Master machine learning from fundamentals to production. Build real ML systems, train deep learning models, and deploy them to production. Plus a comprehensive capstone that showcases your abilities to employers.
37 Weeks • 100% Online & Flexible • Live Hands-On Labs • Portfolio + Capstone • 1:1 Mentorship
✅ Python fundamentals included • ✅ ML fundamentals built-in • ✅ 100% Recorded & Rewatchable
Land in-demand Data Science & ML Engineer roles after completing the diploma
Build, train, and deploy ML models that power real applications and solve business problems.
Design and implement neural networks for computer vision, NLP, and other complex AI tasks.
Manage ML pipelines, automate model training, and ensure production ML systems run smoothly.
Work with large-scale data using Spark, Hadoop, and cloud platforms to build data pipelines.
Use advanced ML models to forecast trends, predict customer behavior, and drive business decisions.
Explore cutting-edge ML techniques and contribute to research in AI, NLP, and computer vision.
Entry-level Data Science & ML roles in Egypt and the MENA region
EGP/Month (Entry)
EGP/Month (1–2 Years)
Remote/International
A comprehensive path from Python fundamentals through production ML systems
From Python basics through supervised learning, unsupervised learning, deep learning, and production deployment. A structured progression that builds your skills systematically.
Build actual ML systems: house price prediction, image classification, NLP models, and time series forecasting. Portfolio projects that showcase your work to employers.
6 courses = 5+ projects + 1 comprehensive capstone. Graduate with a portfolio of end-to-end ML systems you can walk employers through in interviews.
Entry interview (skills mapping), mid interview (ML checkpoint), and final interview (portfolio defense + mock technical)—all geared for DS/ML roles.
Live coding labs during every session + graded assignments afterward. Write real Python code, train models, and debug issues—not just watching lectures.
Learn how to package and deploy ML models to production. Understand pipelines, monitoring, and real-world challenges—skills that make you hireable as a junior ML/MLOps engineer.
37 weeks • 300+ hours • 12+ comprehensive courses • Real projects & capstone
Get started with data science environment
Week 1 • 8h
Kick off your data science journey. Understand what data science is, explore career paths, and set up your professional development environment with Python, Anaconda, and Jupyter Notebooks.
Development Environment Setup
Verify complete setup by importing key libraries and creating a test plot in Jupyter.
Advanced formulas, Pivot Tables & Power Query
Weeks 1-1.5 • 32h
Master advanced Excel for business analytics. Learn complex formulas, Power Query for data transformation, Power Pivot for modeling, DAX for calculations, and build dynamic dashboards with slicers.
Sales Analytics Dashboard
Build end-to-end dashboard using Power Query, relationships, DAX measures, and interactive slicers.
Query, join & aggregate data like a pro
Weeks 1.5-3 • 16h/week
Master SQL for analytics and data engineering. Learn relational concepts, write complex queries with joins and aggregations, design tables, and optimize performance. PostgreSQL focus with real-world data.
E-Commerce Analytics
Build complete analysis pipeline with joins, aggregations, window functions, and business insights.
Data modeling, DAX & interactive dashboards
Weeks 4-5 • 14h/week
Master business intelligence with Power BI. Build reliable data models, write DAX measures, create interactive reports, and publish dashboards for real stakeholders.
Sales Performance Dashboard
Build end-to-end dashboard with Power Query, star schema, core DAX measures, and row-level security.
Turn analysis into actionable insights for stakeholders
Week 6 • 16h
Master the art of translating analysis into compelling stories. Learn narrative structure, visualization design, executive communication, and ethical data presentation.
End-to-End Presentation
Create 1-page brief + 5-7 slide deck with clear narrative, compelling visuals, and business impact.
Mathematical foundations for machine learning
Weeks 7-8 • 16h/week
Master the mathematical foundation for machine learning. Learn vectors, matrices, eigenvalues, linear transformations, derivatives, and optimization techniques.
Dimensionality Reduction Project
Apply PCA for dataset reduction and visualization. Demonstrate mathematical understanding.
Hypothesis testing, A/B testing & inference
Weeks 9-10 • 16h/week
Master statistical foundations and A/B testing. Learn probability distributions, hypothesis testing, confidence intervals, Bayesian inference, and design experiments for product decisions.
Complete A/B Testing Case Study
Design experiment, calculate sample size, run analysis, and deliver business recommendation.
From fundamentals to data manipulation & visualization
Weeks 11-15 • 12h/week
Master Python for data science. From syntax and data structures to NumPy, Pandas, Matplotlib/Seaborn, and APIs. Build strong programming foundations with practical examples.
