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Find the Best Online Computer Programming Tutors & Teachers for Private Lessons

For over a decade, our private Computer Programming tutors have been helping learners improve and fulfil their ambitions. With one-on-one lessons online, you’ll enjoy high-quality, personalised teaching that’s tailored to your goals, availability, and learning style.

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1665 online computer programming teachers

Ziad

France
52€

60-min

/h

Mathematics, Statistics, Signal Processing & Scientific Computing – University Level

Mathematics, Statistics, Signal Processing & Scientific Computing – University Level

I offer personalized university-level support in Mathematics, Statistics, Signal Processing, Data Analysis, and Scientific Computing for undergraduate, Master’s, and engineering students. Lessons are adapted to the student’s academic program, level, and objectives, whether the goal is to better understand theoretical concepts, solve exercises and problems, prepare for exams, analyze experimental data, or develop computational methods for scientific and engineering applications. Depending on the student’s curriculum and needs, lessons can cover: ¤ Mathematics — calculus, linear algebra, differential equations, complex numbers, numerical methods, Fourier analysis, and other mathematical tools used in physics and engineering ¤ Probability & Statistics — probability distributions, descriptive and inferential statistics, uncertainty, regression, statistical analysis, and interpretation of experimental data ¤ Signal Processing — time- and frequency-domain analysis, Fourier transforms, filtering, sampling, correlation, spectral analysis, noise analysis, and signal characterization ¤ Data Analysis — processing and visualization of scientific data, statistical interpretation, fitting, uncertainty analysis, and extraction of meaningful information from experimental measurements ¤ Scientific Computing — MATLAB and Python for numerical calculations, data processing, signal analysis, visualization, automation, and scientific applications My approach focuses on understanding the mathematical reasoning behind the methods rather than simply applying formulas or code. I help students connect theory with practical applications, understand why a particular method is appropriate, and progressively develop the ability to solve problems independently. I hold a PhD in Physics and have extensive experience using mathematics, statistics, signal processing, and scientific computing in experimental research. Throughout my doctoral work, I used MATLAB and Python for scientific data processing, filtering, correlation, noise and stability analysis, statistical characterization, and the analysis of optical measurements. Lessons can be provided in English or French, with explanations in Arabic whenever helpful.

Mustafa

verified teacher icon
Palestine
21€

60-min

/h

Data Mining Algorithms Training Course

Data Mining Algorithms Training Course

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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Our students evaluate their Computer Programming teacher.

To ensure the quality of our Computer Programming teachers, we ask our students to review them.

Only reviews of students are published and they are guaranteed by Apprentus. Rated 5.0 out of 5 based on 2 reviews.

“ Fantastic! Ilias really prepared well and took the time to understand what my son needed. He was very positive and encouraging and we look forward to the next lesson! ”

To ensure the quality of our Computer Programming teachers, we ask our students to review them.

Only reviews of students are published and they are guaranteed by Apprentus. Rated 5.0 out of 5 based on 2 reviews.