{"id":13234,"date":"2026-05-09T16:31:02","date_gmt":"2026-05-09T11:01:02","guid":{"rendered":"https:\/\/ivyproschool.com\/blog\/?p=13234"},"modified":"2026-05-09T18:06:47","modified_gmt":"2026-05-09T12:36:47","slug":"python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first","status":"publish","type":"post","link":"https:\/\/ivyproschool.com\/blog\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\/","title":{"rendered":"Python vs SQL for Data Analytics Beginners: Which One Should You Learn First?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"13234\" class=\"elementor elementor-13234\">\n\t\t\t\t\t\t<div class=\"elementor-inner\">\n\t\t\t\t<div class=\"elementor-section-wrap\">\n\t\t\t\t\t\t\t\t\t<section class=\"has_ma_el_bg_slider elementor-section elementor-top-section elementor-element elementor-element-283ee7f9 elementor-section-boxed elementor-section-height-default elementor-section-height-default jltma-glass-effect-no\" data-id=\"283ee7f9\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t\t\t<div class=\"elementor-row\">\n\t\t\t\t\t<div class=\"has_ma_el_bg_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f201c10 jltma-glass-effect-no\" data-id=\"f201c10\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-column-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0332568 jltma-glass-effect-no elementor-widget elementor-widget-image\" data-id=\"0332568\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-image\">\n\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1080\" height=\"608\" src=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/Python-vs-SQL.jpg-1080x608.jpeg\" class=\"attachment-large size-large wp-image-13241\" alt=\"\" srcset=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/Python-vs-SQL.jpg-1080x608.jpeg 1080w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/Python-vs-SQL.jpg-300x169.jpeg 300w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/Python-vs-SQL.jpg-150x84.jpeg 150w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/Python-vs-SQL.jpg-768x432.jpeg 768w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/Python-vs-SQL.jpg-1536x864.jpeg 1536w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/Python-vs-SQL.jpg.jpeg 1920w\" sizes=\"auto, (max-width: 1080px) 100vw, 1080px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c8cef6f uael-heading-align-left jltma-glass-effect-no elementor-widget elementor-widget-ma-table-of-contents\" data-id=\"c8cef6f\" data-element_type=\"widget\" data-widget_type=\"ma-table-of-contents.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"jltma-toc-main-wrapper\" data-jltma-headings=\"h2\">\n\t\t\t<div class=\"jltma-toc-wrapper\">\n\t\t\t\t<div class=\"jltma-toc-header\">\n\t\t\t\t\t<span class=\"jltma-toc-heading elementor-inline-editing\" data-elementor-setting-key=\"heading_title\" data-elementor-inline-editing-toolbar=\"basic\">Table of Contents<\/span>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<div class=\"jltma-toc-toggle-content\">\n\t\t\t\t\t<div class=\"jltma-toc-content-wrapper\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ul data-toc-headings=\"headings\" class=\"jltma-toc-list jltma-toc-list-disc\" data-jltma-scroll=\"\"><\/ul>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"jltma-toc-empty-note\">\n\t\t\t\t\t<span>Add a header to begin generating the table of contents<\/span>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-79894613 jltma-glass-effect-no elementor-widget elementor-widget-text-editor\" data-id=\"79894613\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-text-editor elementor-clearfix\">\n\t\t\t\t<p><span style=\"font-weight: 400;\">If you are planning to start a career in data analytics, one of the first questions you will face is: should I learn Python or SQL first?<\/span><\/p><p><span style=\"font-weight: 400;\">This confusion is very common. Many beginners hear that <a href=\"https:\/\/ivyproschool.com\/aihelpcenter\/python-basics\/merge-csv-files\">Python<\/a> is powerful and used in data science, machine learning, automation, and AI. At the same time, they also hear that <a href=\"https:\/\/ivyproschool.com\/aihelpcenter\/genai-llm\/connecting-llms-to-sql\">SQL<\/a> is essential because most business data is stored in databases.<\/span><\/p><p><span style=\"font-weight: 400;\">So, when it comes to <\/span><b>Python vs SQL for <a href=\"https:\/\/ivyproschool.com\/courses\/data-analytics-and-generative-ai-course\">data analytics<\/a> beginners<\/b><span style=\"font-weight: 400;\">, which one is more important? Which one is easier? Which one helps you get a job faster? And most importantly, which one should you learn first?<\/span><\/p><p><span style=\"font-weight: 400;\">The honest answer is simple: if you are starting in data analytics, learn SQL first, then Python.<\/span><\/p><p><span style=\"font-weight: 400;\">SQL helps you access and extract data. Python helps you analyze, clean, automate, and extend your work further. Both are valuable, but they serve different purposes. A strong data analyst should ideally know both.<\/span><\/p><p><span style=\"font-weight: 400;\">This blog will help you understand the difference between <a href=\"https:\/\/ivyproschool.com\/aihelpcenter\/python-basics\/merge-csv-files\">Python<\/a> and SQL, their roles in data analytics, how difficult they are, where each one is used, and the best learning path for beginners.<\/span><\/p><h2><b>What Is SQL?<\/b><\/h2><p><span style=\"font-weight: 400;\">SQL stands for Structured Query Language. It is used to communicate with databases.<\/span><\/p><p><span style=\"font-weight: 400;\">In most companies, data is stored in structured databases. These databases may contain customer details, sales transactions, employee records, product information, marketing campaign data, inventory details, payment records, and many other types of business information.<\/span><\/p><p><span style=\"font-weight: 400;\"><a href=\"https:\/\/ivyproschool.com\/aihelpcenter\/genai-llm\/connecting-llms-to-sql\">SQL<\/a> helps you ask questions from these databases.<\/span><\/p><p><span style=\"font-weight: 400;\">For example:<\/span><\/p><ul><li><span style=\"font-weight: 400;\">Which products sold the most last month?<\/span><\/li><li><span style=\"font-weight: 400;\">Which customers have not purchased in the last 90 days?<\/span><\/li><li><span style=\"font-weight: 400;\">What is the total revenue by region?<\/span><\/li><li><span style=\"font-weight: 400;\">Which salespeople achieved their targets?<\/span><\/li><li><span style=\"font-weight: 400;\">How many employees left the company this year?<\/span><\/li><li><span style=\"font-weight: 400;\">Which orders were delayed?<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">SQL allows you to filter, group, join, and summarize data directly from the database. This is why SQL is one of the most important skills for data analytics beginners.<\/span><\/p><p><span style=\"font-weight: 400;\">A simple SQL query may look like this:<\/span><\/p><p><span style=\"font-weight: 400;\">SELECT region, SUM(sales) AS total_sales<\/span><\/p><p><span style=\"font-weight: 400;\">FROM orders<\/span><\/p><p><span style=\"font-weight: 400;\">GROUP BY region;<\/span><\/p><p><span style=\"font-weight: 400;\">This query tells the database to calculate total sales for each region. Even if you are new to coding, SQL is quite readable because it uses English-like commands such as SELECT, FROM, WHERE, GROUP BY, and ORDER BY.<\/span><\/p><h2><b>What Is Python?