مستودعات البيانات والتنقيب عن البيانات - Data Warehousing and Data Mining IS322T
The course objectives are: Recognize the fundamentals of data warehousing. Manipulate the data warehousing. Use the knowledge discovery in data warehousing. Discover knowledge in different ap
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Introduction
- Course Overview 05:55
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Chapter 1: The Compelling Need for Data Warehousing
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Chapter 2: Data warehouse- Basic Components
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Chapter 3: Principals of Dimensional Modeling
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Chapter 4: OLAP in the data warehouse
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Chapter 5: Introduction to KDD and Data Mining
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Chapter 6: Data and Data Processing
- Lecture 12: Data Types (Old Slide) 16:11
- Lecture 13: Data Quality Problems (Old Slide) 31:47
- Lecture 6: Attribute Types - Part 1 (New Slides) 21:28
- Lecture 6: Attribute Types - Part 2 23:15
- Lecture 6: Duplicate Data - Part 3 27:21
- Lecture 6: Data Preprocessing - Part 4 21:25
- Lecture 6: Binarization - Part 5 10:53
- Chapter 6 Summery
- How to compute Covariance and Correlation Example 10:11
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Chapter 7: Mining Frequent Patterns
- Chapter 7 - Part 1 (شرح السلايدات المحدثة للشابتر بالتفصيل) 25:01
- Chapter 7- Part 2 40:18
- Chapter 7 - Part 3 20:06
- Chapter 7 Revision - Part 1 23:25
- Chapter 7 Revision - Part 2 14:59
- Chapter 7 Summery
- Apriori Algorithm Numerical Example 16:24
- FP Growth Algorithm Explained With Numerical Example 16:31
- The Apriori Algorithm 16:14
- FP Growth Approach 23:39
- Task Solution - Association Rules 27:44
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Chapter 8: Classification
- Lecture 17: Classification - part 1 25:02
- Lecture 18: Classification - Part 2 21:58
- Lecture 19: Extra Example of Decision Tree 18:56
- Play Tennis Example (Decision Tree) 00:00
- Chapter 8: New Slides (Update) 20:49
- Lesson 8 - Classification: Basic Concepts and Techniques - Slide 32 12:35
- Classification of "Buys Computer" and Entropy Calculation 11:59
- Hunt's Algorithm 12:40
- Lecture 8 - Part 1 (Update) 31:16
- Lecture 8 - Part 2 (Update) 30:50
- Lecture 8 - Part 3 (Update) 17:11
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Lecture 9: Nearest Neighbors Classifiers
- KNN - Part 1 10:35
- KNN Classification method - Part 1 18:48
- KNN- Part 2 10:49
- KNN Classification Tutorial 08:38
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Lecture 10: Bayesian Classifiers
- Lecture 11: Clustering
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Assignments
- Assignment 1 - Solution (Research) 10:54
- Assignment 1 Solution (First Semester 2020) 19:55
- Assignment 2- Solution 25:29
- Assignment 3 - Solution 22:40
- Assignment 2 - Solution File
- Assignment 2 - Solution 2022 11:19
- K-means Home Work Solution (Lecture 11) 13:09
- Assignment 1 - Solution File 2021
- Assignment 1 Solution 2021 19:00
- Assignment 2 - Solution 2021 17:12
- Assignment 1 - Solution (1st Term 2021)
- Assignment 2 Solution 1st Term 2021 19:26
- Assignment 1 Solution - ( 1443 Second Semester- 2022)
- Bonus Assignment Solution 16:46
- Bonus Assignment Solution File
- Assignment 1 Solution (1st Semester 2022) 24:51
- Assignment 1 solution (2023) 11:04
- Assignment 1 Solution File
- Assignment 2 Solution 2023 17:07
- Assignment 2 Solution File 2023
- Assignment 1 Solution File 1st Semester 2020
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Labs and Project
- Lab 2 Solution 11:11
- Lab 3 solution 06:50
- Lab 2 Solution (new) 03:55
- Lab 3 solution (new) 08:29
- Lab 4 Exercise Solution - 2021 08:28
- Lab 4 Exercise Solution File - 2021
- Lab 6 Solution 14:46
- Lab 7 Solution 15:07
- Lab 9 Solution 07:36
- The Project Notes 09:10
- Lab 1 and Lab 5 Solution 06:02
- How to do your Project? 13:10
- Lab 7 Solution (new) 11:21
- The Project Phase 2 and 3 14:32
- Project Explanation -2021 19:19
- Project Explanation (1st Semester 2022-2023) 13:34
- Project - Phase 2 and 3 17:14
- Lab 8 Solution 11:46
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Revision and Exams Solution
- Exams solution for Midterm 1 26:20
- Revision for Final 14:18
