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Since July 2025
Instructor since July 2025
Learn Python, SQL and MySQL databases from beginner to Advanced Level | Certified Data Science Professional
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From 26 € /h
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Learn
Python
SQL
Power BI
Excel
Data Science
Data Engineering
Data Analytics
Data Modeling
Data Warehousing
ETL
API's
Machine Learning
Lecture will be Hands-on, beginner friendly, solving real world problems just like I do in my job. So, if you’re just starting out like college, university student, switching careers, or preparing for a job in data field — book a trial lesson, and let’s begin your journey together. I look forward to seeing you in class!
Location
location type icon
Online from Germany
About Me
I'm a certified Data Scientist, Data Engineer, and Data Analyst with over 5 years of teaching and industry experience. I’ve taught students and professionals from around the world — helping them learn Python, SQL, databases, data analytics, and cloud technologies with confidence.

My teaching style is hands-on, project-based, and tailored to each student’s level — whether you're a complete beginner, university student, or a working professional. I believe in learning by doing, and my sessions focus on solving real-world problems using tools like Python, Power BI, SQL, and Oracle.

I've led corporate training, internship programs, and 1-on-1 online tutoring sessions, and many of my students now work in tech, banking, and multinational companies.

If you're looking for personalized, practical lessons that focus on results and clarity, I’d love to help you reach your learning goals.
Education
Bachelor's in Computer Science (CGPA: 3.78/4.0) from Virtual University of Pakistan.
Focused on software development, databases, data structures, and applied machine learning. Developed strong foundations in programming (Python, C++, Java), SQL, and algorithmic thinking. Participated in academic projects related to data analysis and automation.
Experience / Qualifications
5+ Teaching Experience
Oracle Certified Data Science Professional
Certified Automation Engineer
Certified Python Developer
Certified Oracle Database Professional
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
English
Urdu
Hindi
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
This course is designed for anyone who wants to learn practical data skills using Python, SQL, and Oracle databases. Whether you're a complete beginner or looking to strengthen your data handling skills, I’ll guide you step-by-step through core concepts and real-world projects.

You'll learn:

Python programming basics and data analysis tools for data science, data engineering and data analytics

SQL queries: SELECT, JOIN, GROUP BY, procedures, Functions and subqueries

How to work with Oracle Database: tables, indexes, stored procedures

Real-world use cases from business and data science

Best practices for clean, efficient, and scalable code

Lessons are project-based and tailored to your skill level, whether you're in school, switching careers, or preparing for a job in data or IT.
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Stuck in Excel? Want to finally understand how those formulas really work? Do you need to create a pie chart of your expense report for work or school, but feel completely lost? Don’t worry! I’m here to help... at your pace, with no jargon or complicated explanations.

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I’ve been using Excel since I was a teenager — for personal use (like budgeting tools or calorie trackers) and professionally as part of my work and studies in data analysis. Thanks to my experience and structured approach, I explain Excel in a clear and understandable way. I know how frustrating it can be when something technical doesn’t work... and how satisfying it feels when it finally clicks.

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Feel free to send me a message with your question or goal. I’ll gladly look at how I can help you out!
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Data skills can change your career, and you don't need to be a "math person" or have a technical background to learn them. If you can use a computer and you're curious, you can do this. In just 10 hours of one-to-one lessons, you'll go from "I don't know where to start" to analyzing real data on your own.

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Write SQL queries on your own. Select, filter, sort and group data, combine several tables with JOINs, and answer real questions such as "which product sold the most each month?"
Clean and analyze data in Excel. Use lookup formulas, conditional calculations and PivotTables, and build clear charts, instead of spending hours on manual work.
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Explain your results. Turn a business question into a query or a dashboard, check that your results make sense, and present them to someone who doesn't know the data.
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If you already know some Excel or SQL, we skip what you know and spend more time on what you need. If you want to go deeper later (for example advanced DAX, Python or statistics), we can continue from the same foundation.

How the lessons work

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I explain in English or French (or Arabic if that's easier for you), and I'm patient. There are no silly questions. If something doesn't click, I'll explain it another way until it does.

Who this class is for

Students in business, economics, engineering, science or social sciences
Job seekers and career changers who want a first step into data analysis
Professionals who want to automate reports and work faster
Complete beginners. No previous experience with SQL or Power BI is needed.
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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
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• 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

-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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.

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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.
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This course is designed for students and adolescents who want to build a strong beginner-friendly foundation in Computer Science.
Whether you're completely new to Computer Science or need help with a specific programming subject, the course can be customized to match your needs.
Need help with C++ or programming? You can provide me with your syllabus, course outline, or the topics you're studying, and I'll tailor the lessons around what you need to learn.
Learning with friends? Group lessons are also available, allowing you to learn together while following the same customized course.
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Algorithms & Logic: Designing data structures and solving problems.

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I’ve been using Excel since I was a teenager — for personal use (like budgeting tools or calorie trackers) and professionally as part of my work and studies in data analysis. Thanks to my experience and structured approach, I explain Excel in a clear and understandable way. I know how frustrating it can be when something technical doesn’t work... and how satisfying it feels when it finally clicks.

