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Since December 2020
Instructor since December 2020
Translated by GoogleSee original
Relational and non-relational database ( SQL / NoSQL ) (MongoDB)
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From 25 C$ /h
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I offer courses in spring and .Net frameworks,
java, php and c # courses
basic web development technologies such as html, css and javascript
relational and non-relational database and NoSQL SQL (MongoDB) courses

a PC and IDE
Location
location type icon
Online from Tunisia
About Me
I am a computer engineering student specializing in software architecture,
sharing knowledge and making information at everyone's fingertips is what I intend to do.
I can help you revise well for your exams, as well as assist you with your projects.
Education
computer engineering at the private higher school of engineering and technology (ESPRIT)
certified in Big Data environment, Data clustering, Data managing by cloudera
Experience / Qualifications
realization of various academic projects (web / mobile / desktop), follow-up to the development of websites during internships within companies or personal follow-up.
Age
Children (7-12 years old)
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Advanced
Duration
60 minutes
The class is taught in
French
English
Arabic
Reviews
Availability of a typical week
(GMT -04:00)
New York
at teacher icon
Online via webcam
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
We offer courses in the Spring framework and more. net,
Cours of java, php and c#
Basic web development technologies such as HTML, CSS and Javascript
From the base of donation links and non-links and SQL NoSQL (MongoDB)
Read more
I offer courses in spring and .Net frameworks,
java, php and c # courses
basic web development technologies such as html, css and javascript
relational and non-relational database and NoSQL SQL (MongoDB) courses

a PC and an IDE
Read more
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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
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2-1 Data Types, Characteristics, and Features
2-2 Statistical Description of Data
2-3 Visualization of Data
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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
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4-10 Measuring the type and strength of the relationship in dependency and correlation rules
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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
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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
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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
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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
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Contact Beheddine
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1st lesson is backed
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Adam
Master Algorithmic Logic and Development
Learning to program at the university or engineering school level is not just about writing lines of code: it's about learning to analyze complex problems, construct logical reasoning, and develop effective solutions. Holding a PhD in Computer Science and a State Engineering Diploma, I bring my more than 35 years of experience to bear on helping students and professionals achieve complete mastery of programming.

Expertise and Pedagogy through Practice
Whether you're preparing for an exam, a complex academic project, or a technical interview, my goal is to make you completely independent. We will work together on:

Essential languages: Python, Java, and SQL (databases).

Advanced algorithms and data structures.

Object-Oriented Programming (OOP).

Optimization, structured design and debugging of your programs.

Rather than memorizing code, you will acquire the good programming practices required in higher education and business, including the thoughtful use of assistive tools (AI) to verify and improve your solutions.

Work Environment and Requirements
Programming requires precise, interactive, and technical guidance. Therefore, it's essential to note that the course is conducted via webcam and video conference with screen sharing. This format allows us to write, test, and debug your code together in real time, ensuring extremely rapid progress.

To ensure a serious and immediate commitment to your development projects, a trial lesson or brief webcam chat is not part of my approach. We dive straight into analyzing your code and resolving your issues from our very first session.

Support Formats

60-minute session: Ideal for unlocking a specific bug, understanding a complex algorithmic concept, or correcting a targeted program.

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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
Good-fit Instructor Guarantee
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