Data Mining Algorithms Training Course

From 18 £ /h

Data Mining Algorithms and Techniques Training Course - Beginner and Intermediate Level, for Computer Science Professionals and Non-Professionals.

The course content is titled: Advanced Analysis and Data Mining.
The book can be searched for using its name or the author's name.

Table of Contents

Chapter 1: Introduction to Advanced Analysis and Data Mining
1-1 What is data mining, its procedures and tools
1-2 What type of data is mined?
1-3 What are databases?
1-4 Relational Database
1-5 Query Language
1-6 Benefits of Database Mining
1-7 months data mining applications
A - Business Intelligence (Business Intelligence)
B - Internet search engines

Chapter Two: Data Recognition
2-1 Data Types, Characteristics, and Features
2-2 Statistical Description of Data
2-3 Visualization of Data
2-4 Measuring data similarity and difference
Chapter Three: Preparing Data for Analysis and Mining
3-1 The importance of preparing data for analysis and mining
3-2 Data Cleanup
3-3 Data Integration
3-4 Data Reduction
3-5 Data Transformation and Data Individualization

Chapter Four: Pattern Discovery and Exploration, Dependency and Correlation Rules
4-1 Basic Concepts
4-2 Shopping basket analysis (example)
4-3 Evaluating the dependency and correlation rules being explored
4-4 Mining Multi-Level Dependency and Linkage Rules
4-5 Mining multidimensional dependency and correlation rules
4-6 Rules of nominal and quantitative dependency and correlation
4-7 Exploring and identifying rare and negative patterns
4-8 Exploring and Determining the Rules of Dependency and Conditional Linkage
4-9 Evaluating dependency and correlation rules and distinguishing between useful and unhelpful ones
4-10 Measuring the type and strength of the relationship in dependency and correlation rules
4-11 Applications of pattern mining in practical life

Chapter Five: Analysis and Mining Using Classification and Prediction Algorithms
5-1 Basic Concepts
5-2 Classification using decision tree extrapolation
5-3 Classification using probability theory (hypothetical theory)
5-4 Classification using hypothetical network theory
5-5 Classification using correlation rules extrapolation
5-6 Classification using neural network algorithm
5-7 Classification using the nearest neighbor algorithm
5-8 Multi-category classification algorithms
5-9 Evaluating the efficiency and selection of classification algorithms

Chapter Six: Analysis and Mining Using Cluster Hashing Algorithms
6-1 Basic Concepts
6-2 Clustering by Division
6-3 Hierarchical Clustering
A. Hierarchical clustering
b. Hierarchical fission
6-4 Probability Clustering
6-5 High-Dimensional Clustering
6-6 Clustering of graphs and network data
6-7 Conditional Clustering
6-8 Cluster Segmentation Assessment

Chapter Seven: Analyzing and Mining Outliers and Complex Data Types
7-1 Basic Concepts
7-2 Types of extreme values
7-3 Ways to Explore Extreme Values
7-4 Complex Data Analysis and Mining

Chapter Eight: Planning Data Mining Operations and Their Applications in Society
8-1 Planning Data Mining Operations
8-2 Data Mining in the Community
8-3 Data mining applications in vital areas of society
8-4 Practical Application: Recommendation System Usage Scenario

Appendix 1: Database Fundamentals
Appendix 2: Data Warehouse Fundamentals
Appendix 3: Glossary of Data Mining Terms

Extra information

Online with explanations using simple examples that can be applied using Excel.

Location

Online from Palestine

About Me

A scientist and researcher specializing in mathematics and computer science.
A seasoned mathematics teacher, specializing in explaining, simplifying, and developing curricula based on scientific and research foundations.
A researcher specializing in designing and developing aptitude tests and thinking and analytical skills for students.
A part-time trainer and lecturer specializing in training courses in mathematics, statistics, financial mathematics, computer science, data science, systems analysis, database design and development, and data mining algorithms.

Education

Bachelor of Science degree in Mathematics and Computer Science from Ain Shams University in Cairo, 1991. He also holds other professional degrees, a Master's degree and a PhD in Computer Science specializing in Business Intelligence.

Experience / Qualifications

Over 30 years of experience in teaching mathematics, computer science, programming, systems analysis, data mining, and interdisciplinary research. Founder and director of the Interdisciplinary Research and Studies Center and its affiliated testing center.

Age

Adults (18-64 years old)

Seniors (65+ years old)

Student level

Beginner

Intermediate

Duration

60 minutes

The class is taught in

Arabic

English

Skills

Computer programming

Database

Algorithms

Availability of a typical week

(GMT -04:00)

New York

Online via webcam

Mon

Tue

Wed

Thu

Fri

Sat

Sun

00-04

04-08

08-12

12-16

16-20

20-24

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Understanding the concept, objectives and benefits of OOP.
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Why unstructured code quickly becomes unmanageable.
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Properties, methods and representation of the real world.
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Why duplicating code is a bad idea.
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What it's actually doing under the hood.
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Sharing methods and memory optimization.
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Proper initialization of objects.
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13. The keyword super
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Choosing the right architecture.
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🚀 Learner's result

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2- When to use it
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