<\/b><\/h2><p><span style=\"font-weight: 400;\">Python is a general-purpose programming language. It is used in many fields, including web development, automation, <a href=\"https:\/\/ivyproschool.com\/courses\/data-analytics-and-generative-ai-course\">data analytics<\/a>, <a href=\"https:\/\/ivyproschool.com\/courses\/data-science-and-ml-course\">data science<\/a>, <a href=\"https:\/\/ivyproschool.com\/courses\/ai-machine-learning-course\">machine learning<\/a>, <a href=\"https:\/\/ivyproschool.com\/courses\/iit-generative-ai-course\">AI<\/a>, finance, and software development.<\/span><\/p><p><span style=\"font-weight: 400;\">In data analytics, <a href=\"https:\/\/ivyproschool.com\/aihelpcenter\/python-basics\/excel-automation-openpyxl\">Python<\/a> is mainly used to clean, analyze, manipulate, visualize, and automate data.<\/span><\/p><p><span style=\"font-weight: 400;\">Python becomes especially powerful because of libraries such as:<\/span><\/p><ul><li><span style=\"font-weight: 400;\">Pandas for data analysis<\/span><\/li><li><span style=\"font-weight: 400;\">NumPy for numerical operations<\/span><\/li><li><span style=\"font-weight: 400;\">Matplotlib and Seaborn for visualization<\/span><\/li><li><span style=\"font-weight: 400;\">OpenPyXL for Excel automation<\/span><\/li><li><span style=\"font-weight: 400;\">Scikit-learn for machine learning<\/span><\/li><li><span style=\"font-weight: 400;\">Statsmodels for statistical analysis<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">Python can read data from Excel files, CSV files, databases, APIs, websites, and cloud platforms. Once the data is loaded, Python can help you clean it, transform it, analyze it, and create charts or reports.<\/span><\/p><p><span style=\"font-weight: 400;\">A simple Python example may look like this:<\/span><\/p><p><span style=\"font-weight: 400;\">import pandas as pd<\/span><\/p><p><span style=\"font-weight: 400;\">df = pd.read_csv(&#8220;sales_data.csv&#8221;)<\/span><\/p><p><span style=\"font-weight: 400;\">region_sales = df.groupby(&#8220;Region&#8221;)[&#8220;Sales&#8221;].sum()<\/span><\/p><p><span style=\"font-weight: 400;\">print(region_sales)<\/span><\/p><p><span style=\"font-weight: 400;\">This code reads a sales file and calculates total sales by region.<\/span><\/p><p><span style=\"font-weight: 400;\">Compared to SQL, Python is broader and more flexible. But for beginners, it may also feel slightly more complex because it involves programming concepts such as variables, functions, loops, libraries, and data structures.<\/span><\/p><h2><b>Python vs SQL for Data Analytics Beginners: The Core Difference<\/b><\/h2><p><span style=\"font-weight: 400;\">The easiest way to understand the difference is this:<\/span><\/p><p><span style=\"font-weight: 400;\">SQL is mainly used to get data from databases.<\/span><\/p><p><span style=\"font-weight: 400;\">Python is mainly used to work with data after you get it.<\/span><\/p><p><span style=\"font-weight: 400;\">Think of SQL as the tool you use to enter the data warehouse and pull the required information. Think of Python as the tool you use to clean, analyze, automate, and model that information.<\/span><\/p><p><span style=\"font-weight: 400;\">For example, imagine a company wants to analyze customer churn.<\/span><\/p><p><span style=\"font-weight: 400;\">SQL can help you extract customer records, transactions, subscriptions, and payment history from the database.<\/span><\/p><p><span style=\"font-weight: 400;\">Python can help you clean the extracted data, create churn indicators, build visualizations, run statistical analysis, and even create a predictive model.<\/span><\/p><p><span style=\"font-weight: 400;\">Both tools are connected. SQL gives you access to structured data. Python gives you flexibility to perform deeper analysis.<\/span><\/p><p><span style=\"font-weight: 400;\">That is why the debate of <\/span><b>Python vs SQL for data analytics beginners<\/b><span style=\"font-weight: 400;\"> should not be treated as an either-or decision. It is better to understand which one to learn first and how both fit into your data analytics journey.<\/span><\/p><p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-13250\" src=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/4.jpg-4-300x75.jpeg\" alt=\"\" width=\"300\" height=\"75\" srcset=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/4.jpg-4-300x75.jpeg 300w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/4.jpg-4-1080x271.jpeg 1080w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/4.jpg-4-150x38.jpeg 150w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/4.jpg-4-768x192.jpeg 768w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/4.jpg-4-1536x385.jpeg 1536w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/4.jpg-4.jpeg 1920w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/p><h2><b>Why SQL Is Important for Data Analytics Beginners<\/b><\/h2><p><span style=\"font-weight: 400;\">SQL is important because most business data lives in databases. Even if you know Excel, Power BI, or Python, you will often need SQL to extract the right data.<\/span><\/p><p><span style=\"font-weight: 400;\">Here are the main reasons beginners should learn SQL.<\/span><\/p><h2><b>1. SQL Helps You Access Real Business Data<\/b><\/h2><p><span style=\"font-weight: 400;\">In real companies, data is rarely available as a clean Excel file. It is usually stored in systems such as CRM, ERP, HRMS, accounting software, e-commerce platforms, banking systems, and cloud databases.<\/span><\/p><p><span style=\"font-weight: 400;\">SQL helps you pull the data you need from these systems.<\/span><\/p><p><span style=\"font-weight: 400;\">For example, a sales analyst may need customer-wise revenue from a database. A marketing analyst may need campaign leads and conversion data. A finance analyst may need invoice and payment details. SQL makes this possible.<\/span><\/p><p><span style=\"font-weight: 400;\">Without SQL, you may depend on someone else to extract data for you. With SQL, you become more independent.<\/span><\/p><h2><b>2. SQL Is Easier to Start With<\/b><\/h2><p><span style=\"font-weight: 400;\">For most beginners, SQL is easier than Python because the syntax is more direct. You do not need to understand full programming logic before writing useful SQL queries.<\/span><\/p><ul><li><span style=\"font-weight: 400;\">Basic SQL commands are simple:<\/span><\/li><li><span style=\"font-weight: 400;\">SELECT * FROM customers;<\/span><\/li><li><span style=\"font-weight: 400;\">SELECT customer_name, city<\/span><\/li><li><span style=\"font-weight: 400;\">FROM customers<\/span><\/li><li><span style=\"font-weight: 400;\">WHERE city = &#8216;Kolkata&#8217;;<\/span><\/li><li><span style=\"font-weight: 400;\">SELECT product_category, COUNT(*) AS total_orders<\/span><\/li><li><span style=\"font-weight: 400;\">FROM orders<\/span><\/li><li><span style=\"font-weight: 400;\">GROUP BY product_category;<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">These queries are readable even for non-programmers.<\/span><\/p><p><span style=\"font-weight: 400;\">This makes SQL a strong starting point for beginners who are coming from business, commerce, finance, HR, marketing, operations, or non-technical backgrounds.<\/span><\/p><h2><b>3. SQL Is Used in Almost Every Data Analyst Job<\/b><\/h2><p><span style=\"font-weight: 400;\">If you look at most data analyst job descriptions, SQL is usually one of the core requirements. Employers expect analysts to extract, filter, join, and aggregate data from databases.