{"id":145090,"date":"2026-11-02T15:24:43","date_gmt":"2026-11-02T09:54:43","guid":{"rendered":"https:\/\/www.guvi.in\/blog\/?p=145090"},"modified":"2026-11-02T15:24:46","modified_gmt":"2026-11-02T09:54:46","slug":"statistics-for-data-analysts-to-data-science","status":"publish","type":"post","link":"https:\/\/www.guvi.in\/blog\/statistics-for-data-analysts-to-data-science\/","title":{"rendered":"Statistics for Analysts Moving to Data Science: What to Learn First"},"content":{"rendered":"\n<p>Moving from analytics to data science is a practical career step when you already work with reports, dashboards, or business questions. You do not need to master every statistical method at once. Start with concepts that help you interpret evidence, measure uncertainty, test ideas, and explain results.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>TL;DR<\/strong><\/h2>\n\n\n\n<ul>\n<li>Learn descriptive statistics and probability first.<\/li>\n\n\n\n<li>Understand sampling, distributions, and confidence intervals.<\/li>\n\n\n\n<li>Practise hypothesis testing, correlation, and regression.<\/li>\n\n\n\n<li>Apply concepts using Python and real datasets.<\/li>\n\n\n\n<li>Build projects that demonstrate analytical reasoning.<\/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 href=\"https:\/\/www.edept.co\/blogs\/the-ultimate-journey-of-data-analyst-to-data-scientist\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Analysts moving into data science<\/a> should learn descriptive statistics, probability, sampling, statistical inference, hypothesis testing, correlation, and regression first. These concepts help you summarise data, assess uncertainty, and explain relationships.<\/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=statistics-for-data-analysts-to-data-science\" 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>Begin with Descriptive Statistics<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Learn the core measures<\/strong><\/h3>\n\n\n\n<ul>\n<li><strong>Mean:<\/strong> The arithmetic average.<\/li>\n\n\n\n<li><strong>Median:<\/strong> The middle value after sorting observations.<\/li>\n\n\n\n<li><strong>Mode:<\/strong> The most frequent value.<\/li>\n\n\n\n<li><strong>Range:<\/strong> The difference between the highest and lowest values.<\/li>\n\n\n\n<li><strong>Standard deviation:<\/strong> A measure of how widely values vary around the mean.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Practise with visual summaries<\/strong><\/h3>\n\n\n\n<ul>\n<li>Use <a href=\"https:\/\/www.guvi.in\/hub\/python-for-data-science\/exploring-data-with-scatter-plots-and-histograms\/\" target=\"_blank\" rel=\"noreferrer noopener\">histograms<\/a> to understand distributions.<\/li>\n\n\n\n<li>Use box plots to examine spread and potential outliers.<\/li>\n\n\n\n<li>Use scatter plots to explore relationships between numerical variables.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Understand Probability and Distributions<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Learn probability basics<\/strong><\/h3>\n\n\n\n<p>Study events, <a href=\"https:\/\/skillodisha.guvi.in\/hub\/artificial-intelligence-tutorial\/probability-and-conditional-probability\/\" target=\"_blank\" rel=\"noreferrer noopener\">conditional probability<\/a>, independence, and <a href=\"https:\/\/www.guvi.in\/blog\/bayes-theorem-in-machine-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">Bayes\u2019 theorem<\/a>. Probability provides a mathematical framework for reasoning about uncertain outcomes.<\/p>\n\n\n\n<p>Conditional probability measures how likely an event is when another event is already known. For example, an analyst might investigate whether customers who use a product feature are more likely to renew their subscriptions.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Recognise common distributions<\/strong><\/h3>\n\n\n\n<p>Distributions describe how values or events are spread across possible outcomes. Learn the purpose of common distributions before memorising their formulas.<\/p>\n\n\n\n<ul>\n<li><strong>Normal distribution:<\/strong> Approximately bell-shaped measurements.<\/li>\n\n\n\n<li><strong>Binomial distribution:<\/strong> Successes across a fixed number of trials.<\/li>\n\n\n\n<li><strong>Poisson distribution:<\/strong> Event counts within a defined interval, when assumptions fit.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Learn Sampling and Statistical Inference<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Understand samples and bias<\/strong><\/h3>\n\n\n\n<p>A population is the entire group you want to understand, while a sample is the subset you actually observe. <a href=\"https:\/\/www.guvi.in\/blog\/what-is-statistical-analysis\/\" target=\"_blank\" rel=\"noreferrer noopener\">Statistical<\/a> inference uses sample information to draw conclusions about a wider population.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.guvi.in\/blog\/what-is-sampling-techniques-in-statistics\/\" target=\"_blank\" rel=\"noreferrer noopener\">Sampling bias<\/a> occurs when the selection process systematically excludes certain people or outcomes. For example, surveying only highly active customers may produce results that do not represent your entire customer base.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Interpret confidence intervals<\/strong><\/h3>\n\n\n\n<p>A standard error describes the variability of a sample statistic across repeated samples. A <a href=\"https:\/\/www.guvi.in\/blog\/confidence-intervals-explained\/\" target=\"_blank\" rel=\"noreferrer noopener\">confidence interval<\/a> provides a range using a method designed to capture the true population parameter at a stated long-run rate.<\/p>\n\n\n\n<p>A 95% confidence level does not mean a particular calculated interval has a 95% probability of containing a fixed parameter. Instead, the method captures the true parameter in approximately 95% of repeated samples when its assumptions hold.