Computer science courses (Algorithms, Python, SQL, Power BI and MS office)

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From 28 € /h

Hi there!
I’m a Data Scientist and Data Analyst with over five years of experience, here to help you master Python, algorithms, SQL, and Power BI.
I focus on practical, real-world applications, so whether you're just starting or looking to advance, my lessons will equip you with the skills to succeed. Let's make learning fun and effective!

Location

Online from Belgium

About Me

Hello! I'm a Data Scientist and Data Analyst with six years of online teaching experience, specializing in Python, algorithms, SQL, and Power BI. With over five years in the industry, I know how to simplify even the most complex concepts, making learning accessible and engaging for all levels. My goal is to make these essential data skills both understandable and practical, so you can confidently apply them to real-world challenges. Let's work together to unlock the power of data and take your skills to the next level!

Education

2019 - 2021: Ecole-IT University, Belgium - Engineering Degree in Software Engineering/Programming with a specialization in Data Science

2014 - 2017: ISITCOM, Hammam Sousse, Tunisia - Advanced Technician Diploma in Information and Communication Science and Technology (ISTC)

2014: High School Diploma in Computer Science (Honors: Fairly Good), Sahline High School, Tunisia

Experience / Qualifications

Data Analyst at Randstad Digital (2024 – Present, Leuven, Belgium)
Created interactive dashboards in Tableau for key departmental metrics.
Automated data cleaning and preprocessing tasks to enhance data accuracy and consistency.

Data Scientist at Redpharma/IQVIA (2021 – 2023, Brussels, Belgium)
Developed machine learning models for brand identification.
Improved product classification by refining Python algorithms.
Optimized SQL views and generated monthly stakeholder reports.
Enhanced ELT flow efficiency, speeding up data processing and improving accuracy.

Data Analyst at TakiAcademy (2017 – 2020, Sousse, Tunisia)
Analyzed student engagement patterns and created Power BI reports.
Built an ETL pipeline that improved data processing speed and accuracy.

Age

Teenagers (13-17 years old)

Adults (18-64 years old)

Seniors (65+ years old)

Student level

Beginner

Intermediate

Duration

60 minutes

90 minutes

120 minutes

The class is taught in

French

Arabic

English

Skills

Algorithms

Python

Networking

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

Dr.

5.0

(15)

I am Dr Iyer- a tutor with over 18 years of teaching experience as of 2023 and students from across the globe. I teach one-on-one online (over Skype/ Google Hangout and other media) using a pen tablet and the screen-share feature.

I have helped several students in courses like Python Programming, R Programming, Data Science,
Machine learning etc. I can customise the content to domains like business, economics finance and investments as per student requirements.

I have taught students of various age groups - high school (IB/Cambridge/IGCSE/ ICSE,) University (bachelors, masters, doctoral) and working industry professionals.

More than anything, I trust that if I can replace the fear of a subject with love for it, then I would have truly made a difference to the student.

Matías

5.0

(15)

Python is one of the best, if not the best, language to start learning programming. It is also one of the most widely used languages today, especially in cutting-edge areas such as machine learning.

This popularity means that Python is constantly evolving. It offers a wide range of tools and libraries, which are free and very varied.

As an aeronautical engineer, I like to share my knowledge and derive satisfaction from it by teaching and motivating others.

I'm used to working with people of different ages. I believe in the importance of segmenting learning, visualizing progress, setting concrete goals and practicing regularly.

Beyond these general principles, there is no magic rule or method. Some approaches work with some students but not with others. Adaptation to individual needs is therefore the main objective of private lessons. So I will do my best to find what motivates and helps my student.

Adam

5.0

(15)

Learn to code with method and logic
Whether it's to succeed in the NSI (Digital Sciences and Technology) specialization in high school, design personal projects, or prepare for higher scientific studies, mastering code relies on solid algorithmic thinking. I help students understand the structure of programming languages and the logic of data.

Subject areas and languages taught:

Algorithms & Logic: Designing data structures and solving problems.

Programming Languages: Python, C/C++, C# and Java.

Data Management: Analysis and SQL queries / databases.

Basic Web Development: HTML & CSS for creating structured pages.

The goal is to take the student from simply writing code to true autonomy in development.

Amin

5.0

(15)

Python is the most in-demand programming language in the world right now — and one of the easiest to learn with the right guidance.
Whether you've never written a line of code or you're a student who needs to pass a programming course, this is a practical, no-fluff introduction that gets you writing real code from session one.
What we can cover depending on your goals:

Python fundamentals: variables, loops, functions, data structures
- Object-oriented programming (OOP)
- Data manipulation with pandas and NumPy
- Introduction to machine learning with scikit-learn
- Database management with SQL
- C and Java upon request
- MATLAB and R available for engineering/science students

Why learn with me?
I'm not a student teaching on the side — I'm a professional engineer who uses Python daily for data analysis, modeling, and automation. I know exactly which concepts matter in the real world and which ones you can skip for now.
Sessions are 100% personalized: I adapt the pace, the examples, and the exercises to your background and your goal — whether that's passing your university exam, building a project, or landing a job.

