
{"id":1602,"date":"2025-10-15T11:55:57","date_gmt":"2025-10-15T11:55:57","guid":{"rendered":"https:\/\/fanlaw.in\/?post_type=product&#038;p=1602"},"modified":"2025-10-15T11:55:58","modified_gmt":"2025-10-15T11:55:58","slug":"data-science-kickstart-30-day-course","status":"publish","type":"product","link":"https:\/\/fanlaw.in\/?product=data-science-kickstart-30-day-course","title":{"rendered":"Data Science Kickstart: 30-Day Course"},"content":{"rendered":"<p>&nbsp;<\/p>\n<div class=\"horizontal-scroll-wrapper\">\n<div class=\"table-block-component\">\n<div class=\"table-block has-export-button\">\n<div class=\"table-content not-end-of-paragraph\" data-hveid=\"0\" data-ved=\"0CAAQ3ecQahcKEwjpzKjui6aQAxUAAAAAHQAAAAAQQQ\">\n<table>\n<thead>\n<tr>\n<td>Days<\/td>\n<td>Content Title<\/td>\n<td>Key Concepts Covered<\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><b>Days 1-7<\/b><\/td>\n<td><b>Tools &amp; Data Handling<\/b><\/td>\n<td>Python\/Jupyter Notebook Setup, Introduction to <b>NumPy<\/b> (Array creation, indexing, operations), Introduction to <b>Pandas<\/b> (DataFrames, Series).<\/td>\n<\/tr>\n<tr>\n<td><b>Days 8-14<\/b><\/td>\n<td><b>Data Cleaning &amp; Preprocessing<\/b><\/td>\n<td>Loading data, Handling missing values, Data filtering and selection, Grouping and Aggregation.<\/td>\n<\/tr>\n<tr>\n<td><b>Days 15-21<\/b><\/td>\n<td><b>Data Visualization<\/b><\/td>\n<td>Introduction to <b>Matplotlib<\/b> and <b>Seaborn<\/b>, Creating basic plots (Bar, Line, Scatter, Histograms), Telling stories with data.<\/td>\n<\/tr>\n<tr>\n<td><b>Days 22-30<\/b><\/td>\n<td><b>Basic Statistics &amp; Project<\/b><\/td>\n<td>Descriptive Statistics, Introduction to Linear Regression (Conceptual), <b>Final Project:<\/b> Exploratory Data Analysis (EDA) on a public dataset (e.g., Titanic or Iris).<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<ul>\n<li><b>Target Audience:<\/b> Individuals with basic Python knowledge interested in data analysis.<\/li>\n<li><b>Goal:<\/b> Learn the initial steps of the Data Science workflow using essential Python libraries.<\/li>\n<\/ul>\n","protected":false},"featured_media":1603,"comment_status":"open","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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