{"id":145373,"date":"2026-11-02T17:51:17","date_gmt":"2026-11-02T12:21:17","guid":{"rendered":"https:\/\/www.guvi.in\/blog\/?p=145373"},"modified":"2026-11-02T17:51:19","modified_gmt":"2026-11-02T12:21:19","slug":"marketing-analyst-to-data-scientist","status":"publish","type":"post","link":"https:\/\/www.guvi.in\/blog\/marketing-analyst-to-data-scientist\/","title":{"rendered":"Marketing Analyst to Data Scientist: Skills and Career Path"},"content":{"rendered":"\n<p>Marketing analysts work with campaign performance, customer behaviour, conversion rates, and market trends. This experience provides a foundation for data science. By learning programming, statistics, and machine learning, marketing analysts can move beyond reporting campaign results to predicting customer behaviour and identifying business opportunities.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>TL;DR<\/strong><\/h2>\n\n\n\n<ul>\n<li>Marketing analytics develops skills in customer analysis, campaign measurement, and data interpretation.<\/li>\n\n\n\n<li>Learn Python, SQL, statistics, and machine learning to prepare for data science roles.<\/li>\n\n\n\n<li>Develop projects involving customer churn, campaign response, and customer segmentation.<\/li>\n\n\n\n<li>Build a portfolio demonstrating predictive modelling and problem-solving.<\/li>\n\n\n\n<li>Explore roles such as Junior Data Scientist, Marketing Data Scientist, and Customer Analytics Specialist.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Quick Answer<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td>A <a href=\"https:\/\/www.coursera.org\/in\/articles\/marketing-analyst\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">marketing analyst<\/a> can become a data scientist by combining marketing knowledge with technical skills. Start with Python and SQL, learn statistics and machine learning, and practise analysing customer datasets. Build projects involving churn prediction, campaign response, and customer segmentation to demonstrate your ability to develop predictive solutions.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n<div class=\"wp-block-guvi-cta-button\" style=\"justify-content:center;\"><a href=\"https:\/\/www.guvi.in\/zen-class\/data-science-course\/?utm_source=blog&#038;utm_medium=cta-button&#038;utm_campaign=marketing-analyst-to-data-scientist\" class=\"guvi-btn-enquire\" target=\"_blank\" rel=\"noopener noreferrer\">Explore HCL GUVI Data Science Course<svg class=\"guvi-btn-chevron\" style=\"transform:rotate(0deg);\" width=\"16\" height=\"16\" viewBox=\"0 0 24 24\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-hidden=\"true\"><path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M9.39935 18.1007C9.0674 17.7687 9.0674 17.2305 9.39935 16.8986L13.6922 12.6057C13.7508 12.5471 13.7508 12.4521 13.6922 12.3935L9.39935 8.10065C9.0674 7.7687 9.0674 7.23051 9.39935 6.89857C9.7313 6.56662 10.2695 6.56662 10.6014 6.89857L14.8943 11.1915C15.6168 11.9139 15.6168 13.0853 14.8943 13.8078L10.6014 18.1007C10.2695 18.4326 9.7313 18.4326 9.39935 18.1007Z\" fill=\"currentColor\"\/><\/svg><\/a><\/div>\n\n\n<h2 class=\"wp-block-heading\"><strong>Identify Your Transferable Skills as a Marketing Analyst<\/strong><\/h2>\n\n\n\n<p>Your existing marketing responsibilities may already involve activities relevant to data science. Understanding these skills helps you identify where to begin.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Customer Behaviour and Segmentation<\/strong><\/h3>\n\n\n\n<p>Marketing analysts examine customer demographics, purchasing patterns, and engagement levels to understand audience preferences.<\/p>\n\n\n\n<p>This experience connects with data science activities such as:<\/p>\n\n\n\n<ul>\n<li>Identifying customer groups using <a href=\"https:\/\/www.guvi.in\/blog\/clustering-in-data-science\/\" target=\"_blank\" rel=\"noreferrer noopener\">clustering<\/a>.<\/li>\n\n\n\n<li>Analysing purchasing behaviour.<\/li>\n\n\n\n<li>Predicting customer responses.<\/li>\n\n\n\n<li>Identifying potential high-value customers.<\/li>\n\n\n\n<li>Examining customer retention patterns.