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Study Hypothesis Testing<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Learn the essential terms<\/strong><\/h3>\n\n\n\n<p>Understand null hypotheses, alternative hypotheses, significance levels, p-values, and statistical power. These concepts help you evaluate whether observed results provide evidence against a baseline assumption.<\/p>\n\n\n\n<p>A <a href=\"https:\/\/www.guvi.in\/blog\/understanding-p-values-in-machine-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">p-value<\/a> measures how surprising the observed result, or a more extreme result, would be if the null <a href=\"https:\/\/www.guvi.in\/blog\/hypothesis-testing-for-data-science\/\" target=\"_blank\" rel=\"noreferrer noopener\">hypothesis<\/a> and model assumptions were true. It does not tell you the probability that the null hypothesis itself is true.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Apply testing to business questions<\/strong><\/h3>\n\n\n\n<p>Imagine a product team changes a checkout page and wants to know whether the new design improves conversion. Define the outcome, compare suitable groups, and select a test that matches the data and study design.<\/p>\n\n\n\n<ul>\n<li>Define the question before analysing results.<\/li>\n\n\n\n<li>Select an appropriate statistical test.<\/li>\n\n\n\n<li>Consider effect size alongside the p-value.<\/li>\n\n\n\n<li>Check whether the sample can detect a meaningful difference.<\/li>\n<\/ul>\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 \/> \nA statistically significant result can still be misleading when the study design is weak, the data is biased, or many comparisons are tested without suitable adjustments. Statistical conclusions depend on assumptions, sampling methods, and data quality.\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Learn Correlation and Regression<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Separate association from causation<\/strong><\/h3>\n\n\n\n<p>A positive <a href=\"https:\/\/www.guvi.in\/blog\/correlation-in-data-science\/\" target=\"_blank\" rel=\"noreferrer noopener\">correlation<\/a> means two variables tend to increase together, while a negative correlation means one tends to decrease as the other increases. Correlation describes an association but does not prove that one variable causes another.<\/p>\n\n\n\n<p>A third factor, selection effects, or chance may explain the relationship. For example, advertising and sales might rise together during a holiday period without advertising alone explaining the increase.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Start with linear and logistic regression<\/strong><\/h3>\n\n\n\n<p><a href=\"https:\/\/www.guvi.in\/blog\/linear-regression-in-data-science\/\" target=\"_blank\" rel=\"noreferrer noopener\">Linear regression<\/a> estimates continuous outcomes, such as monthly revenue or delivery time. Learn coefficients, residuals, model fit, and assumptions before interpreting its predictions.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.guvi.in\/hub\/machine-learning-tutorial\/logistic-regression-explained\/\" target=\"_blank\" rel=\"noreferrer noopener\">Logistic regression<\/a> is commonly used for binary outcomes, such as whether a customer renews or leaves. Practise evaluating model performance and explaining predictions rather than relying on a single score.<\/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=statistics-for-data-analysts-to-data-science\" 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>Apply Statistics Using Python<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Learn the essential libraries<\/strong><\/h3>\n\n\n\n<p>Python helps analysts apply statistical concepts to larger datasets. Common <a href=\"https:\/\/www.guvi.in\/blog\/python-libraries-explained\/\" target=\"_blank\" rel=\"noreferrer noopener\">libraries<\/a> include:<\/p>\n\n\n\n<ul>\n<li><strong>pandas:<\/strong> Organises and analyses tabular data.<\/li>\n\n\n\n<li><strong>NumPy:<\/strong> Supports numerical calculations.<\/li>\n\n\n\n<li><strong>SciPy:<\/strong> Provides scientific and statistical functions.<\/li>\n\n\n\n<li><strong>statsmodels:<\/strong> Supports statistical modelling.<\/li>\n\n\n\n<li><strong>scikit-learn:<\/strong> Builds and evaluates machine learning models.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Practise with real datasets<\/strong><\/h3>\n\n\n\n<p>Choose a dataset involving customer activity, sales, website traffic, or product performance. Calculate <a href=\"https:\/\/www.guvi.in\/blog\/descriptive-statistics-types-applications\/\" target=\"_blank\" rel=\"noreferrer noopener\">descriptive statistics<\/a>, visualise distributions, investigate relationships, and test a clear hypothesis.<\/p>\n\n\n\n<p>Record your assumptions and explain what the findings mean for the original question. Include limitations so readers understand what the evidence supports and what remains uncertain.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Follow a Structured Learning Plan<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Build your foundation progressively<\/strong><\/h3>\n\n\n\n<p>Start with descriptive statistics and probability before moving to sampling and inference. Study hypothesis testing next, followed by correlation, regression, and experiment design.<\/p>\n\n\n\n<p>Set manageable weekly goals and practise each concept before moving forward. Revisiting earlier topics through small projects helps you retain knowledge and connect different statistical methods.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Develop a project portfolio<\/strong><\/h3>\n\n\n\n<p>A strong <a href=\"https:\/\/www.guvi.in\/blog\/data-scientist-resume-and-portfolio-guide\/\" target=\"_blank\" rel=\"noreferrer noopener\">portfolio<\/a> demonstrates how you approach a problem, select a method, and interpret results. Include the business question, dataset, methodology, visualisations, findings, and limitations.