Salah

5.0

(15)

Master Python with Personalized Courses

Discover the art of programming with Python courses tailor-made to meet your specific needs. Whether you are a beginner, intermediate or professional, my lessons are suitable for all levels.

Why Choose My Courses?

Personalized Teaching Approach: Each course is tailored to your skill level and individual goals.

Practical Experience: Learn by doing with real-world projects that build your understanding and skills.

Ongoing Support: Get unlimited email support for any questions you have between sessions.

As a Python expert, I have a passion for teaching and sharing my knowledge. My goal is to guide you effectively in your learning journey.

Book Your First Lesson:

Start your journey to Python mastery now by booking your first lesson. Whether you aspire to enter the development field or hone your existing skills, these courses are designed for you.

Amine

5.0

(15)

Discover programming lessons suitable for children! With a fun and educational approach, my lessons allow young minds to dive into the fascinating world of programming. Provide your children with an enriching learning opportunity in a fun and stimulating environment.

Baia

5.0

(15)

I teach Python specifically for finance and data applications - the kind used in economics, business analytics, and quantitative programs. This isn't a general "learn to code" course; it's built around real financial data, benchmarking, and the workflows you'll actually use in coursework or early career work.

Topics include:
Python fundamentals through a finance lens (data structures, functions, control flow).
Working with financial data and datasets.
Performance benchmarking and writing efficient code.
Applying concepts from Hilpisch's Python for Finance.

Amal

5.0

(15)

This mathematics course is aimed at students in lower or upper secondary education who wish to strengthen their basic skills, fill in gaps or effectively prepare for an exam.

We will work together on understanding key concepts (arithmetic, geometry, algebra, functions, etc.) through clear explanations, progressive exercises and a method adapted to the pace of each student.

The goal is to restore confidence, develop logic and reasoning skills, and make mathematics more accessible and concrete.

This course is also suitable for students in scientific, technical or linguistic fields who need regular or occasional support.

Mariem

5.0

(15)

Understanding algorithms is essential for anyone pursuing computer science or programming. In this course, I provide a structured approach to learning algorithms—from fundamental concepts like sorting and searching to advanced techniques such as dynamic programming and graph theory. Whether you're preparing for exams, coding interviews, or aiming to improve your problem-solving skills, this course is designed to build strong algorithmic thinking. Through practical examples and step-by-step explanations, I help students master the logic and techniques needed to write efficient and elegant code.

Ali

5.0

(15)

I teach machine learning, AI and Python online to university students, postgraduates, career changers and serious beginners across the UK and Europe. Lessons are in English.

I hold an MSc in Electronics and Electrical Engineering with Distinction and I teach as a Visiting Lecturer on a Master's level module covering data analytics, machine learning and generative AI at a UK university. I also have two accepted international conference papers on deep learning for image classification. I set and mark postgraduate assignments myself, so I know where marks are won and lost on this kind of work.

Who this is for

Undergraduates and postgraduates on AI, ML, data science or computer science modules at any European university. Final year, Master's and thesis students working on a machine learning project. IB and A Level students moving into computing or engineering. Professionals retraining for data roles. Complete beginners who want to learn Python properly rather than copying it from videos.

I work with students on UK, IB and continental European programmes. I have tutored engineering students in Germany and international school students across several countries, so an unfamiliar syllabus or a module taught in a different structure is not a problem. Send me the material and I will work from it.

What we cover

Python for data science with NumPy, Pandas, Matplotlib and scikit-learn. Deep learning using TensorFlow and Keras. Core theory including regression, classification, clustering, decision trees, random forests, neural networks and CNNs, together with the linear algebra, calculus and statistics underneath them. Computer vision and image classification, which is my published research area. Model evaluation, overfitting and hyperparameter tuning. Writing machine learning work up to academic standard, covering methodology, results and critical evaluation.

How lessons work

Send me your module handbook, assignment brief, thesis spec or the code that will not run, and I plan the session around it before we meet. Nothing generic.

In the lesson I explain the concept with a worked example, then you take the keyboard while I watch and correct, because you learn far more doing it than watching me do it. You finish with annotated notes and a clear next step, and you can message me between sessions with questions.

Practical details

Online over Google Meet or Zoom with screen sharing and a shared whiteboard. Sessions run 60 or 90 minutes. I am based in the UK and teach across GMT and Central European time, with evening and weekend slots that suit students anywhere in Europe.