<\/li>\n<\/ul>\n\n\n\n<p>This knowledge helps you select meaningful variables when developing predictive <a href=\"https:\/\/www.guvi.in\/blog\/data-science-models-types-and-techniques\/\" target=\"_blank\" rel=\"noreferrer noopener\">models<\/a>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Campaign Performance Analysis<\/strong><\/h3>\n\n\n\n<p>Marketing analysts evaluate campaign results using <a href=\"https:\/\/www.guvi.in\/blog\/top-conversion-rate-optimization-techniques\/\" target=\"_blank\" rel=\"noreferrer noopener\">conversion rates<\/a>, click-through rates, customer acquisition costs, and return on investment.<\/p>\n\n\n\n<p>These metrics provide useful context for developing models that predict campaign responses, estimate <a href=\"https:\/\/www.salesforce.com\/blog\/sales\/customer-lifetime-value\/\" target=\"_blank\" rel=\"noreferrer noopener\">customer lifetime value<\/a>, and identify effective marketing channels.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Turn Marketing Analysis into Data Science Applications<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Move from Reporting to Predictive Analysis<\/strong><\/h3>\n\n\n\n<p>Marketing analysis often explains what happened, while data science can help estimate what may happen next.<\/p>\n\n\n\n<p>Consider these examples:<\/p>\n\n\n\n<ul>\n<li><strong>Customer <\/strong><a href=\"https:\/\/www.guvi.in\/hub\/power-bi-tutorial\/project-3-customer-analytics-and-retention\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>retention<\/strong><\/a><strong>:<\/strong> Which customers are likely to stop purchasing?<\/li>\n\n\n\n<li><strong>Campaign performance:<\/strong> Who is most likely to respond to an offer?<\/li>\n\n\n\n<li><strong>Lead conversion:<\/strong> Which leads have the highest conversion probability?<\/li>\n\n\n\n<li><strong>Customer value:<\/strong> Which customers may generate higher lifetime revenue?<\/li>\n\n\n\n<li><strong>Product recommendations:<\/strong> What products might customers purchase next?<\/li>\n<\/ul>\n\n\n\n<p>These questions can become practical machine learning projects.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Develop Statistical and Experimentation Skills<\/strong><\/h3>\n\n\n\n<p>Understanding marketing experiments is useful when moving into data science. Learn probability, hypothesis testing, confidence intervals, statistical significance, and regression analysis.<\/p>\n\n\n\n<p>Practice evaluating <a href=\"https:\/\/www.guvi.in\/blog\/a-b-testing-in-marketing\/\" target=\"_blank\" rel=\"noreferrer noopener\">A\/B test<\/a> results and determining whether observed differences are meaningful. This helps prevent incorrect conclusions from marketing datasets.<\/p>\n\n\n\n<div style=\"background-color: #099f4e; border: 3px solid #110053; border-radius: 12px; padding: 18px 22px; color: #FFFFFF; font-size: 18px; font-family: Montserrat, Helvetica, sans-serif; line-height: 1.6; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15); max-width: 750px;\"> \n  <strong style=\"font-size: 22px; color: #FFFFFF;\">\ud83d\udca1 Did You Know?<\/strong> \n  <br \/><br \/> \nMarketing data science can help businesses estimate customer lifetime value, predict churn, forecast campaign responses, and personalise product recommendations. These applications allow marketing teams to make decisions using predictive insights rather than relying only on historical reports.\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Learn the Technical Skills Needed for Data Science<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Start with Python and SQL<\/strong><\/h3>\n\n\n\n<p><a href=\"https:\/\/www.guvi.in\/blog\/useful-python-libraries-tools-for-data-science\/\" target=\"_blank\" rel=\"noreferrer noopener\">Python<\/a> is widely used for data manipulation, analysis, and machine learning. Begin with variables, loops, functions, conditional statements, and <a href=\"https:\/\/www.guvi.in\/blog\/types-of-data-structures-in-python\/\" target=\"_blank\" rel=\"noreferrer noopener\">data structures<\/a>.<\/p>\n\n\n\n<p>Focus on these libraries:<\/p>\n\n\n\n<ul>\n<li><strong>Pandas:<\/strong> Data cleaning and manipulation.<\/li>\n\n\n\n<li><strong>NumPy:<\/strong> Numerical calculations.