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Since March 2026
Instructor since March 2026
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1 repeat student
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Think Like a Software Engineer: Java & Kotlin & Best Practices
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From 50 C$ /h
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Learn Java and Kotlin from a practical, real-world perspective. This class focuses not only on programming languages, but also on essential software engineering skills such as clean code, testing, and working with databases.

Ideal for students and professionals who want to improve their coding abilities, build projects, and gain confidence in solving real problems.
Location
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Online from Spain
About Me
Hi! I’m a software engineer specializing in Java and Kotlin, with 7+ years of experience.
I help students and professionals not only learn programming, but also develop real software engineering skills.

My lessons are practical, focused on clean code, problem-solving, and real-world scenarios. I aim to create a supportive environment where you can learn with confidence and improve step by step.
Education
- Master’s Degree in Computer Science, University of Trento (UNITN)
- Master’s Degree in Computer Science, Eötvös Loránd University (ELTE)
- Bachelor’s Degree in Computer Science, University of Camerino
- Exchange Program (Erasmus), Metropolia University of Applied Sciences – Helsinki
Experience / Qualifications
- Specialized in Java and Kotlin development
- Solid understanding of software engineering best practices (clean code, testing, design principles)
- Experience with backend systems and databases
- Passion for mentoring and helping others grow as developers
Age
Preschool children (4-6 years old)
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
English
Italian
Spanish
Availability of a typical week
(GMT -04:00)
New York
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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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Harry
Hello,

I am 26 years experienced Online Tutor and Assignment Helper for Computer Science. I teach PHP, MySQL, Python, Machine Learning, Programming in C, C++, Java, ASP, C#.NET, Visual Basic, Oracle, HTML, VBScript, JavaScript, Data Structures, JQuery, Bootstrap , MS Office. I have teaching experience of teaching IT Professionals , students from different grades, graduate and post graduate classes for more than 22 years.

Presently I am teaching students online, providing homework assignment help, provide help in online tests for the students from USA ,UK, Canada, New Zealand, Germany, Australia, Malaysia, Austria, Malta, Saudi Arab etc. Also, I am in Software Development and Web Designing. I have helped more than 1200 students from different countries in last 26 years.

Regarding my teaching methodology, I always start teaching the concepts right from the scratch so that the students can learn concepts easily. I would like to describe you how do I teach through Internet . There is 100 % interaction between me and my students using Internet.

For starting the lessons with me all that you need is PC with Internet Connection and Headphone and Mic. Rest I will guide you about everything when you start learning with me. My lessons are also customized, planned and prepared according to the needs of individual student. I can also provide old student references if the student needs that.


If you join the course with me, I assure you that it will be value for your time and money.


Thanks
Harry
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Mustafa
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
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Similar classes
arrow icon previousarrow icon next
verified badge
Harry
Hello,

I am 26 years experienced Online Tutor and Assignment Helper for Computer Science. I teach PHP, MySQL, Python, Machine Learning, Programming in C, C++, Java, ASP, C#.NET, Visual Basic, Oracle, HTML, VBScript, JavaScript, Data Structures, JQuery, Bootstrap , MS Office. I have teaching experience of teaching IT Professionals , students from different grades, graduate and post graduate classes for more than 22 years.

Presently I am teaching students online, providing homework assignment help, provide help in online tests for the students from USA ,UK, Canada, New Zealand, Germany, Australia, Malaysia, Austria, Malta, Saudi Arab etc. Also, I am in Software Development and Web Designing. I have helped more than 1200 students from different countries in last 26 years.

Regarding my teaching methodology, I always start teaching the concepts right from the scratch so that the students can learn concepts easily. I would like to describe you how do I teach through Internet . There is 100 % interaction between me and my students using Internet.

For starting the lessons with me all that you need is PC with Internet Connection and Headphone and Mic. Rest I will guide you about everything when you start learning with me. My lessons are also customized, planned and prepared according to the needs of individual student. I can also provide old student references if the student needs that.


If you join the course with me, I assure you that it will be value for your time and money.


Thanks
Harry
verified badge
Mustafa
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
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