<\/span><\/p><p><span style=\"font-weight: 400;\">Common SQL tasks in data analyst roles include:<\/span><\/p><ul><li><span style=\"font-weight: 400;\">Writing queries<\/span><\/li><li><span style=\"font-weight: 400;\">Joining multiple tables<\/span><\/li><li><span style=\"font-weight: 400;\">Creating summary reports<\/span><\/li><li><span style=\"font-weight: 400;\">Filtering business data<\/span><\/li><li><span style=\"font-weight: 400;\">Cleaning data at the database level<\/span><\/li><li><span style=\"font-weight: 400;\">Creating views<\/span><\/li><li><span style=\"font-weight: 400;\">Working with date functions<\/span><\/li><li><span style=\"font-weight: 400;\">Using window functions<\/span><\/li><li><span style=\"font-weight: 400;\">Finding duplicates<\/span><\/li><li><span style=\"font-weight: 400;\">Preparing datasets for dashboards<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">SQL is not just a beginner tool. It is used daily by analysts, business intelligence professionals, data engineers, product analysts, and data scientists.<\/span><\/p><h2><b>4. SQL Builds Strong Data Thinking<\/b><\/h2><p><span style=\"font-weight: 400;\">SQL teaches you how structured data works. You learn about tables, rows, columns, keys, relationships, joins, and aggregations.<\/span><\/p><p><span style=\"font-weight: 400;\">This is extremely useful for understanding real-world business data.<\/span><\/p><p><span style=\"font-weight: 400;\">For example, a customer table may connect with an order table. An order table may connect with a product table. A product table may connect with a category table. SQL teaches you how to combine these tables logically.<\/span><\/p><p><span style=\"font-weight: 400;\">This understanding helps later when you learn Power BI, Tableau, Python, or data modeling.<\/span><\/p><p><span style=\"font-weight: 400;\"> <img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-13251\" src=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/5.jpg-4-300x75.jpeg\" alt=\"\" width=\"300\" height=\"75\" srcset=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/5.jpg-4-300x75.jpeg 300w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/5.jpg-4-1080x271.jpeg 1080w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/5.jpg-4-150x38.jpeg 150w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/5.jpg-4-768x192.jpeg 768w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/5.jpg-4-1536x385.jpeg 1536w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/5.jpg-4.jpeg 1920w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/span><\/p><h2><b>Why Python Is Important for Data Analytics Beginners<\/b><\/h2><p><span style=\"font-weight: 400;\">If SQL is the foundation for accessing data, Python is the tool that gives you deeper analytical power. It helps when data becomes large, messy, repetitive, or complex.<\/span><\/p><p><span style=\"font-weight: 400;\">Here are the main reasons Python matters for beginners.<\/span><\/p><h2><b>1. Python Is Flexible<\/b><\/h2><p><span style=\"font-weight: 400;\">Python can work with many types of data sources. You can use it with Excel files, CSV files, databases, APIs, text files, web data, and cloud platforms.<\/span><\/p><p><span style=\"font-weight: 400;\">This flexibility makes Python useful in many scenarios.<\/span><\/p><p><span style=\"font-weight: 400;\">For example, you can use Python to:<\/span><\/p><ul><li><span style=\"font-weight: 400;\">Combine multiple Excel files<\/span><\/li><li><span style=\"font-weight: 400;\">Clean messy customer data<\/span><\/li><li><span style=\"font-weight: 400;\">Create automated reports<\/span><\/li><li><span style=\"font-weight: 400;\">Analyze sales trends<\/span><\/li><li><span style=\"font-weight: 400;\">Generate charts<\/span><\/li><li><span style=\"font-weight: 400;\">Build forecasting models<\/span><\/li><li><span style=\"font-weight: 400;\">Scrape publicly available web data<\/span><\/li><li><span style=\"font-weight: 400;\">Work with APIs<\/span><\/li><li><span style=\"font-weight: 400;\">Prepare datasets for machine learning<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">Python is not limited to databases. It gives you more freedom to work with different kinds of data.<\/span><\/p><h2><b>2. Python Is Powerful for Data Cleaning<\/b><\/h2><p><span style=\"font-weight: 400;\">Data cleaning is one of the most time-consuming parts of analytics. Real-world data often has missing values, duplicate rows, inconsistent spellings, incorrect formats, extra spaces, and wrong data types.<\/span><\/p><p><span style=\"font-weight: 400;\">Python\u2019s Pandas library is excellent for cleaning such data.<\/span><\/p><p><span style=\"font-weight: 400;\">You can use Python to:<\/span><\/p><ul><li><span style=\"font-weight: 400;\">Remove duplicates<\/span><\/li><li><span style=\"font-weight: 400;\">Fill missing values<\/span><\/li><li><span style=\"font-weight: 400;\">Convert date formats<\/span><\/li><li><span style=\"font-weight: 400;\">Replace incorrect values<\/span><\/li><li><span style=\"font-weight: 400;\">Rename columns<\/span><\/li><li><span style=\"font-weight: 400;\">Merge datasets<\/span><\/li><li><span style=\"font-weight: 400;\">Split columns<\/span><\/li><li><span style=\"font-weight: 400;\">Create new calculated columns<\/span><\/li><li><span style=\"font-weight: 400;\">Filter records<\/span><\/li><li><span style=\"font-weight: 400;\">Reshape data<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">For example:<\/span><\/p><p><span style=\"font-weight: 400;\">df[&#8220;Order Date&#8221;] = pd.to_datetime(df[&#8220;Order Date&#8221;])<\/span><\/p><p><span style=\"font-weight: 400;\">df = df.drop_duplicates()<\/span><\/p><p><span style=\"font-weight: 400;\">df[&#8220;City&#8221;] = df[&#8220;City&#8221;].str.strip().str.title()<\/span><\/p><p><span style=\"font-weight: 400;\">This type of work is possible in Excel and SQL too, but Python is especially useful when the dataset is large or when the same cleaning process must be repeated again and again.<\/span><\/p><h2><b>3. Python Helps with Automation<\/b><\/h2><p><span style=\"font-weight: 400;\">One of Python\u2019s biggest advantages is automation. Many working professionals spend hours preparing the same reports every week or month. Python can automate such repetitive work.<\/span><\/p><p><span style=\"font-weight: 400;\">For example, Python can:<\/span><\/p><ul><li><span style=\"font-weight: 400;\">Read multiple Excel files from a folder<\/span><\/li><li><span style=\"font-weight: 400;\">Clean and combine them<\/span><\/li><li><span style=\"font-weight: 400;\">Create summary tables<\/span><\/li><li><span style=\"font-weight: 400;\">Generate charts<\/span><\/li><li><span style=\"font-weight: 400;\">Export a final report<\/span><\/li><li><span style=\"font-weight: 400;\">Send automated emails<\/span><\/li><li><span style=\"font-weight: 400;\">Update dashboards<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">This is very useful for MIS analysts, finance professionals, HR analysts, sales analysts, and operations teams.<\/span><\/p><p><span style=\"font-weight: 400;\">A beginner who learns Python for automation can save hours of manual work.<\/span><\/p><h2><b>4. Python Opens the Door to Data Science and AI<\/b><\/h2><p><span style=\"font-weight: 400;\">If your long-term goal is data science, machine learning, AI, forecasting, or advanced analytics, Python becomes very important.