<\/li>\n\n\n\n<li><strong>Matplotlib:<\/strong> Data visualisation.<\/li>\n\n\n\n<li><strong>Scikit-learn:<\/strong> Machine learning.<\/li>\n<\/ul>\n\n\n\n<p>Learn <a href=\"https:\/\/www.guvi.in\/blog\/guide-on-sql-for-data-science\/\" target=\"_blank\" rel=\"noreferrer noopener\">SQL<\/a> to retrieve and combine customer and campaign information. Focus on:<\/p>\n\n\n\n<ul>\n<li><strong>SELECT, WHERE, and ORDER BY:<\/strong> Filtering records.<\/li>\n\n\n\n<li><strong>GROUP BY and aggregate functions:<\/strong> Summarising metrics.<\/li>\n\n\n\n<li><strong>Joins:<\/strong> Combining datasets.<\/li>\n\n\n\n<li><strong>CASE WHEN:<\/strong> Categorising records.<\/li>\n\n\n\n<li><strong>Subqueries:<\/strong> Organising complex queries.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Learn Statistics and Machine Learning<\/strong><\/h3>\n\n\n\n<p><a href=\"https:\/\/www.guvi.in\/blog\/statistics-fundamentals-for-machine-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">Statistics<\/a> helps you understand relationships and patterns in marketing data. Study probability, distributions, correlation, regression, and hypothesis testing.<\/p>\n\n\n\n<p>Next, learn essential machine learning techniques:<\/p>\n\n\n\n<ul>\n<li><a href=\"https:\/\/skillodisha.guvi.in\/hub\/artificial-intelligence-tutorial\/linear-regression-and-logistic-regression\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Linear and logistic regression<\/strong><\/a><strong>:<\/strong> Prediction.<\/li>\n\n\n\n<li><a href=\"https:\/\/www.guvi.in\/hub\/artificial-intelligence-tutorial\/decision-trees-and-random-forests\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Decision trees and random forests<\/strong><\/a><strong>:<\/strong> Classification.<\/li>\n\n\n\n<li><a href=\"https:\/\/www.guvi.in\/hub\/machine-learning-tutorial\/k-means-clustering-explained\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>K-means clustering<\/strong><\/a><strong>:<\/strong> Customer segmentation.<\/li>\n\n\n\n<li><a href=\"https:\/\/www.guvi.in\/hub\/fake-review-detection-system-using-machine-learning\/calculating-precision-recall-and-f1-score\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Precision, recall, and F1-score<\/strong><\/a><strong>:<\/strong> Model evaluation.<\/li>\n\n\n\n<li><a href=\"https:\/\/www.guvi.in\/hub\/machine-learning-tutorial\/cross-validation-explained\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Cross-validation<\/strong><\/a><strong>:<\/strong> Checking model performance.<\/li>\n<\/ul>\n\n\n\n<p>Understand overfitting and train-test splitting before applying models to business problems.<\/p>\n\n\n<div class=\"wp-block-guvi-cta-button\" style=\"justify-content:center;\"><a href=\"https:\/\/www.guvi.in\/zen-class\/data-science-course\/?utm_source=blog&#038;utm_medium=cta-button&#038;utm_campaign=marketing-analyst-to-data-scientist\" class=\"guvi-btn-enquire\" target=\"_blank\" rel=\"noopener noreferrer\">Explore HCL GUVI Data Science Course<svg class=\"guvi-btn-chevron\" style=\"transform:rotate(0deg);\" width=\"16\" height=\"16\" viewBox=\"0 0 24 24\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-hidden=\"true\"><path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M9.39935 18.1007C9.0674 17.7687 9.0674 17.2305 9.39935 16.8986L13.6922 12.6057C13.7508 12.5471 13.7508 12.4521 13.6922 12.3935L9.39935 8.10065C9.0674 7.7687 9.0674 7.23051 9.39935 6.89857C9.7313 6.56662 10.2695 6.56662 10.6014 6.89857L14.8943 11.1915C15.6168 11.9139 15.6168 13.0853 14.8943 13.8078L10.6014 18.1007C10.2695 18.4326 9.7313 18.4326 9.39935 18.1007Z\" fill=\"currentColor\"\/><\/svg><\/a><\/div>\n\n\n<h2 class=\"wp-block-heading\"><strong>Build Marketing Data Science Portfolio Projects<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Project 1: Customer Churn Prediction<\/strong><\/h3>\n\n\n\n<p>In this <a href=\"https:\/\/www.guvi.in\/hub\/customer-churn-prediction-project-using-classification-techniques\/what-is-customer-churn-prediction-\/\" target=\"_blank\" rel=\"noreferrer noopener\">project<\/a>, Create a model to identify customers who may stop purchasing or discontinue subscriptions. Use Python and <a href=\"https:\/\/www.guvi.in\/blog\/pandas-introduction\/\" target=\"_blank\" rel=\"noreferrer noopener\">Pandas<\/a> to prepare customer data, then apply logistic regression or random forest classification. Evaluate predictions using precision, recall, and F1-score.