analytics.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It is generally used for analysing data in real time but can also be used for storing and analysing data in batches as well. It is a commercial product that is available on a subscription basis from SAS Inc. (formerly known as SAS Institute).<\/span><\/p>\n<h3 id=\"sql\"><span class=\"ez-toc-section\" id=\"SQL\"><\/span><b>SQL<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><a href=\"https:\/\/pickl.ai\/blog\/optimising-inventory-with-data-analytics\/\"><span style=\"font-weight: 400;\">SQL<\/span><\/a><span style=\"font-weight: 400;\"> is a relational database language that is commonly used for data management and analytics purposes. It is an ANSI standard language and is supported by all major database management systems.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It can be used for creating and maintaining databases as well as performing basic and advanced query operations on them.<\/span><\/p>\n<h3 id=\"machine-learning-tools\"><span class=\"ez-toc-section\" id=\"Machine_Learning_Tools\"><\/span><b>Machine Learning Tools<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><a href=\"https:\/\/pickl.ai\/blog\/feature-engineering-in-machine-learning\/\"><span style=\"font-weight: 400;\">Machine Learning tools<\/span><\/a><span style=\"font-weight: 400;\"> can be used to develop custom models that can be used to make predictions about future trends based on the historical data available.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">They can incorporate many different techniques including decision trees, regression, clustering, anomaly detection, etc. and they are widely used in industries such as finance, retail, healthcare, manufacturing, transportation, etc.<\/span><\/p>\n<p><b>Click to Know More: <\/b><a href=\"https:\/\/pickl.ai\/blog\/remote-data-analyst-jobs-salary-employment\/\"><b>Remote Data Analyst Jobs, Salary, Employment<\/b><\/a><\/p>\n<h2 id=\"parting-thoughts\"><span class=\"ez-toc-section\" id=\"Parting_Thoughts\"><\/span><b>Parting Thoughts!!<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Hence, from the above blog post, it can be concluded that Data Analytics plays a very vital role in business organizations today and the tools that are used for performing analytics are constantly evolving and improving with time.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This, in turn, makes the data that is analysed much more reliable and provides better insights which can help businesses make more informed decisions.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Individuals aspiring to a technical career in the Data Science field can pursue various Data Science and Data Analytics online courses to gain the necessary skills and expertise required to perform Data Analytics effectively.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">They can opt for a <\/span><a href=\"https:\/\/www.pickl.ai\/\"><b>Data Analytics course online<\/b><\/a><span style=\"font-weight: 400;\"> which will help them gain the needed skills and knowledge required to become a successful Data Analyst.\u00a0<\/span><\/p>\n<h2 id=\"frequently-asked-questions\"><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span><b>Frequently Asked Questions<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3 id=\"is-data-analytics-a-stepping-stone-to-data-science\"><span class=\"ez-toc-section\" id=\"Is_Data_Analytics_a_Stepping_Stone_to_Data_Science\"><\/span><b>Is Data Analytics a Stepping Stone to Data Science?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data Analytics provides a strong foundation for Data Science. By honing your skills in data cleaning, analysis, and visualization, you gain transferable knowledge crucial for building models and solving complex problems with data \u2013 hallmarks of Data Science.<\/span><\/p>\n<h3 id=\"how-does-data-analytics-contribute-to-data-science-projects\"><span class=\"ez-toc-section\" id=\"How_Does_Data_Analytics_Contribute_to_Data_Science_Projects\"><\/span><b>How Does Data Analytics Contribute to Data Science Projects?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data Analytics plays a vital role throughout the Data Science lifecycle. It helps define the initial questions, clean and prepare data for modelling, and interpret the results of Data Science models. Essentially, Data Analytics provides the groundwork for Data Science to flourish.<\/span><\/p>\n<h3 id=\"can-data-scientists-perform-data-analytics-tasks\"><span class=\"ez-toc-section\" id=\"Can_Data_Scientists_Perform_Data_Analytics_Tasks\"><\/span><b>Can Data Scientists Perform Data Analytics Tasks?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Absolutely! Data scientists possess the skills of a data analyst, but their expertise extends further. While they can handle data cleaning and visualization, data scientists delve deeper into building models and using advanced techniques to extract knowledge from data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"Data Analytics vs. Data Science: Explained in a table.\n","protected":false},"author":9,"featured_media":11292,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[82,292,46],"tags":[396,398,199,395,397,399,394,392,393],"ppma_author":[2170,2184],"class_list":{"0":"post-1912","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-career-path","8":"category-data-analysts","9":"category-data-science","10":"tag-application-of-data-analytics","11":"tag-data-analyst-vs-data-scientist-salary","12":"tag-data-analytics-tools","13":"tag-difference-between-data-science-and-data-analytics-with-example","14":"tag-steps-of-application-of-data-analytics","15":"tag-techniques-in-data-analytics","16":"tag-types-of-data-analytics","17":"tag-what-is-data-analytics-in-data-science","18":"tag-why-is-data-analytics-important"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>What is Data Analytics in Data Science? - Pickl.AI<\/title>\n<meta name=\"description\" content=\"Data Analytics in Data Science helps organizations &amp; individuals to make sense of data. 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