<\/span><\/p><p><span style=\"font-weight: 400;\">Python is widely used for:<\/span><\/p><ul><li><a href=\"https:\/\/ivyproschool.com\/aihelpcenter\/machine-learning\/decision-tree-vs-random-forest\"><span style=\"font-weight: 400;\">Machine learning<\/span><\/a><\/li><li><span style=\"font-weight: 400;\">Predictive modeling<\/span><\/li><li><span style=\"font-weight: 400;\">Natural language processing<\/span><\/li><li><span style=\"font-weight: 400;\">Recommendation systems<\/span><\/li><li><span style=\"font-weight: 400;\">Forecasting<\/span><\/li><li><span style=\"font-weight: 400;\">Statistical modeling<\/span><\/li><li><span style=\"font-weight: 400;\">AI applications<\/span><\/li><li><span style=\"font-weight: 400;\">Data engineering scripts<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">SQL may help you extract data, but Python allows you to build advanced models and data-driven applications.<\/span><\/p><p><span style=\"font-weight: 400;\">This is why many learners start with SQL and then move to Python once they are comfortable with analytics basics.<\/span><\/p><h2><b>Python vs SQL: Which Is Easier for Beginners?<\/b><\/h2><p><span style=\"font-weight: 400;\">SQL is usually easier for complete beginners.<\/span><\/p><p><span style=\"font-weight: 400;\">The reason is simple. SQL is designed for one main purpose: working with structured database tables. Its commands are focused and readable. You can start writing useful queries quickly.<\/span><\/p><p><span style=\"font-weight: 400;\">Python is also beginner-friendly compared to many programming languages, but it is still a programming language. You need to understand concepts like:<\/span><\/p><ul><li><span style=\"font-weight: 400;\">Variables<\/span><\/li><li><span style=\"font-weight: 400;\">Data types<\/span><\/li><li><span style=\"font-weight: 400;\">Lists<\/span><\/li><li><span style=\"font-weight: 400;\">Dictionaries<\/span><\/li><li><span style=\"font-weight: 400;\">Loops<\/span><\/li><li><span style=\"font-weight: 400;\">Functions<\/span><\/li><li><span style=\"font-weight: 400;\">Libraries<\/span><\/li><li><span style=\"font-weight: 400;\">Errors<\/span><\/li><li><span style=\"font-weight: 400;\">DataFrames<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">For someone from a non-technical background, these concepts may take some time.<\/span><\/p><p><span style=\"font-weight: 400;\">However, Python becomes easier when taught with business examples instead of abstract programming exercises. For example, analyzing sales data is easier to understand than printing random patterns or solving pure coding puzzles.<\/span><\/p><p><span style=\"font-weight: 400;\">So, if we compare <\/span><b>Python vs SQL for data analytics beginners<\/b><span style=\"font-weight: 400;\"> purely on ease of learning, SQL wins in the first stage. But Python becomes manageable once you understand basic data logic.<\/span><\/p><h2><b>Python vs SQL: Which Is More Useful for Jobs?<\/b><\/h2><p><span style=\"font-weight: 400;\">Both are useful, but SQL is more commonly required for entry-level data analyst roles.<\/span><\/p><p><span style=\"font-weight: 400;\">Most companies expect data analysts to know SQL because analysts must often pull data from databases. Even if the company uses Power BI or Tableau, SQL is still valuable for preparing the data behind dashboards.<\/span><\/p><p><span style=\"font-weight: 400;\">Python is also very useful, especially for roles that involve automation, advanced analysis, large datasets, data science, or machine learning.<\/span><\/p><p><span style=\"font-weight: 400;\">Here is a practical way to understand it:<\/span><\/p><p><span style=\"font-weight: 400;\">For data analyst roles: SQL is essential, Python is a strong advantage.<\/span><\/p><p><span style=\"font-weight: 400;\">For business analyst roles: SQL is highly useful, Python may be optional.<\/span><\/p><p><span style=\"font-weight: 400;\">For BI analyst roles: SQL plus Power BI or Tableau is very important.<\/span><\/p><p><span style=\"font-weight: 400;\">For data scientist roles: Python is essential, SQL is also important.<\/span><\/p><p><span style=\"font-weight: 400;\">For analytics automation roles: Python is very useful.<\/span><\/p><p><span style=\"font-weight: 400;\">For product analytics roles: SQL is essential, Python is useful.<\/span><\/p><p><span style=\"font-weight: 400;\">For finance analytics or marketing analytics roles: SQL and Python both add value.<\/span><\/p><p><span style=\"font-weight: 400;\">So, if your goal is to get into analytics faster, start with SQL. If your goal is to move into advanced analytics or data science later, definitely learn Python after SQL.<\/span><\/p><h2><b>Python vs SQL: Which One Is Better for Data Cleaning?<\/b><\/h2><p><span style=\"font-weight: 400;\">Both can clean data, but they are used differently.<\/span><\/p><p><span style=\"font-weight: 400;\">SQL is useful for cleaning data inside databases. You can remove duplicates, handle null values, format text, filter wrong records, and create cleaned views.<\/span><\/p><p><span style=\"font-weight: 400;\">Python is better when cleaning is more complex, repetitive, or file-based. If you need to clean multiple Excel files, handle messy columns, apply advanced transformations, or automate the process, Python is more powerful.<\/span><\/p><p><span style=\"font-weight: 400;\">For example, SQL works well when your data is already in database tables. Python works well when your data is coming from Excel files, CSVs, APIs, or multiple sources.<\/span><\/p><p><span style=\"font-weight: 400;\">In real projects, many analysts use both. They extract and pre-clean data using SQL, then do further cleaning and analysis using Python or Power BI.<\/span><\/p><h2><b>Python vs SQL: Which One Is Better for Dashboards?<\/b><\/h2><p><span style=\"font-weight: 400;\">Neither Python nor SQL is usually the final dashboarding tool for most business users.<\/span><\/p><p><span style=\"font-weight: 400;\">Dashboards are generally built using tools like Power BI, Tableau, Looker Studio, or Excel.<\/span><\/p><p><span style=\"font-weight: 400;\">However, SQL and Python support dashboard creation in different ways.<\/span><\/p><p><span style=\"font-weight: 400;\">SQL helps prepare the dataset for dashboards. You can write queries to extract clean and summarized data.<\/span><\/p><p><span style=\"font-weight: 400;\">Python can be used to create charts, automated reports, and analytical outputs. It can also support dashboards using libraries or frameworks like Plotly, Dash, or Streamlit.<\/span><\/p><p><span style=\"font-weight: 400;\">For most beginners, the best combination is:<\/span><\/p><p><span style=\"font-weight: 400;\">SQL for data extraction<\/span><\/p><p><span style=\"font-weight: 400;\">Power BI or <a href=\"https:\/\/ivyproschool.com\/aihelpcenter\/visualization\/dual-axis-charts\">Tableau<\/a> for dashboards<\/span><\/p><p><span style=\"font-weight: 400;\">Python for deeper analysis and automation<\/span><\/p><p><span style=\"font-weight: 400;\">This combination is very strong for data analytics careers.<\/span><\/p><h2><b>Python vs SQL: Which One Should You Learn First?