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Since July 2026
Instructor since July 2026
Microsoft Power BI: From Beginner to Advanced – Data Analysis & Dashboard Creation
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From 18 £ /h
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Learn Microsoft Power BI from scratch and build interactive, professional dashboards using real-world business data. This course is designed for beginners as well as learners who want to improve their reporting and analytics skills.

During the class, you will learn:
• Introduction to Power BI Desktop
• Connecting and transforming data with Power Query
• Data modeling and relationships
• Creating interactive dashboards and reports
• DAX formulas and calculated measures
• Data visualization best practices
• Publishing and sharing reports with Power BI Service
• Real business case studies and hands-on exercises

By the end of the course, you will be able to build professional dashboards, analyze data efficiently, and make data-driven business decisions.
Extra information
Please have your own laptop with Microsoft Power BI Desktop installed. No previous experience is required for beginners.
Location
location type icon
Online from Morocco
About Me
Hello! I’m a Software Engineer and Data Analyst passionate about helping students develop practical data analytics skills. I specialize in Microsoft Power BI, SQL, Excel, and Python, and I enjoy making complex concepts easy to understand through hands-on learning and real-world projects.
My teaching approach is interactive, patient, and personalized. Whether you’re a complete beginner or looking to improve your business intelligence skills, I’ll guide you step by step at your own pace. Together, we’ll build professional dashboards, analyze data, and work on practical exercises that you can apply in your studies or career.
My goal is to help every student gain confidence and become independent in using Power BI for data visualization and business reporting.
Education
Software Engineering Degree – École Marocaine des Sciences de l’Ingénieur (EMSI), Rabat, Morocco. Specialized in Data Analysis, Business Intelligence, SQL, Python, and Microsoft Power BI.
Experience / Qualifications
• Software Engineer and Data Analyst
• 6-month Business Intelligence internship at ENOSIS Group
• Developed interactive Power BI dashboards for multiple business departments
• Experience with Power BI, SQL, Excel, Python, ETL, and Power Query
• Strong knowledge of data visualization, business reporting, and dashboard design
• Passionate about teaching and helping students achieve their learning goals
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
French
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
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Are you looking for efficiency, creativity and productivity in your daily tasks? Do not look any further. Microsoft Office is there to meet all your expectations.

Why choose Microsoft Office?

Create with Power: Word, Excel, PowerPoint and many other applications give you powerful tools to bring your ideas to life, whether it's for a professional document, a financial dashboard or a stunning presentation.

Collaborate with ease: OneDrive and Teams allow you to collaborate with your colleagues or friends, no matter where you are. Work together in real time, share files and communicate easily.

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Available Everywhere: Whether you're in the office, on the road, or at home, Microsoft Office is accessible on all your devices, allowing you to work wherever and whenever you want.

Join the Microsoft Office Revolution!

Don't let everyday challenges slow you down. Invest in the power of Microsoft Office and unlock your potential.


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Python is a powerful and versatile programming language with countless possibilities. You can use it for data analysis, image processing, automation, software development, hardware control, and much more.

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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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Overview

Transitioning from learning data science theory to solving actual business problems is the hardest step for any aspiring data professional. [Insert Chosen Course Title] is an intensive, mentor-led program designed to simulate a real-world data team environment. Instead of working through synthetic, pre-cleaned textbook datasets, you will take on messy, complex industry scenarios and turn them into end-to-end data products.

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1st lesson is backed
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Collaborate with ease: OneDrive and Teams allow you to collaborate with your colleagues or friends, no matter where you are. Work together in real time, share files and communicate easily.

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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.

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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.

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

👨‍🏫 About me
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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- Creating charts, pivot tables, and dashboards
- Using Excel for budgeting, planning, or managing projects
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- Preparing for Excel tests or improving job-related Excel skills

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- 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
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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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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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.
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Overview

Transitioning from learning data science theory to solving actual business problems is the hardest step for any aspiring data professional. [Insert Chosen Course Title] is an intensive, mentor-led program designed to simulate a real-world data team environment. Instead of working through synthetic, pre-cleaned textbook datasets, you will take on messy, complex industry scenarios and turn them into end-to-end data products.

What You’ll Experience

End-to-End Execution: Walk through the full data lifecycle—from problem scoping and data extraction to exploratory analysis, modeling, and executive stakeholder presentation.

Industry-Standard Workflows: Work with messy real-world datasets, practice Git-based version control, write production-ready code, and structure reports that business leaders actually care about.

1-on-1 & Group Mentorship: Receive continuous code reviews, architectural feedback, and project guidance mirroring the experience of working under a Senior Data Scientist or Analytics Lead.

Portfolio-Ready Deliverables: Graduate with 2–3 complete, polished projects that demonstrate actual business value to hiring managers—not just another churn prediction copy-pasted from Kaggle.

Who This Is For
Aspiring Data Analysts, Data Scientists, and recent graduates who know Python, but want the practical experience, confidence, and portfolio needed to land high-impact roles in the industry.
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