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Project 2: Campaign Response Prediction<\/strong><\/h3>\n\n\n\n<p>Analyse customer characteristics, previous campaign interactions, and response records. Use Python and Scikit-learn to build a classification model that estimates the likelihood of customers responding to future campaigns.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Project 3: Customer Segmentation<\/strong><\/h3>\n\n\n\n<p>Analyse purchase frequency, spending patterns, and customer recency. Apply K-means clustering to identify customer groups and visualise their characteristics. Explain how businesses can use these segments for targeted marketing.<\/p>\n\n\n\n<p>For each <a href=\"https:\/\/www.guvi.in\/blog\/what-is-semantic-segmentation\/\" target=\"_blank\" rel=\"noreferrer noopener\">project<\/a>, document the business question, dataset, cleaning steps, modelling approach, evaluation metrics, findings, and limitations.<\/p>\n\n\n<div class=\"wp-block-guvi-cta-button\" style=\"justify-content:center;\"><a href=\"https:\/\/www.guvi.in\/zen-class\/data-science-course\/?utm_source=blog&#038;utm_medium=cta-button&#038;utm_campaign=marketing-analyst-to-data-scientist\" class=\"guvi-btn-enquire\" target=\"_blank\" rel=\"noopener noreferrer\">Explore HCL GUVI Data Science Course<svg class=\"guvi-btn-chevron\" style=\"transform:rotate(0deg);\" width=\"16\" height=\"16\" viewBox=\"0 0 24 24\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-hidden=\"true\"><path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M9.39935 18.1007C9.0674 17.7687 9.0674 17.2305 9.39935 16.8986L13.6922 12.6057C13.7508 12.5471 13.7508 12.4521 13.6922 12.3935L9.39935 8.10065C9.0674 7.7687 9.0674 7.23051 9.39935 6.89857C9.7313 6.56662 10.2695 6.56662 10.6014 6.89857L14.8943 11.1915C15.6168 11.9139 15.6168 13.0853 14.8943 13.8078L10.6014 18.1007C10.2695 18.4326 9.7313 18.4326 9.39935 18.1007Z\" fill=\"currentColor\"\/><\/svg><\/a><\/div>\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube\"><div class=\"wp-block-embed__wrapper\">\n<div class=\"container-lazyload preview-lazyload container-youtube js-lazyload--not-loaded\"><a href=\"https:\/\/www.youtube.com\/watch?v=ibz66eWxl-4&#038;list=PL_9uM5be2amoyP0j3E_J8MmRAtlKlrQs2\" class=\"lazy-load-youtube preview-lazyload preview-youtube\" data-video-title=\"Data Analyst to Data Scientist in 6 months\ud83e\udd73\" title=\"Play video &quot;Data Analyst to Data Scientist in 6 months\ud83e\udd73&quot;\" target=\"_blank\" rel=\"noopener\">https:\/\/www.youtube.com\/watch?v=ibz66eWxl-4&#038;list=PL_9uM5be2amoyP0j3E_J8MmRAtlKlrQs2<\/a><noscript>Video can&#8217;t be loaded because JavaScript is disabled: <a href=\"https:\/\/www.youtube.com\/watch?v=ibz66eWxl-4&#038;list=PL_9uM5be2amoyP0j3E_J8MmRAtlKlrQs2\" title=\"Data Analyst to Data Scientist in 6 months\ud83e\udd73\" target=\"_blank\" rel=\"noopener\">Data Analyst to Data Scientist in 6 months\ud83e\udd73 (https:\/\/www.youtube.com\/watch?v=ibz66eWxl-4&#038;list=PL_9uM5be2amoyP0j3E_J8MmRAtlKlrQs2)<\/a><\/noscript><\/div>\n<\/div><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h2>\n\n\n\n<p>Moving from marketing analytics to data science requires developing programming, statistical, and machine learning skills. Your experience with customer behaviour, campaign performance, and conversion analysis provides useful business knowledge.<\/p>\n\n\n\n<p>Start with Python and SQL, strengthen your statistical foundations, and learn machine learning. Build practical projects, evaluate your models, and document your findings. Consistent practice can help you move from reporting marketing performance to developing predictive solutions.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>FAQs<\/strong><\/h2>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list \">\n<div id=\"faq-question-1793456173830\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>1. Can a marketing analyst become a data scientist?