<\/b><\/h2><p><span style=\"font-weight: 400;\">For most data analytics beginners, the recommended order is:<\/span><\/p><ol><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Excel basics and business data understanding<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SQL for data extraction and database querying<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Power BI or Tableau for visualization<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Python for data cleaning, automation, and advanced analytics<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Statistics and machine learning basics if needed<\/span><\/li><\/ol><p><span style=\"font-weight: 400;\">SQL should usually come before Python because it teaches how business data is stored and retrieved. It also gives faster confidence to beginners because the learning curve is lower.<\/span><\/p><p><span style=\"font-weight: 400;\">Once you know SQL, Python becomes more meaningful. You will understand what kind of data you need, how tables work, and how datasets are structured.<\/span><\/p><p><span style=\"font-weight: 400;\">Learning Python first is also possible, especially if you are already from a technical background. But for non-technical learners entering analytics, SQL-first is usually the smarter path.<\/span><\/p><h2><b>A Simple Learning Roadmap for Beginners<\/b><\/h2><p><span style=\"font-weight: 400;\">Here is a practical roadmap if you are confused about where to begin.<\/span><\/p><h2><b>Step 1: Learn Excel for Data Handling<\/b><\/h2><p><span style=\"font-weight: 400;\">Before SQL or Python, make sure you understand basic data concepts using Excel.<\/span><\/p><p><span style=\"font-weight: 400;\">Learn:<\/span><\/p><ul><li><span style=\"font-weight: 400;\">Rows and columns<\/span><\/li><li><span style=\"font-weight: 400;\">Tables<\/span><\/li><li><span style=\"font-weight: 400;\">Filters<\/span><\/li><li><span style=\"font-weight: 400;\">Sorting<\/span><\/li><li><span style=\"font-weight: 400;\">Basic formulas<\/span><\/li><li><span style=\"font-weight: 400;\">Pivot tables<\/span><\/li><li><span style=\"font-weight: 400;\">Charts<\/span><\/li><li><span style=\"font-weight: 400;\">Data cleaning<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">Excel gives you visual comfort with data.<\/span><\/p><h2><b>Step 2: Learn SQL Basics<\/b><\/h2><p><span style=\"font-weight: 400;\">Start with:<\/span><\/p><ul><li><span style=\"font-weight: 400;\">SELECT<\/span><\/li><li><span style=\"font-weight: 400;\">WHERE<\/span><\/li><li><span style=\"font-weight: 400;\">ORDER BY<\/span><\/li><li><span style=\"font-weight: 400;\">GROUP BY<\/span><\/li><li><span style=\"font-weight: 400;\">HAVING<\/span><\/li><li><span style=\"font-weight: 400;\">JOINS<\/span><\/li><li><span style=\"font-weight: 400;\">CASE WHEN<\/span><\/li><li><span style=\"font-weight: 400;\">Date functions<\/span><\/li><li><span style=\"font-weight: 400;\">Subqueries<\/span><\/li><li><span style=\"font-weight: 400;\">Window functions<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">Practice SQL on business datasets like sales, customers, products, orders, employees, and transactions.<\/span><\/p><h2><b>Step 3: Learn Power BI or Tableau<\/b><\/h2><p><span style=\"font-weight: 400;\">Once you can extract data, learn how to present it visually.<\/span><\/p><p><span style=\"font-weight: 400;\">Focus on:<\/span><\/p><ul><li><span style=\"font-weight: 400;\">Data loading<\/span><\/li><li><span style=\"font-weight: 400;\">Data transformation<\/span><\/li><li><span style=\"font-weight: 400;\">Data modeling<\/span><\/li><li><span style=\"font-weight: 400;\">Charts<\/span><\/li><li><span style=\"font-weight: 400;\">Filters<\/span><\/li><li><span style=\"font-weight: 400;\">KPIs<\/span><\/li><li><span style=\"font-weight: 400;\">Dashboard layout<\/span><\/li><li><span style=\"font-weight: 400;\">Business storytelling<\/span><\/li><\/ul><h2><b>Step 4: Learn Python for Analytics<\/b><\/h2><p><span style=\"font-weight: 400;\">Start Python only after you are comfortable with data thinking.<\/span><\/p><p><span style=\"font-weight: 400;\">Learn:<\/span><\/p><ul><li><span style=\"font-weight: 400;\">Python basics<\/span><\/li><li><span style=\"font-weight: 400;\">Pandas<\/span><\/li><li><span style=\"font-weight: 400;\">NumPy<\/span><\/li><li><span style=\"font-weight: 400;\">Reading files<\/span><\/li><li><span style=\"font-weight: 400;\">Cleaning data<\/span><\/li><li><span style=\"font-weight: 400;\">Grouping data<\/span><\/li><li><span style=\"font-weight: 400;\">Merging datasets<\/span><\/li><li><span style=\"font-weight: 400;\">Creating charts<\/span><\/li><li><span style=\"font-weight: 400;\">Exporting reports<\/span><\/li><li><span style=\"font-weight: 400;\">Basic automation<\/span><\/li><\/ul><h2><b>Step 5: Build Projects<\/b><\/h2><p><span style=\"font-weight: 400;\">Projects convert knowledge into confidence.<\/span><\/p><p><span style=\"font-weight: 400;\">Build projects such as:<\/span><\/p><ul><li><span style=\"font-weight: 400;\">Sales performance analysis<\/span><\/li><li><span style=\"font-weight: 400;\">Customer retention dashboard<\/span><\/li><li><span style=\"font-weight: 400;\">HR attrition analysis<\/span><\/li><li><span style=\"font-weight: 400;\">Marketing campaign analysis<\/span><\/li><li><span style=\"font-weight: 400;\">Inventory analysis<\/span><\/li><li><span style=\"font-weight: 400;\">Financial expense dashboard<\/span><\/li><li><span style=\"font-weight: 400;\">Logistics delay analysis<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">These projects will help you build a portfolio and prepare for interviews.<\/span><\/p><p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-13252\" src=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/6.jpg-3-300x75.jpeg\" alt=\"\" width=\"300\" height=\"75\" srcset=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/6.jpg-3-300x75.jpeg 300w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/6.jpg-3-1080x271.jpeg 1080w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/6.jpg-3-150x38.jpeg 150w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/6.jpg-3-768x192.jpeg 768w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/6.jpg-3-1536x385.jpeg 1536w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/6.jpg-3.jpeg 1920w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/p><h2><b>Common Mistakes Beginners Make<\/b><\/h2><p><span style=\"font-weight: 400;\">Many beginners make the mistake of trying to learn too many tools at once. They start Excel, SQL, Python, Power BI, statistics, machine learning, and AI together. This creates confusion.<\/span><\/p><p><span style=\"font-weight: 400;\">Another mistake is learning only syntax without solving business problems. Knowing commands is not enough. You should know when and why to use them.<\/span><\/p><p><span style=\"font-weight: 400;\">Some learners also jump into Python machine learning before understanding basic data cleaning and analysis. This creates weak fundamentals.<\/span><\/p><p><span style=\"font-weight: 400;\">A better approach is to follow a clear sequence. Learn one tool at a time. Practice on real datasets. Build small projects. Then combine tools gradually.<\/span><\/p><h2><b>Can You Become a Data Analyst with Only SQL?<\/b><\/h2><p><span style=\"font-weight: 400;\">You can start with SQL, but SQL alone may not be enough for most data analyst roles.<\/span><\/p><p><span style=\"font-weight: 400;\">SQL is excellent for extracting and transforming data. But analysts also need visualization, reporting, communication, and business interpretation skills.