Data Mining Algorithms Training Course

From 21 € /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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electrical engineering (transformers, electrical machines, switching power supply)
C / c ++ programming, Assembler, ARM, STM32
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📚 On the program:

Variables

Loops

Functions

Data structures

Practical projects for implementation

💡 How does the course work?

Clear explanations to understand the programming logic

Targeted exercises adapted to your level

Concrete projects to create your own applications

🎯 My goal:

Helping you understand the logic behind the code

Progress at your own pace

Create your own projects in Python and gain independence

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Object-Oriented Programming is often perceived as complex or abstract.

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🎯 Training Objectives

Upon completion of this training, you will be able to:

Understanding what Object-Oriented Programming really is (and when to use it)
Create and manipulate objects in JavaScript in a clean and efficient way
Use ES6 classes, constructors, and methods with confidence
Mastering this, the prototype, and the instantiation logic
Apply encapsulation, inheritance, and polymorphism without confusion
Avoiding common mistakes made by OOP beginners
Structure your JavaScript code like a professional developer

📖 Training Plan – Object-Oriented Programming in JavaScript
1. Introduction to Object-Oriented Programming 🧠
Understanding the concept, objectives and benefits of OOP.
2. Procedural Programming vs. OOP
Why unstructured code quickly becomes unmanageable.
3. Objects in JavaScript
Properties, methods and representation of the real world.
4. The keyword this
Understanding the execution context (often poorly understood).
5. Limitations of simple objects
Why duplicating code is a bad idea.
6. Constructive functions
Create multiple objects from the same model.
7. The keyword new
What it's actually doing under the hood.
8. The prototype
Sharing methods and memory optimization.
9. ES6 Classes
Modern syntax and best practices.
10. The builder
Proper initialization of objects.
11. Data Encapsulation
Protect the internal state of objects.
12. Inheritance between classes
Reusing code intelligently.
13. The keyword super
Communication between parent and child in the classroom.
14. Polymorphism
The same behavior, several forms.
15. Composition vs. Inheritance
Choosing the right architecture.
16. Best practices in OOP
Write readable, scalable, and maintainable code.
17. Common mistakes made by beginners
Pitfalls to absolutely avoid.
18. Guided practical exercise
Creation of a concrete class (product, user, etc.).
19. Assessment Quiz (Multiple Choice Questions)
To validate the actual understanding of the concepts.

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Concrete examples from real projects
Simple but effective exercises
Constant questioning to avoid rote learning
Adaptation to the learner's level and pace
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🚀 Learner's result

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1- Why does it exist?
2- When to use it
3- and when not to use it

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a solid understanding of OOP
a cleaner and more professional code
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Areas I cover regularly:

Python from the basics through functions, data structures, file handling, object orientation and libraries like NumPy and Pandas
C and C++, including the parts that cause most of the trouble: pointers, memory management, structs, classes and compilation
GCSE and A-Level Computer Science across all exam boards, including pseudocode, trace tables and written paper technique
A-Level NEA projects and university coursework, plus debugging sessions and code review
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First session is free and lasts 30 minutes. We use it to work out what you need and whether I'm the right person for it. If I'm not, I'll say so and point you somewhere better.

Message me with what you're studying and what's giving you trouble, and I'll tell you honestly how I'd approach it.