<\/p>\n\n\n\n<p>Choose <a href=\"https:\/\/www.guvi.in\/blog\/data-science-projects\/\" target=\"_blank\" rel=\"noreferrer noopener\">projects<\/a> that answer realistic business questions rather than simply displaying charts. Explain your reasoning in plain language so recruiters and non-technical stakeholders can understand your contribution.<\/p>\n\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=K4xNc9u7IY4\" class=\"lazy-load-youtube preview-lazyload preview-youtube\" data-video-title=\"Data Science and Analytics | HCL GUVI\" title=\"Play video &quot;Data Science and Analytics | HCL GUVI&quot;\" target=\"_blank\" rel=\"noopener\">https:\/\/www.youtube.com\/watch?v=K4xNc9u7IY4<\/a><noscript>Video can&#8217;t be loaded because JavaScript is disabled: <a href=\"https:\/\/www.youtube.com\/watch?v=K4xNc9u7IY4\" title=\"Data Science and Analytics | HCL GUVI\" target=\"_blank\" rel=\"noopener\">Data Science and Analytics | HCL GUVI (https:\/\/www.youtube.com\/watch?v=K4xNc9u7IY4)<\/a><\/noscript><\/div>\n<\/div><\/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=statistics-for-data-analysts-to-data-science\" 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>Conclusion<\/strong><\/h2>\n\n\n\n<p>Start with descriptive statistics, probability, sampling, inference, hypothesis testing, correlation, and regression. These foundations help you describe data, evaluate uncertainty, and build models responsibly.<\/p>\n\n\n\n<p>Practise with datasets, apply concepts using Python, and explain results clearly so teams can make informed decisions. Prioritise reasoning over memorising formulas, and build a portfolio that connects analysis to practical outcomes.<\/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-1793260047602\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>1. Which statistics topic should analysts learn first?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Start with descriptive statistics, including mean, median, variance, standard deviation, and visual summaries. These concepts help you understand a dataset before applying advanced methods.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1793260054136\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>2. Is probability necessary for data science?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes, because probability supports reasoning about uncertainty, distributions, prediction, experiments, and risk. It also provides a foundation for statistical inference and machine learning.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1793260061760\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>3. Do I need advanced mathematics to start learning statistics?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>No, because you can begin with basic arithmetic, algebra, and conceptual explanations. Learn more advanced mathematics when a particular statistical method requires it.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1793260069216\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>4. What is the difference between correlation and regression?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Correlation describes the direction and strength of an association between variables. Regression models an outcome using predictors and can support explanation or prediction, depending on its design and assumptions.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1793260084136\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>5. How are p-values used in hypothesis testing?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>A p-value indicates how compatible an observed result is with a null hypothesis and its assumptions. It does not establish that the null hypothesis is true or measure the size of an effect.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1793260099671\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>6. Should analysts learn statistics before Python?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Learn the concepts first, then practise them using Python as soon as possible. Studying both together connects statistical reasoning to real datasets and makes technical output easier to interpret.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1793260114977\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>7. How can I demonstrate statistical skills in a portfolio?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Create projects with clear questions, suitable methods, visualisations, interpretations, and limitations. Explain what the evidence supports instead of presenting charts or model scores without context.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Moving from analytics to data science is a practical career step when you already work with reports, dashboards, or business questions. You do not need to master every statistical method at once. Start with concepts that help you interpret evidence, measure uncertainty, test ideas, and explain results. TL;DR Quick Answer Analysts moving into data science [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":145092,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16,13,745],"tags":[],"views":"23","authorinfo":{"name":"HCL GUVI","url":"https:\/\/www.guvi.in\/blog\/author\/guvipr\/"},"thumbnailURL":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/10\/Statistics-for-Analysts-Moving-to-Data-Science-What-to-Learn-First-300x116.webp","_links":{"self":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/145090"}],"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=145090"}],"version-history":[{"count":3,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/145090\/revisions"}],"predecessor-version":[{"id":145495,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/145090\/revisions\/145495"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media\/145092"}],"wp:attachment":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media?parent=145090"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/categories?post=145090"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/tags?post=145090"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}