<\/span><\/p><p><span style=\"font-weight: 400;\">A strong entry-level data analyst should ideally know:<\/span><\/p><ul><li><span style=\"font-weight: 400;\">Excel<\/span><\/li><li><span style=\"font-weight: 400;\">SQL<\/span><\/li><li><span style=\"font-weight: 400;\">Power BI or Tableau<\/span><\/li><li><span style=\"font-weight: 400;\">Basic statistics<\/span><\/li><li><span style=\"font-weight: 400;\">Data storytelling<\/span><\/li><li><span style=\"font-weight: 400;\">Some Python<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">So, SQL can help you enter the field, but you should add dashboarding and Python to become stronger.<\/span><\/p><h2><b>Can You Become a Data Analyst with Only Python?<\/b><\/h2><p><span style=\"font-weight: 400;\">Python alone is also not enough.<\/span><\/p><p><span style=\"font-weight: 400;\">Even if you are good at Python, you may struggle in a company if you cannot extract data from databases using SQL. Most business data is stored in relational databases, and SQL remains the standard language for accessing that data.<\/span><\/p><p><span style=\"font-weight: 400;\">Python is powerful, but SQL is often the entry point to business data.<\/span><\/p><p><span style=\"font-weight: 400;\">So, Python-only learning may be useful for data science experiments, but for business analytics jobs, you should learn SQL too.<\/span><\/p><h2><b>Best Combination for Data Analytics Beginners<\/b><\/h2><p><span style=\"font-weight: 400;\">The best combination for beginners is not Python versus SQL. It is Python plus SQL.<\/span><\/p><p><span style=\"font-weight: 400;\">A good beginner toolkit should look like this:<\/span><\/p><p><span style=\"font-weight: 400;\">Excel for basic analysis and reporting<\/span><\/p><p><span style=\"font-weight: 400;\">SQL for database querying<\/span><\/p><p><span style=\"font-weight: 400;\">Power BI or Tableau for dashboards<\/span><\/p><p><span style=\"font-weight: 400;\">Python for cleaning, automation, and advanced analytics<\/span><\/p><p><span style=\"font-weight: 400;\">Statistics for correct interpretation<\/span><\/p><p><span style=\"font-weight: 400;\">AI tools for faster productivity<\/span><\/p><p><span style=\"font-weight: 400;\">This combination helps you become practical, employable, and future-ready.<\/span><\/p><h2><b>Final Verdict: Python vs SQL for Data Analytics Beginners<\/b><\/h2><p><span style=\"font-weight: 400;\">When comparing <\/span><b>Python vs SQL for data analytics beginners<\/b><span style=\"font-weight: 400;\">, the winner depends on your stage.<\/span><\/p><p><span style=\"font-weight: 400;\">If you are a complete beginner, start with SQL.<\/span><\/p><p><span style=\"font-weight: 400;\">If you want to access business data, SQL is essential.<\/span><\/p><p><span style=\"font-weight: 400;\">If you want to clean, automate, and analyze data deeply, Python is powerful.<\/span><\/p><p><span style=\"font-weight: 400;\">If you want to become a strong data analyst, learn both.<\/span><\/p><p><span style=\"font-weight: 400;\">The most practical learning order is: Excel, SQL, Power BI or Tableau, Python, then advanced analytics.<\/span><\/p><p><span style=\"font-weight: 400;\">Do not treat Python and SQL as competitors. Treat them as partners. SQL helps you get the data. Python helps you do more with the data.<\/span><\/p><p><span style=\"font-weight: 400;\">For beginners, the smartest path is to build strong SQL fundamentals first, then add Python to increase your analytical power.<\/span><\/p><p data-start=\"2305\" data-end=\"2351\"><strong data-start=\"2305\" data-end=\"2351\">Turn this roadmap into a real career plan.<\/strong><\/p><p data-start=\"2353\" data-end=\"2531\">Learning tools randomly can waste months. With <a href=\"https:\/\/ivyproschool.com\/\">Ivy Professional School<\/a>, you follow a structured path, build portfolio projects, prepare for interviews, and get placement support.<\/p><p data-start=\"2533\" data-end=\"2592\"><strong data-start=\"2533\" data-end=\"2592\">Learn data analytics the way companies actually use it.<\/strong><\/p><p><b>FAQs\u00a0<\/b><\/p><ol><li><span style=\"font-weight: 400;\"> Should I learn Python or SQL first for data analytics?<\/span><\/li><\/ol><p><span style=\"font-weight: 400;\">For most beginners, SQL should come first. SQL helps you understand structured data and extract information from databases. After that, Python becomes easier and more useful.<\/span><\/p><ol start=\"2\"><li><span style=\"font-weight: 400;\"> Is SQL easier than Python?<\/span><\/li><\/ol><p><span style=\"font-weight: 400;\">Yes, SQL is usually easier for complete beginners because it has a simpler, English-like syntax. Python is also beginner-friendly, but it requires understanding programming concepts like variables, loops, functions, and libraries.<\/span><\/p><ol start=\"3\"><li><span style=\"font-weight: 400;\"> Can I get a data analyst job with SQL only?<\/span><\/li><\/ol><p><span style=\"font-weight: 400;\">SQL is very important, but SQL alone may not be enough. You should also learn Excel, Power BI or Tableau, basic statistics, and data storytelling. Python can further improve your profile.<\/span><\/p><ol start=\"4\"><li><span style=\"font-weight: 400;\"> Is Python required for data analytics?<\/span><\/li><\/ol><p><span style=\"font-weight: 400;\">Python is not always mandatory for entry-level data analytics jobs, but it is a strong advantage. It is useful for automation, cleaning large datasets, advanced analysis, and moving toward data science.<\/span><\/p><ol start=\"5\"><li><span style=\"font-weight: 400;\"> Which is better for salary, Python or SQL?<\/span><\/li><\/ol><p><span style=\"font-weight: 400;\">Professionals who know both Python and SQL usually have better opportunities. SQL helps with analytics and BI roles, while Python adds value for automation, advanced analytics, and data science roles.<\/span><\/p><ol start=\"6\"><li><span style=\"font-weight: 400;\"> How long does it take to learn SQL and Python?<\/span><\/li><\/ol><p><span style=\"font-weight: 400;\">You can learn basic SQL in 3 to 4 weeks with regular practice. Python basics may take 6 to 8 weeks. Becoming confident in both requires projects and real dataset practice.<\/span><\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"has_ma_el_bg_slider elementor-section elementor-top-section elementor-element elementor-element-d711dfd elementor-section-boxed elementor-section-height-default elementor-section-height-default jltma-glass-effect-no\" data-id=\"d711dfd\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t\t\t<div class=\"elementor-row\">\n\t\t\t\t\t<div class=\"has_ma_el_bg_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4b684fe4 jltma-glass-effect-no\" data-id=\"4b684fe4\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-column-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t<section class=\"has_ma_el_bg_slider elementor-section elementor-inner-section elementor-element elementor-element-22307473 elementor-section-boxed elementor-section-height-default elementor-section-height-default jltma-glass-effect-no\" data-id=\"22307473\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t\t\t<div class=\"elementor-row\">\n\t\t\t\t\t<div class=\"has_ma_el_bg_slider elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-42d42859 jltma-glass-effect-no\" data-id=\"42d42859\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-column-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4ae7da35 jltma-glass-effect-no elementor-widget