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- Having your own laptop/PC is helpful, but not required
- Online or in-person sessions possible
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Feel free to send me a message with your question or goal. I’ll gladly look at how I can help you out!
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This course is for anyone who wants to:

✅ Learn Python from the beginning
✅ Strengthen their programming skills

📚 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

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Helping you understand the logic behind the code

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Data skills can change your career, and you don't need to be a "math person" or have a technical background to learn them. If you can use a computer and you're curious, you can do this. In just 10 hours of one-to-one lessons, you'll go from "I don't know where to start" to analyzing real data on your own.

Maybe you're a student who needs to analyze data for a course or a thesis. Maybe you're looking for your first job in data, finance or business and want a skill that employers ask for. Maybe you already use Excel every day, but your reports take hours and you suspect there's a faster way. Or maybe you've never written a line of code and the idea feels intimidating. This class is built for all of you, and every lesson starts from where you are today.

I'm an engineer, and I work with data, statistics and programming (SQL, Python, R, Excel) every day. I don't teach theory for its own sake. I teach the way I use these tools in real work: you get a real question, a real dataset, and a clear method for turning one into the other.

In 10 hours, you will be able to:

Write SQL queries on your own. Select, filter, sort and group data, combine several tables with JOINs, and answer real questions such as "which product sold the most each month?"
Clean and analyze data in Excel. Use lookup formulas, conditional calculations and PivotTables, and build clear charts, instead of spending hours on manual work.
Build your first Power BI dashboard. Import data, clean it with Power Query, create interactive visuals, and share a report that looks professional.
Explain your results. Turn a business question into a query or a dashboard, check that your results make sense, and present them to someone who doesn't know the data.
Show a finished project. Leave with a portfolio-ready analysis you can use in an interview or in your thesis.

How the 10 hours are organized (we adapt this to your level and goal)

Hours 1-2: Foundations. We define your goal, check your level, and cover your first SQL queries (SELECT, WHERE, ORDER BY).
Hours 3-4: SQL in depth. Grouping and summarizing data, JOINs across tables, and cleaning messy data.
Hours 5-6: Excel for analysis. Advanced formulas, PivotTables, and charts.
Hours 7-8: Power BI. Importing and cleaning data, building visuals, and creating an interactive dashboard.
Hours 9-10: Your project. We put everything together on a realistic dataset (sales, finance, HR or your own data), and I give you feedback and a plan for what to practice next.

If you already know some Excel or SQL, we skip what you know and spend more time on what you need. If you want to go deeper later (for example advanced DAX, Python or statistics), we can continue from the same foundation.

How the lessons work

Every lesson is one-to-one and tailored to you. You practice on your own screen with my guidance, so you learn by doing rather than by watching. Between sessions I can give you short exercises if you want extra practice, and you can send me questions anytime. If you have your own files or work data (anonymized, of course), we can use them, which makes the learning much more relevant. Otherwise I provide practice datasets.

I explain in English or French (or Arabic if that's easier for you), and I'm patient. There are no silly questions. If something doesn't click, I'll explain it another way until it does.

Who this class is for

Students in business, economics, engineering, science or social sciences
Job seekers and career changers who want a first step into data analysis
Professionals who want to automate reports and work faster
Complete beginners. No previous experience with SQL or Power BI is needed.
verified badge
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.
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I'm a working MEP engineer, currently building Python automation for Revit workflows daily - plan checks, model coordination, and repetitive drafting tasks. I teach other engineers, architects, and BIM professionals how to do the same, using pyRevit and real project workflows, not toy examples.

Topics include:
pyRevit fundamentals and setup
Automating repetitive Revit tasks (model checks, plan generation, data extraction)
Writing custom scripts for your firm's specific workflows
Applying Python automation to real MEP/BIM projects
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Master Microsoft Excel with personalized courses!

Do you want to learn Excel or improve your skills? Whether you are a beginner, student, professional or entrepreneur, I offer courses tailored to your level and objectives.

✅ Discovering the Excel interface
✅ Data entry and formatting
✅ Essential formulas and functions (SUM, IF, VLOOKUP/XLOOKUP, INDEX, etc.)
✅ Professional charts and graphs
✅ Sorting, filters, and conditional formatting
✅ Pivot tables
✅ Data management and analysis
✅ Tips to save time and improve your productivity

🎯 Why choose these courses?

100% personalized training
Simple and practical explanations
Practical exercises and projects
Flexible hours
Step-by-step guidance
Suitable for students, employees, entrepreneurs and job seekers

Develop an essential skill and improve your efficiency with Microsoft Excel. Book your first lesson today and progress quickly with professional guidance!
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This course is designed for students and adolescents who want to build a strong beginner-friendly foundation in Computer Science.
Whether you're completely new to Computer Science or need help with a specific programming subject, the course can be customized to match your needs.
Need help with C++ or programming? You can provide me with your syllabus, course outline, or the topics you're studying, and I'll tailor the lessons around what you need to learn.
Learning with friends? Group lessons are also available, allowing you to learn together while following the same customized course.
Good-fit Instructor Guarantee
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