Tell me your course, your deadline and exactly where you are stuck, and I will come back with a plan for the first session.

Ammar

5.0

(15)

A- TOPICS YOU CAN EXPLORE AND MASTER:
1- PYTHON FOUNDATIONS
• Variables, data types, operators, conditional structures, loops, functions, modules, files, exceptions, and object-oriented programming
• Lists, tuples, dictionaries, sets, comprehensions, debugging, and writing clear, reusable, well-structured code
• Jupyter Notebook, Anaconda, Visual Studio Code, virtual environments, and package management

2 — PROGRAMMING, ALGORITHMS, AND COMPUTER SCIENCE FOUNDATIONS
• Algorithmic thinking, problem decomposition, pseudocode, flowcharts, procedural programming, object-oriented programming, recursion, and modular program design
• Fundamental data structures including arrays, lists, stacks, queues, dictionaries/hash tables, sets, trees, graphs, and their appropriate use
• Searching, sorting, traversal, algorithm efficiency, computational complexity, Big-O notation, debugging, testing, code organization, and problem-solving strategies
• Practical programming exercises ranging from beginner problems to university-level algorithmic and computational challenges

3- DATA PREPARATION AND EXPLORATION
• NumPy and pandas for importing, cleaning, transforming, filtering, grouping, reshaping, and merging data
• Missing values, duplicates, outliers, inconsistent formats, data leakage, and data-quality validation
• Exploratory data analysis using descriptive statistics, Matplotlib, Seaborn, and graphical interpretation

4- MATHEMATICAL FOUNDATIONS
• Linear algebra, vectors, matrices, derivatives, optimization, probability, and statistics
• Loss functions, gradients, distance measures, regularization, likelihood, and model complexity
• Mathematical concepts are explained according to the learner’s level and the requirements of the selected algorithms

5- SUPERVISED MACHINE LEARNING
• Linear and polynomial regression, logistic regression, and regularized models
• k-nearest neighbours, decision trees, random forests, gradient boosting, support vector machines, and Naive Bayes classifiers
• Classification, regression, model assumptions, decision boundaries, feature importance, and interpretation of results

6- UNSUPERVISED LEARNING
• Clustering using k-means, hierarchical clustering, and density-based methods
• Principal component analysis, dimensionality reduction, anomaly detection, and pattern or structure discovery
• Method selection, evaluation of data structure, and interpretation of results without predefined labels

7- MODEL EVALUATION AND IMPROVEMENT
• Training, validation, and test sets; cross-validation; hyperparameter optimization
• Accuracy, precision, recall, specificity, F1 score, ROC–AUC, confusion matrices, MAE, MSE, RMSE, and R2
• Underfitting, overfitting, bias–variance trade-off, class imbalance, feature engineering, feature selection, scaling, and regularization

8- DEEP LEARNING
• Neural-network foundations, activation functions, forward propagation, backpropagation, and gradient descent
• Multilayer perceptrons, convolutional neural networks, recurrent neural networks, and Transformer foundations
• TensorFlow, Keras, or PyTorch depending on the learner’s project and working environment

9- ARTIFICIAL INTELLIGENCE APPLICATIONS
• Natural language processing, text classification, embeddings, sentiment analysis, and foundations of language models
• Computer vision, image classification, fundamental principles of object detection, and image preprocessing
• Recommendation systems, forecasting, anomaly detection, intelligent automation, and decision-support applications

10- GENERATIVE AI AND LARGE LANGUAGE MODELS
• Transformer architecture, tokens, embeddings, attention mechanisms, prompt engineering, Retrieval-Augmented Generation (RAG), and model evaluation
• Use of artificial-intelligence APIs, vector databases, document-retrieval systems, and structured AI-enabled workflows when relevant
• Reliability, hallucinations, bias, privacy, responsible use, and appropriate human validation

11- TOOLS AND LIBRARIES
• Python, NumPy, pandas, Matplotlib, Seaborn, scikit-learn, SciPy, Statsmodels, TensorFlow, Keras, and PyTorch
• Jupyter Notebook, Anaconda, Visual Studio Code, Git, GitHub, SQL, Excel, and Power BI when useful to the project
• Additional libraries may be introduced depending on the selected specialization and dataset

12- PROJECTS, RESEARCH, AND INTERVIEW PREPARATION
• Complete projects covering data preparation, model development, evaluation, interpretation, and presentation of results
• University assignments, dissertations, theses, research projects, portfolio projects, technical interviews, and professional applications
• Code review, debugging, documentation, reproducibility, model comparison, and communication of results

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B- PERSONALIZED TUTORING: LEARNING HOW TO REASON
Machine learning and artificial intelligence become much more accessible when mathematics, algorithms, Python code, data, and real-world applications are clearly connected.