elementor-widget-image\" data-id=\"4ae7da35\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-image\">\n\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"415\" height=\"277\" src=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2022\/09\/author2.png\" class=\"attachment-large size-large wp-image-12236\" alt=\"Prateek Agrawal\" srcset=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2022\/09\/author2.png 415w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2022\/09\/author2-300x200.png 300w, https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2022\/09\/author2-150x100.png 150w\" sizes=\"auto, (max-width: 415px) 100vw, 415px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"has_ma_el_bg_slider elementor-column elementor-col-66 elementor-inner-column elementor-element elementor-element-dbf5afe jltma-glass-effect-no\" data-id=\"dbf5afe\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-column-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4190d022 jltma-glass-effect-no elementor-widget elementor-widget-text-editor\" data-id=\"4190d022\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-text-editor elementor-clearfix\">\n\t\t\t\t<p>Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Table of Contents Add a header to begin generating the table of contents If you are planning to start a career in data analytics, one of the first questions you will face is: should I learn Python or SQL first? This confusion is very common. Many beginners hear that Python is powerful and used in data science, machine learning, automation, and AI. At the same time, they also hear that SQL is essential because most business data is stored in databases. So, when it comes to Python vs SQL for data analytics beginners, which one is more important? Which one is easier? Which one helps you get a job faster? And most importantly, which one should you learn first? The honest answer is simple: if you are starting in data analytics, learn SQL first, then Python. SQL helps you access and extract data. Python helps you analyze, clean, automate, and extend your work further. Both are valuable, but they serve different purposes. A strong data analyst should ideally know both. This blog will help you understand the difference between Python and SQL, their roles in data analytics, how difficult they are, where each one is used, and the best learning [&hellip;]<\/p>\n","protected":false},"author":1001976,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[],"class_list":["post-13234","post","type-post","status-publish","format-standard","hentry","category-data-analytics"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Python vs SQL for Data Analytics Beginners: Which One Should You Learn First? - R vs Python: Which Analytics Tool Should You Choose for Data Science?<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ivyproschool.com\/blog\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python vs SQL for Data Analytics Beginners: Which One Should You Learn First? - R vs Python: Which Analytics Tool Should You Choose for Data Science?\" \/>\n<meta property=\"og:description\" content=\"Table of Contents Add a header to begin generating the table of contents If you are planning to start a career in data analytics, one of the first questions you will face is: should I learn Python or SQL first? This confusion is very common. Many beginners hear that Python is powerful and used in data science, machine learning, automation, and AI. At the same time, they also hear that SQL is essential because most business data is stored in databases. So, when it comes to Python vs SQL for data analytics beginners, which one is more important? Which one is easier? Which one helps you get a job faster? And most importantly, which one should you learn first? The honest answer is simple: if you are starting in data analytics, learn SQL first, then Python. SQL helps you access and extract data. Python helps you analyze, clean, automate, and extend your work further. Both are valuable, but they serve different purposes. A strong data analyst should ideally know both. This blog will help you understand the difference between Python and SQL, their roles in data analytics, how difficult they are, where each one is used, and the best learning [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/ivyproschool.com\/blog\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\/\" \/>\n<meta property=\"og:site_name\" content=\"R vs Python: Which Analytics Tool Should You Choose for Data Science?\" \/>\n<meta property=\"article:published_time\" content=\"2026-05-09T11:01:02+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-05-09T12:36:47+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/Python-vs-SQL.jpg.jpeg\" \/>\n\t<meta property=\"og:image:width\" content=\"1920\" \/>\n\t<meta property=\"og:image:height\" content=\"1080\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Prateek Agrawal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Prateek Agrawal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"17 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\\\/\"},\"author\":{\"name\":\"Prateek Agrawal\",\"@id\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/#\\\/schema\\\/person\\\/8010a561e914798a4419e937b20aa49b\"},\"headline\":\"Python vs SQL for Data Analytics Beginners: Which One Should You Learn First?\",\"datePublished\":\"2026-05-09T11:01:02+00:00\",\"dateModified\":\"2026-05-09T12:36:47+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\\\/\"},\"wordCount\":3380,\"commentCount\":0,\"image\":{\"@id\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/05\\\/Python-vs-SQL.jpg-1080x608.jpeg\",\"articleSection\":[\"Data Analytics\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\\\/\",\"url\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\\\/\",\"name\":\"Python vs SQL for Data Analytics Beginners: Which One Should You Learn First? - R vs Python: Which Analytics Tool Should You Choose for Data Science?\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/05\\\/Python-vs-SQL.jpg-1080x608.jpeg\",\"datePublished\":\"2026-05-09T11:01:02+00:00\",\"dateModified\":\"2026-05-09T12:36:47+00:00\",\"author\":{\"@id\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/#\\\/schema\\\/person\\\/8010a561e914798a4419e937b20aa49b\"},\"breadcrumb\":{\"@id\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\\\/#primaryimage\",\"url\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/05\\\/Python-vs-SQL.jpg.jpeg\",\"contentUrl\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/05\\\/Python-vs-SQL.jpg.jpeg\",\"width\":1920,\"height\":1080},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Python vs SQL for Data Analytics Beginners: Which One Should You Learn First?\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/#website\",\"url\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/\",\"name\":\"Ivy Professional School | Official Blog\",\"description\":\"Confused between R and Python for your data science journey? Discover the key differences in data visualization, handling capabilities, speed, and ease of learning.