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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.
Read more
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Machine learning etc. I can customise the content to domains like business, economics finance and investments as per student requirements.

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Drafting and formatting administrative or personal documents.

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Understand what Artificial Intelligence is (like ChatGPT or Gemini) and use it as a personal assistant on a daily basis.

Detailed program (Syllabus)
The program is adaptable to your initial level. Each session includes simplified theory and plenty of practice.

Module 1: The basics of office software (Microsoft Word)

Exploring the interface: the keyboard, the mouse, and the word processing screen.

Enter, correct and format text (bold, italics, colours, alignment).

Write an official letter or a letter to loved ones.

Save, find your document on the computer, and print it.

Module 2: Daily Organization (Microsoft Excel)

Understanding the principle of tables and cells.

Create simple lists (address book, shopping list).

Create a tracking table for the monthly budget (expenses, income).

Use basic formulas (automatic sum) to have the computer calculate for you.

Module 3: The Arrival of Artificial Intelligence (Chatbots)

What is Artificial Intelligence (AI)? A simple explanation and demystification.

Discover virtual assistants (ChatGPT or Gemini): how to access them for free and securely.

The art of the "Prompt" (the query): how to ask the right questions to the machine to get the best answers.

Practical workshops with AI:

Ask him to draft a complex email.

Looking for recipe ideas using leftovers from the fridge.

Have him summarize a long newspaper article.

Organize a travel itinerary from A to Z.

Module 4: Safety and Best Practices

Recognizing reliable information in the face of AI "hallucinations" (errors).

The golden rules for protecting your personal data on the internet and in Office documents.

My Pedagogical Methodology
The approach is entirely personalized. I adapt to your learning pace. Each new concept is immediately followed by a practical, concrete exercise directly related to your everyday needs (writing to an administration, planning your vacation, etc.). Simplified course materials can be provided at the end of each session to help you review on your own.
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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
• 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

-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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 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.
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Lessons (1 hour online only) - 1.5 or 2 hours depending on the subject/level/location
Educational and organized teacher with excellent revision and organizational techniques
Microsoft Office - Excel/Word/PowerPoint - email - typing - ...

Educational and patient with a good methodology adapted to each student

97% success rate with regular monitoring

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I would guide you step by step to get familiarised with the tools of Microsoft Office programmes (Word, Excel, and PowerPoint) that you believe you might need to meet your purposes. Your level of experience will determine where we start and how we proceed through the course.
Contact me
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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.
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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.

Whether you:
- are starting completely from scratch,
- already have some basic knowledge,
- or want to solve a specific problem,
we’ll tailor the session to your needs. No one-size-fits-all course, just practical help that actually works for you.

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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.

🎯 Topics we can cover
- Smart formulas (IF, VLOOKUP, INDEX, MATCH, etc.)
- Creating charts, pivot tables, and dashboards
- Using Excel for budgeting, planning, or managing projects
- Data analysis and basic programming in Excel
- Preparing for Excel tests or improving job-related Excel skills

💻 Practical info
- Having your own laptop/PC is helpful, but not required
- Online or in-person sessions possible
- Have specific questions or files you’d like to work on? Let me know! I also have my own practice materials.

Feel free to send me a message with your question or goal. I’ll gladly look at how I can help you out!
verified badge
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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

🎯 My goal:

Helping you understand the logic behind the code

Progress at your own pace

Create your own projects in Python and gain independence
verified badge
Online Private Classes: Microsoft Word, Excel & PowerPoint
Unlock your productivity potential with personalized one-on-one training!

📘 Microsoft Word: Master document formatting, reports, and professional layouts.

📊 Microsoft Excel: Learn formulas, charts, and data analysis with ease.

🎤 Microsoft PowerPoint: Create impactful presentations that impress every audience.

👩‍🏫 Private & Flexible: Classes tailored to your level, schedule, and goals. 🌍 Online Access: Learn from anywhere, at your own pace. 🎯 Practical Skills: Focus on real-world applications for work, study, or personal projects.

👉 Start today and gain the confidence to use Microsoft Office like a pro!
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This course is specifically designed for seniors, young retirees, or anyone wishing to update their computer skills at their own pace. No prior technical knowledge is required. Patience, clarity, and a supportive approach are at the heart of this training.

Why take this course?
Information technology should no longer be a barrier, but a tool to simplify your daily life and keep you connected with loved ones. This program has two objectives: to make you completely proficient with standard software (Word, Excel) and to introduce you, in a simple and engaging way, to the new Artificial Intelligence (AI) technologies that everyone is talking about.

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Drafting and formatting administrative or personal documents.

Managing a family budget or creating simple organizational lists.

Understand what Artificial Intelligence is (like ChatGPT or Gemini) and use it as a personal assistant on a daily basis.

Detailed program (Syllabus)
The program is adaptable to your initial level. Each session includes simplified theory and plenty of practice.

Module 1: The basics of office software (Microsoft Word)

Exploring the interface: the keyboard, the mouse, and the word processing screen.

Enter, correct and format text (bold, italics, colours, alignment).

Write an official letter or a letter to loved ones.

Save, find your document on the computer, and print it.

Module 2: Daily Organization (Microsoft Excel)

Understanding the principle of tables and cells.

Create simple lists (address book, shopping list).

Create a tracking table for the monthly budget (expenses, income).

Use basic formulas (automatic sum) to have the computer calculate for you.

Module 3: The Arrival of Artificial Intelligence (Chatbots)

What is Artificial Intelligence (AI)? A simple explanation and demystification.

Discover virtual assistants (ChatGPT or Gemini): how to access them for free and securely.

The art of the "Prompt" (the query): how to ask the right questions to the machine to get the best answers.

Practical workshops with AI:

Ask him to draft a complex email.

Looking for recipe ideas using leftovers from the fridge.

Have him summarize a long newspaper article.

Organize a travel itinerary from A to Z.

Module 4: Safety and Best Practices

Recognizing reliable information in the face of AI "hallucinations" (errors).

The golden rules for protecting your personal data on the internet and in Office documents.

My Pedagogical Methodology
The approach is entirely personalized. I adapt to your learning pace. Each new concept is immediately followed by a practical, concrete exercise directly related to your everyday needs (writing to an administration, planning your vacation, etc.). Simplified course materials can be provided at the end of each session to help you review on your own.
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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
• 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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