My lessons help you move beyond simply copying code or using models as “black boxes.” You will learn how to define the problem correctly, prepare the data, select an appropriate algorithm, understand how it works, train and evaluate the model, diagnose errors, improve performance, and interpret results rigorously and responsibly.

Each lesson is personalized according to your current level, mathematical background, programming experience, dataset, university work, research project, interview preparation, or professional objective. We begin by identifying your existing knowledge, software environment, expected outcomes, and main conceptual or technical difficulties. We then establish a structured learning plan.

The first lesson combines a discussion of your background, objectives, and tutoring needs; an initial assessment of your current knowledge; personalized planning and organization of future sessions; and a short trial lesson to determine the most effective learning approach.

A typical session may include conceptual explanation, development of mathematical intuition, live coding, guided implementation, model evaluation, technical problem solving, and a concise summary of the next steps.

You may work with your own dataset, university assignment, research project, or professional problem, provided that confidential information is handled appropriately. I can also provide structured examples and datasets suited to your level.

My goal is not simply to help you run an algorithm. It is to help you understand why it is appropriate, how it learns from data, how to evaluate it correctly, why it may fail, and how to build a reliable, interpretable, and scientifically rigorous solution.

Pallavi

5.0

(15)

Learn Python with a structured, hands-on approach! Whether you are a absolute beginner looking to start coding, a student needing help with coursework, or a professional aiming to automate tasks, these lessons are tailored for you.What we will cover:
Core Fundamentals: Variables, loops, functions, and data structures.Object-Oriented Programming: Building reusable and clean code.Real-World Projects: Creating scripts, data analysis, or web scraping based on your goals.

Problem Solving: Learning how to debug and think like a programmer. Lessons are highly interactive. We will write code together from day one, and you will receive practical exercises after every session to build your confidence.

Laith

5.0

(15)

Ever wondered what really happens when you open a website or hit "send"? Let's find out together.

I've spent 3 years working hands-on with real networks: analyzing live traffic, managing firewalls, and troubleshooting network issues for government, financial, and telecom organizations. I'm Fortinet certified (FCA, FCF) and hold the CNSP (Certified Network Security Practitioner) certification. I'll teach you networking the way it's actually used on the job. No boring theory dumps, just live, hands-on learning you can use right away.

You'll learn:

How the internet works: IP addresses, DNS, DHCP, and HTTP/HTTPS
The OSI and TCP/IP models, made simple
Subnetting, routing, switches, and firewalls
TCP vs. UDP, ports, and how connections work
How to read real traffic with Wireshark and test networks with ping, traceroute, and Nmap
How to spot suspicious traffic and keep a network healthy

Perfect for: beginners, career changers, students, developers, and anyone preparing for Network+ or CCNA.

You'll walk away with a clear picture of how data travels, the confidence to troubleshoot real network problems, and a rock-solid foundation for your IT career.

No experience needed, just curiosity and a computer. Book your first session and let's dive in!

Baptiste

5.0

(15)

My method is based primarily on practice, because I believe that it is by writing code and trying to solve problems that one truly progresses in programming.
Each lesson begins with an explanation of the new concepts we will cover. I then provide exercises tailored to the student's level, which we work through together gradually. The goal is not simply to give the answer, but to understand the reasoning that leads to the solution.
I adapt to each student's pace and difficulties: if a concept isn't understood, we take the time to review it with simple examples before returning to practice. Conversely, if the basics are mastered, we can move on to more complex exercises.
A typical lesson therefore usually takes place in three stages: review or discovery of a concept, guided practical exercises, then correction and explanation of errors.
My courses are primarily aimed at beginners and students discovering Python, particularly in high school or the first years of higher education. I can help them understand the basics of the language: variables, conditions, loops, functions, lists, dictionaries, etc.
My goal is for the student to gradually become autonomous when faced with an exercise, rather than simply learning solutions by heart.

Cedric

5.0

(15)

Vous souhaitez apprendre Python, découvrir la programmation ou renforcer vos bases ?
Enseignant en informatique et titulaire d’un Master en Sciences Informatiques, orientation Data Science, je propose des cours adaptés à votre niveau et à vos objectifs.
Nous pouvons travailler notamment sur les bases de Python, les variables et types de données, les conditions, les boucles, les fonctions, les structures de données, les fichiers, ainsi que la résolution de problèmes et les premiers projets en Python.
Mon approche est avant tout pratique : j’explique les notions progressivement, avec des exemples simples, puis nous les appliquons à travers des exercices. Le contenu et le rythme sont adaptés aux difficultés et aux objectifs de chaque apprenant.
Les cours s’adressent aux débutants, étudiants ou adultes souhaitant apprendre Python ou consolider leurs connaissances en programmation.