\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/#\\\/schema\\\/person\\\/8010a561e914798a4419e937b20aa49b\",\"name\":\"Prateek Agrawal\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/7b44716c53f75a40cfd6a238640ed4bd0e72117b1789f1bea3c4fe0e43c2475a?s=96&d=mm&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/7b44716c53f75a40cfd6a238640ed4bd0e72117b1789f1bea3c4fe0e43c2475a?s=96&d=mm&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/7b44716c53f75a40cfd6a238640ed4bd0e72117b1789f1bea3c4fe0e43c2475a?s=96&d=mm&r=g\",\"caption\":\"Prateek Agrawal\"},\"sameAs\":[\"https:\\\/\\\/www.linkedin.com\\\/in\\\/prateekagrawal\\\/\"],\"url\":\"https:\\\/\\\/ivyproschool.com\\\/blog\\\/author\\\/dm_ivy\\\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Python vs SQL for Data Analytics Beginners: Which One Should You Learn First? - R vs Python: Which Analytics Tool Should You Choose for Data Science?","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/ivyproschool.com\/blog\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\/","og_locale":"en_US","og_type":"article","og_title":"Python vs SQL for Data Analytics Beginners: Which One Should You Learn First? - R vs Python: Which Analytics Tool Should You Choose for Data Science?","og_description":"Table of Contents Add a header to begin generating the table of contents If you are planning to start a career in data analytics, one of the first questions you will face is: should I learn Python or SQL first? This confusion is very common. Many beginners hear that Python is powerful and used in data science, machine learning, automation, and AI. At the same time, they also hear that SQL is essential because most business data is stored in databases. So, when it comes to Python vs SQL for data analytics beginners, which one is more important? Which one is easier? Which one helps you get a job faster? And most importantly, which one should you learn first? The honest answer is simple: if you are starting in data analytics, learn SQL first, then Python. SQL helps you access and extract data. Python helps you analyze, clean, automate, and extend your work further. Both are valuable, but they serve different purposes. A strong data analyst should ideally know both. This blog will help you understand the difference between Python and SQL, their roles in data analytics, how difficult they are, where each one is used, and the best learning [&hellip;]","og_url":"https:\/\/ivyproschool.com\/blog\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\/","og_site_name":"R vs Python: Which Analytics Tool Should You Choose for Data Science?","article_published_time":"2026-05-09T11:01:02+00:00","article_modified_time":"2026-05-09T12:36:47+00:00","og_image":[{"width":1920,"height":1080,"url":"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/Python-vs-SQL.jpg.jpeg","type":"image\/jpeg"}],"author":"Prateek Agrawal","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Prateek Agrawal","Est. reading time":"17 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/ivyproschool.com\/blog\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\/#article","isPartOf":{"@id":"https:\/\/ivyproschool.com\/blog\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\/"},"author":{"name":"Prateek Agrawal","@id":"https:\/\/ivyproschool.com\/blog\/#\/schema\/person\/8010a561e914798a4419e937b20aa49b"},"headline":"Python vs SQL for Data Analytics Beginners: Which One Should You Learn First?","datePublished":"2026-05-09T11:01:02+00:00","dateModified":"2026-05-09T12:36:47+00:00","mainEntityOfPage":{"@id":"https:\/\/ivyproschool.com\/blog\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\/"},"wordCount":3380,"commentCount":0,"image":{"@id":"https:\/\/ivyproschool.com\/blog\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\/#primaryimage"},"thumbnailUrl":"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/Python-vs-SQL.jpg-1080x608.jpeg","articleSection":["Data Analytics"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/ivyproschool.com\/blog\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/ivyproschool.com\/blog\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\/","url":"https:\/\/ivyproschool.com\/blog\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\/","name":"Python vs SQL for Data Analytics Beginners: Which One Should You Learn First? - R vs Python: Which Analytics Tool Should You Choose for Data Science?","isPartOf":{"@id":"https:\/\/ivyproschool.com\/blog\/#website"},"primaryImageOfPage":{"@id":"https:\/\/ivyproschool.com\/blog\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\/#primaryimage"},"image":{"@id":"https:\/\/ivyproschool.com\/blog\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\/#primaryimage"},"thumbnailUrl":"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/Python-vs-SQL.jpg-1080x608.jpeg","datePublished":"2026-05-09T11:01:02+00:00","dateModified":"2026-05-09T12:36:47+00:00","author":{"@id":"https:\/\/ivyproschool.com\/blog\/#\/schema\/person\/8010a561e914798a4419e937b20aa49b"},"breadcrumb":{"@id":"https:\/\/ivyproschool.com\/blog\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/ivyproschool.com\/blog\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/ivyproschool.com\/blog\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\/#primaryimage","url":"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/Python-vs-SQL.jpg.jpeg","contentUrl":"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2026\/05\/Python-vs-SQL.jpg.jpeg","width":1920,"height":1080},{"@type":"BreadcrumbList","@id":"https:\/\/ivyproschool.com\/blog\/python-vs-sql-for-data-analytics-beginners-which-one-should-you-learn-first\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/ivyproschool.com\/blog\/"},{"@type":"ListItem","position":2,"name":"Python vs SQL for Data Analytics Beginners: Which One Should You Learn First?"}]},{"@type":"WebSite","@id":"https:\/\/ivyproschool.com\/blog\/#website","url":"https:\/\/ivyproschool.com\/blog\/","name":"Ivy Professional School | Official Blog","description":"Confused between R and Python for your data science journey? Discover the key differences in data visualization, handling capabilities, speed, and ease of learning.","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/ivyproschool.com\/blog\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/ivyproschool.com\/blog\/#\/schema\/person\/8010a561e914798a4419e937b20aa49b","name":"Prateek Agrawal","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/7b44716c53f75a40cfd6a238640ed4bd0e72117b1789f1bea3c4fe0e43c2475a?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/7b44716c53f75a40cfd6a238640ed4bd0e72117b1789f1bea3c4fe0e43c2475a?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/7b44716c53f75a40cfd6a238640ed4bd0e72117b1789f1bea3c4fe0e43c2475a?s=96&d=mm&r=g","caption":"Prateek Agrawal"},"sameAs":["https:\/\/www.linkedin.com\/in\/prateekagrawal\/"],"url":"https:\/\/ivyproschool.com\/blog\/author\/dm_ivy\/"}]}},"_links":{"self":[{"href":"https:\/\/ivyproschool.com\/blog\/wp-json\/wp\/v2\/posts\/13234","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ivyproschool.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ivyproschool.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ivyproschool.com\/blog\/wp-json\/wp\/v2\/users\/1001976"}],"replies":[{"embeddable":true,"href":"https:\/\/ivyproschool.com\/blog\/wp-json\/wp\/v2\/comments?post=13234"}],"version-history":[{"count":15,"href":"https:\/\/ivyproschool.com\/blog\/wp-json\/wp\/v2\/posts\/13234\/revisions"}],"predecessor-version":[{"id":13259,"href":"https:\/\/ivyproschool.com\/blog\/wp-json\/wp\/v2\/posts\/13234\/revisions\/13259"}],"wp:attachment":[{"href":"https:\/\/ivyproschool.com\/blog\/wp-json\/wp\/v2\/media?parent=13234"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ivyproschool.com\/blog\/wp-json\/wp\/v2\/categories?post=13234"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ivyproschool.com\/blog\/wp-json\/wp\/v2\/tags?post=13234"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}