{"id":246402,"date":"2022-01-24T05:23:42","date_gmt":"2022-01-24T05:23:42","guid":{"rendered":"https:\/\/imarticus.org\/?p=246402"},"modified":"2024-01-22T12:51:34","modified_gmt":"2024-01-22T12:51:34","slug":"top-10-hacks-to-speed-up-your-data-analysis","status":"publish","type":"post","link":"https:\/\/imarticus.org\/blog\/top-10-hacks-to-speed-up-your-data-analysis\/","title":{"rendered":"Top 10 Hacks to speed up your data analysis"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Data analysis can be a tedious task. Sometimes it feels like there is so much data and not enough time to analyze it all. But some simple tricks will save you a ton of time! In this blog post, we will share 10 top hacks to speed up your data analysis process. You&#8217;ll learn to quickly find insights in data without wasting precious hours waiting for programs to run or crunch numbers.<\/span><\/p>\n<h2><b>Ten hacks to speed up data analysis<\/b><\/h2>\n<ol>\n<li><b> Use hash tables instead of unsorted arrays:<\/b><\/li>\n<\/ol>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">An unsorted array is an ordered collection of objects accessible by numerical index, where the index indicates the sequence of its element&#8217;s appearance in the variety.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A hash table is an associative array, map, lookup table, and dictionary (in programming languages with a limited vocabulary, as Python), a data structure that associates keys to values.\u00a0<\/span><\/li>\n<\/ul>\n<ol start=\"2\">\n<li><b> Store data in row-major order:<\/b><\/li>\n<\/ol>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Use row-major order when storing data, which is faster to load into memory. Row major storage orders memory by rows.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Row major storage orders memory by rows instead of ordering memory by columns (called column-major storage).<\/span><\/li>\n<\/ul>\n<ol start=\"3\">\n<li><b> Group like items in buffers:<\/b><\/li>\n<\/ol>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">To speed up processing, store data in the most efficient order.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">For example, focus on grouping items in separate buffers instead of creating a different pad for every item.<\/span><\/li>\n<\/ul>\n<ol start=\"4\">\n<li><b> Store many data sets in memory:<\/b><\/li>\n<\/ol>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">If your data sets can fit into the RAM, many data sets into memory by using a hash table to map from keys to their corresponding data sets.<\/span><\/li>\n<\/ul>\n<ol start=\"5\">\n<li><b> Use persistent objects to pass data between function calls:<\/b><\/li>\n<\/ol>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Endless things are less expensive to construct and maintain than ephemeral objects.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">For example, instead of passing data from one function call to another, give object references and update the thing as needed.<\/span><\/li>\n<\/ul>\n<ol start=\"6\">\n<li><b> Use a meta-object system to add behavior to data:<\/b><\/li>\n<\/ol>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A meta-object system is a software framework that provides ways to add behavior to objects.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Use a meta-object system to add behavior to data so that you don&#8217;t have to write the same code for every data set.<\/span><\/li>\n<\/ul>\n<ol start=\"7\">\n<li><b> Avoid garbage collection overhead:<\/b><\/li>\n<\/ol>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Avoid using a garbage collector to reclaim unused memory if you can avoid it because the garbage collector has overhead that slows down the program.<\/span><\/li>\n<\/ul>\n<ol start=\"8\">\n<li><b> Reuse objects instead of allocating new ones:<\/b><\/li>\n<\/ol>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">To reuse objects, maintain a cache of things that get frequently used.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Enable garbage collection only after the cache has filled up since garbage collection is less expensive if the stock is entire.<\/span><\/li>\n<\/ul>\n<ol start=\"9\">\n<li><b> Create only the objects you need:<\/b><\/li>\n<\/ol>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Create only the objects you need to reduce memory allocations and garbage collection overhead.<\/span><\/li>\n<\/ul>\n<ol start=\"10\">\n<li><b> Use language-specific techniques:<\/b><\/li>\n<\/ol>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">If possible, use language-specific techniques to avoid memory allocations that you can prevent in languages with control over memory allocation.<\/span><\/li>\n<\/ul>\n<h2><b>Explore and Learn with Imarticus Learning<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Industry specialists created this postgraduate program to help you understand real-world Data Science applications from the ground up and construct strong models to deliver relevant business insights and forecasts. This program is for recent graduates who want to further their <strong><a href=\"https:\/\/imarticus.org\/postgraduate-program-in-data-science-analytics\/\">careers in Data Analytics course online<\/a><\/strong>, the most in-demand job skill. With this program&#8217;s job assurance guarantee, you may take a considerable step forward in your career.\u00a0<\/span><\/p>\n<p><strong>Some course USP:<\/strong><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">These <strong><a href=\"https:\/\/imarticus.org\/postgraduate-program-in-data-science-analytics\/\">data analytics courses in India<\/a><\/strong> to aid the students in learning job-relevant skills.<\/span><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Impress employers &amp; showcase skills with the certification in data analytics endorsed by India&#8217;s most prestigious academic collaborations.<\/span><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">World-Class Academic Professors to learn from through live online sessions and discussions.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Contact us through the chat support system or visit <strong><a href=\"https:\/\/imarticus.org\/postgraduate-program-in-data-science-analytics-mumbai\/\">Mumbai<\/a><\/strong>, Thane, <strong><a href=\"https:\/\/imarticus.org\/postgraduate-program-in-data-science-analytics-pune\/\">Pune<\/a><\/strong>, Chennai, <strong><a href=\"https:\/\/imarticus.org\/postgraduate-program-in-data-science-analytics-bangalore\/\">Bengaluru<\/a><\/strong>, <a href=\"https:\/\/imarticus.org\/postgraduate-program-in-data-science-analytics-delhi\/\">Delhi<\/a>, and <strong>Gurgaon<\/strong>, training centers.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Data analysis can be a tedious task. Sometimes it feels like there is so much data and not enough time to analyze it all. But some simple tricks will save you a ton of time! In this blog post, we will share 10 top hacks to speed up your data analysis process. You&#8217;ll learn to [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":245652,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_mo_disable_npp":"","om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[23],"tags":[831,1967,2876],"pages":[],"coe":[],"class_list":["post-246402","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-data-analytics-career","tag-data-analytics-online-training","tag-best-data-analytics-courses"],"acf":[],"contentshake_article_id":"","yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Top 10 Hacks to speed up your data analysis<\/title>\n<meta name=\"description\" content=\"You&#039;ll learn to quickly find insights in data without wasting precious hours waiting for programs to run or crunch numbers.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/imarticus.org\/blog\/top-10-hacks-to-speed-up-your-data-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Top 10 Hacks to speed up your data analysis\" \/>\n<meta property=\"og:description\" content=\"You&#039;ll learn to quickly find insights in data without wasting precious hours waiting for programs to run or crunch numbers.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/imarticus.org\/blog\/top-10-hacks-to-speed-up-your-data-analysis\/\" \/>\n<meta property=\"og:site_name\" content=\"Finance, Tech &amp; 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Sometimes it feels like there is so much data and not enough time to analyze it all. But some simple tricks will save you a ton of time! In this blog post, we will share 10 top hacks to speed up your data analysis process. You&#8217;ll learn to&hellip;","_links":{"self":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/246402","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=246402"}],"version-history":[{"count":2,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/246402\/revisions"}],"predecessor-version":[{"id":258669,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/246402\/revisions\/258669"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/media\/245652"}],"wp:attachment":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/media?parent=246402"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/categories?post=246402"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/tags?post=246402"},{"taxonomy":"pages","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/pages?post=246402"},{"taxonomy":"coe","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/coe?post=246402"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<!-- This website is optimized by Airlift. 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