- Revision for Chapters 4, 5, 6 and 7 31:36
- Exam Collection Solution (مهم لميد 1) 13:02
- تعريفات ملخص
- تعداد ملخص
- Quiz 1 Solution 06:52
- Exam Collection Solution (مهم لميد 1)
- Extra Exercises for Entropy and k-means 27:14
- Exam Collection File for Midterm 1
- Midterm 2 Exam Solution 10:01
- Revision for Chapters 6, 7 and 11 40:15
- Midterm 1 Exam Solution -2024 13:17
- مقطع يشرح الفرق بين Additive Measure 05:22
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Anaconda Lab (Python)
- How to install Anaconda ? 12:01
- Lab 1 - Python Basics - Part 1 26:35
- Lab 1 - Python Basics - Part 2 10:12
- Lab 2 - Pandas - Part 1 14:27
- Lab 2 - Pandas - Part 2 16:30
- Lab 3 Python 13:08
- Lab 4 and Lab 5 - Decision Tree and Apriori 18:21
- Evaluation 4 - Fish_L dataset 10:59
- Project Explanation 20:02
- Jupyter File (Sample Code for Project)
- Evaluation # 4 Solution
- Lab 5 Evaluation (Introduction to Python) 20:26
- Lab 5 (Weather.csv, Car_Sales.csv) 12:44
- Evaluation 6 Solution File
- Evaluation 6 Solution 06:37
- Evaluation 7: Decision Tree and Random Forest 08:26
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Assignments Solution 2026
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Labs 2026
- Lab 1: How to Open Jupyter Online 03:21
- Lab 2 Solution 03:40
- Lab 3 Solution 02:59
- Lab 2 Solution File
- Lab 3 Solution File
- Lab 4 Solution File
- Lab 4 Solution 06:22
- Lab 5 Solution File
- Lab 5 Solution 09:20
- Lab 6: Unsupervised Learning (Clustering) 14:11
- Lab 7: Machine Learning Models Slides- Part 1 22:43
- Lab 7: Supervised Learning Lab (Evaluation Methods) - Part 2 16:54
- Lab 7: Supervised Learning Code - Part 3 18:01
- Project Explanation 2026 10:27
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Introduction
- The course objectives are: Recognize the fundamentals of data warehousing. Manipulate the data warehousing. Use the knowledge discovery in data warehousing. Discover knowledge in different applications. Recognize the data mining. Conduct different methods and algorithms of data mining
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Eng. Enaam Ahmed Mohammed holds a B.sc of Computer Engineering (2004) from Cairo University - Egypt and Master degree in IT (2007) from SIKKIM MANIPAL University - India. She has teaching experience for more than 15 years and has professional Experience in the following fields: • Oracle 9i – Certified from oracle (OCA) • Visual Studio .Net ( C
) • C, C++ • Visual Basic 6 • PHP • JavaScript • HTML, CSS She worked as a Teacher at many Universities and educational centers from 2005 - 2010 such as UMS University, Al Wataneia University, NCC Eduction center and APTECH Worldwide center. From the beginning of 2010 till now she had taught many courses for different Saudi Arabia Universities such as PNU, KSU, PSU, Shaqra University and Imamu University. She has experience for teaching many courses as below: • Database fundamentals. • Software Engineering. • Networking and Network Protocols. • Information security. • Programming language (Java, C++, C
, Python) • Operating system. • Multi-tier and Web development. • System Analysis and design. • Data warehouse and Data Mining • Object Oriented design • Support and Decision Making Systems • Data Structure -
Student feedback
5.00Course Rating
500 ريال
المحاضرات
154 فيديو
المدة الزمنية
34:09:43
الملفات
63 ملف
الواجبات
Yes, Completely
الجزء العملي
Yes, Completely
المشروع
Explanation, No implementation
الشهادة
Not Applicable
التقييمات (7)
مشاركة التجارب وقصص النجاح ستظل أجمل الأشياء دومًا.
افضل معلمه اونلاين اشتركت معها مو طبيعي كيف تدخل المعلومه بمخك بكل بساطه الشرح مريح والوقت مو طويل مختصر بكل فائده كانت من المواد الدسمه عندي ومعها صارت بسيطه بكل ماتعنيه الكلمه بدون مبالغه ادخل احل بالاختبار وانا متطمنه على درجاتي كل شي شارحته وتقول انه بيجي يجي فعلا ونفسه بالضبط كانك تدرسين الاختبار لهدرجه ومراجعاتها ممتازه لاتفوتونها
افضل تيتشر تشرح مستودعات ماراح تندمون ابد معاها ❤️❤️❤️شرح واضح و سهل و تختصر عليكم الوقت الله يسعدك استاذة انعام
افضل شرح مختصر و مفهوم جدااا انصح بالاشتراك معاها على انها تشرح مواد اخرى
افضل تيتشر والله متعاونه و تساعد مره و شرحها رهيب توصل المعلومه ببساطه
افضل تيتشر سهلت علينا الماده وشرحها حلو ومختصر❤️❤️❤️❤️
الله يسعدها م.انعام من افضل الي اشتركت معهم صوتها مريح وشرح واضح وبسيط وهي جدا متعاونه وسريعه
الله يسعدها م.انعام من افضل الي اشتركت معهم صوتها مريح وشرح واضح وبسيط وهي جدا متعاونه وسريعه