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes. Marketing analysts can transition by learning Python, SQL, statistics, and machine learning. Their understanding of customer behaviour and campaign data provides useful business context.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1793456182678\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>2. Is marketing analytics experience useful for data science?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes. Experience with customer segmentation, campaign measurement, and conversion analysis can help when developing predictive models.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1793456192960\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>3. Which programming languages should marketing analysts learn first?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Start with Python for data analysis and machine learning. Learn SQL to retrieve and combine information from databases.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1793456201564\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>4. Can marketing analysts become data scientists without a computer science degree?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes. Requirements vary by employer. Technical skills, practical projects, statistical knowledge, and relevant experience can strengthen an application.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1793456213179\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>5. What projects should marketing analysts build?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Customer churn prediction, campaign response prediction, and customer segmentation are relevant projects that demonstrate practical machine learning skills.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1793456224736\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>6. Is machine learning necessary for becoming a data scientist?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Machine learning is an important skill for many data science roles. Start with regression, classification, and clustering before exploring advanced techniques.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1793456235526\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>7. Which jobs can marketing analysts explore after learning data science?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Consider Junior Data Scientist, Marketing Data Scientist, Customer Analytics Specialist, Predictive Analytics Analyst, and Marketing Data Science Analyst positions.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Marketing analysts work with campaign performance, customer behaviour, conversion rates, and market trends. This experience provides a foundation for data science. By learning programming, statistics, and machine learning, marketing analysts can move beyond reporting campaign results to predicting customer behaviour and identifying business opportunities. TL;DR Quick Answer A marketing analyst can become a data scientist [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":145393,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[13,745,16],"tags":[],"views":"24","authorinfo":{"name":"HCL GUVI","url":"https:\/\/www.guvi.in\/blog\/author\/guvipr\/"},"thumbnailURL":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/10\/Marketing-Analyst-to-Data-Scientist-Skills-and-Career-Path-300x117.webp","_links":{"self":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/145373"}],"collection":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/comments?post=145373"}],"version-history":[{"count":5,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/145373\/revisions"}],"predecessor-version":[{"id":145592,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/145373\/revisions\/145592"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media\/145393"}],"wp:attachment":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media?parent=145373"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/categories?post=145373"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/tags?post=145373"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}