Curriculum
- 4 Sections
- 37 Lessons
- 24 Weeks
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- Course Overview/*! CSS Used from: Embedded */ *, ::after, ::before { box-sizing: border-box; border-width: 0; border-style: solid; border-color: #e5e7eb; } ::after, ::before { --tw-content: ''; } h2 { font-size: inherit; font-weight: inherit; } a { color: inherit; text-decoration: inherit; } h2, p { margin: 0; } :disabled { cursor: default; } *, ::before, ::after { --tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-scroll-snap-strictness: proximity; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgb(59 130 246 / 0.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; } .mx-auto { margin-left: auto; margin-right: auto; } .mb-2 { margin-bottom: 0.5rem; } .mb-4 { margin-bottom: 1rem; } .mb-6 { margin-bottom: 1.5rem; } .mr-2 { margin-right: 0.5rem; } .max-w-screen-sm { max-width: 640px; } .max-w-screen-xl { max-width: 1280px; } .rounded-lg { border-radius: 0.5rem; } .bg-primary-700 { --tw-bg-opacity: 1; background-color: rgb(29 78 216 / var(--tw-bg-opacity)); } .bg-white { --tw-bg-opacity: 1; background-color: rgb(255 255 255 / var(--tw-bg-opacity)); } .px-4 { padding-left: 1rem; padding-right: 1rem; } .px-5 { padding-left: 1.25rem; padding-right: 1.25rem; } .py-2.5 { padding-top: 0.625rem; padding-bottom: 0.625rem; } .py-8 { padding-top: 2rem; padding-bottom: 2rem; } .text-center { text-align: center; } .text-4xl { font-size: 3rem; line-height: 2.5rem; } .text-sm { font-size: 0.875rem; line-height: 1.25rem; } .font-extrabold { font-weight: 800; } .font-light { font-weight: 300; } .font-medium { font-weight: 500; } .leading-tight { line-height: 1.25; } .tracking-tight { letter-spacing: -0.025em; } .text-gray-500 { --tw-text-opacity: 1; color: rgb(107 114 128 / var(--tw-text-opacity)); } .text-gray-900 { --tw-text-opacity: 1; color: rgb(17 24 39 / var(--tw-text-opacity)); } .text-white { --tw-text-opacity: 1; color: rgb(255 255 255 / var(--tw-text-opacity)); } .hover\:bg-primary-800:hover { --tw-bg-opacity: 1; background-color: rgb(30 64 175 / var(--tw-bg-opacity)); } .focus\:outline-none:focus { outline: 2px solid transparent; outline-offset: 2px; } .focus\:ring-4:focus { --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(4px + var(--tw-ring-offset-width)) var(--tw-ring-color); box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); } .focus\:ring-primary-300:focus { --tw-ring-opacity: 1; --tw-ring-color: rgb(147 197 253 / var(--tw-ring-opacity)); } @media (min-width: 640px) { .sm\:py-16 { padding-top: 4rem; padding-bottom: 4rem; } } @media (min-width: 768px) { .md\:text-lg { font-size: 1.5rem; line-height: 1.75rem; } } @media (min-width: 1024px) { .lg\:px-6 { padding-left: 1.5rem; padding-right: 1.5rem; } } .imgdata { width: 35% } @media (max-width: 767px) { .imgdata { width: 40% } } .course-payment { display: none !important; } .thim-course-landing-button { display: none !important; }0
- Index29
- 2.1Introduction to Data Warehouse11 Minutes
- 2.2Meta Data5 Minutes
- 2.3Data Mart6 Minutes
- 2.4Data Warehouse Architecture7 Minutes
- 2.5How to draw star schema10 Minutes
- 2.6Olap Operations8 Minutes
- 2.7OLAP VS OLTP8 Minutes
- 2.8K-Mean12 Minutes
- 2.9Introduction to Data Mining10 Minutes
- 2.10Naive Bayes Part #118 Minutes
- 2.11Apriori algorithm12 Minutes
- 2.12Agglomerative Clustering13 Minutes
- 2.13Knowledge Discovery in Database (KDD)9 Minutes
- 2.14Extract Transform and Load (ETL)9 Minutes
- 2.15FP-Tree15 Minutes
- 2.16Decision Tree24 Minutes
- 2.17K -Medoids21 Minutes
- 2.18Naive Bayes Part #225 Minutes
- 2.19Agglomerative Adjacency Matrix5 Minutes
- 2.20DBSCAN4 Minutes
- 2.21Design Strategy of Data warehouse and Data Mining12 Minutes
- 2.22Types of Attribute for Data Exploration9 Minutes
- 2.23K mean clustering Sum – Type 2 where K=223 Minutes
- 2.24Data Preprocessing Part #117 Minutes
- 2.25Data Preprocessing Part #29 Minutes
- 2.26Data Visualization Part #111 Minutes
- 2.27Data Visualization Part #211 Minutes
- 2.28Schema Design – Dimension Modeling Part #116 Minutes
- 2.29Schema Design – Dimension Modeling Part #211 Minutes
- Data Warehouse and Data Mining NOTES and Importance2
- Data Warehousing and Mining Viva Question6
Introduction to Data Warehouse
Introduction to Data Warehouse
A data warehouse is mainly a data management system that’s designed to enable and support business intelligence (BI) activities, particularly analytics. Data warehouses are alleged to perform queries, cleaning, manipulating, transforming, and analyzing the data and they also contain large amounts of historical data.
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Meta Data
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2 Comments
very good
Useful Learning