Data Processing Pipeline
Build EDA notebook: clean data, visualize distributions, derive insights, create sharable notebook.
Regression, classification & ensemble methods
Weeks 16-17 • 20h/week
Master supervised learning algorithms. Build regression and classification models, tune hyperparameters, use ensemble methods like Random Forests and XGBoost, and evaluate with appropriate metrics.
End-to-End Prediction Model
Build, train, tune, and evaluate a supervised model with complete evaluation report.
Clustering, dimensionality reduction & fraud detection
Weeks 18-19 • 20h/week
Discover patterns in unlabeled data. Learn clustering algorithms, dimensionality reduction techniques, and anomaly detection methods for real-world applications.
Clustering & Segmentation Analysis
Perform clustering analysis with business insights and recommendations for each segment.
Neural networks, CNNs, RNNs & transfer learning
Weeks 20-21 • 22h/week
Master deep learning. Build neural networks from scratch, implement CNNs for computer vision, RNNs for sequences, and leverage transfer learning with TensorFlow/Keras.
CNN Image Classification
Build CNN for image task with data augmentation, training, evaluation, and deployment planning.
Interactive dashboards & storytelling
Week 26
Master interactive visualizations with Plotly and build dynamic dashboards with Dash. Create compelling, interactive data stories for stakeholders.
Interactive Dashboard
Build complete interactive dashboard with data loading, multiple visualizations, and user controls.
MongoDB, Redis & scalable pipelines
Week 27
Master NoSQL databases and data engineering fundamentals. Work with MongoDB for document storage, Redis for caching, and design scalable data pipelines.
Product API with Caching
Build MongoDB API with Redis caching layer for optimal performance.
Systematic ML experiments & model management
Week 28
Master experiment tracking and model management with MLflow. Track parameters, metrics, and artifacts systematically for reproducible ML workflows.
Tracked ML Pipeline
Track multiple model experiments, compare, and promote best model to production stage.
Distributed data processing & ML at scale
Weeks 29-30 • 20h/week
Master distributed data processing with Apache Spark. Process massive datasets, implement distributed ML pipelines, and optimize Spark jobs for production.
Distributed ML Pipeline
Build end-to-end Spark pipeline: data cleaning, feature engineering, training, and evaluation.
Cloud computing, EC2, S3, SageMaker basics
Weeks 31-32 • 16h/week
Learn essential AWS services for data science. Navigate AWS Console, work with S3 for data storage, launch EC2 instances, and explore SageMaker basics.
End-to-End AWS Pipeline
Data in S3, model training on EC2, artifacts back to S3, batch predictions.
Docker, Kubernetes, CI/CD & monitoring
Weeks 33-34 • 16h/week
Master production ML deployment. Containerize applications with Docker, orchestrate with Kubernetes, build CI/CD pipelines, and monitor models in production.
Production ML System
Containerized model with CI/CD pipeline, deployed to K8s with automated monitoring.
Text processing, RNNs, transformers & BERT
Weeks 23-25 • 16h/week
Master NLP from fundamentals to production. Build text pipelines, implement RNNs/LSTMs/GRUs, fine-tune transformer models like BERT for real-world NLP tasks.
Production NLP System
Fine-tuned transformer model for text task with evaluation, error analysis, and deployment readiness.
Your complete end-to-end data science system
Weeks 35-37 • independent
Apply everything you've learned by building a complete, real-world ML project from problem definition through production deployment. This becomes your portfolio centerpiece for interviews.
Portfolio Showcase
This capstone becomes your main talking point in interviews. Walk through your project, explain decisions, and demonstrate your complete ML expertise.
Weeks of Learning
Total Training Hours
Comprehensive Courses
Lifetime Access
Interactive, hands-on, and designed for busy professionals
Learn from anywhere at any time. All sessions recorded for access whenever you need them.
Every session includes live coding, debugging, and real problems. Not just theory.
Regular assignments with feedback to reinforce learning and build your portfolio.
Build ML systems, train models, and deploy them. Capstone showcases your best work.
8-10 hours/week
Perfect if working or studying. 3-4 months to complete.
15-20 hours/week
For full-time commitment. 6-8 weeks to complete.
25+ hours/week
Accelerated bootcamp style. 4-6 weeks to complete.
This diploma is for anyone serious about becoming a job-ready Data Scientist
If you're a beginner, consider our Data Analysis Diploma first to build foundational skills in Excel, SQL, and Python. Or reach out to us at ceo@elevorix.com—we can recommend a starting path based on your background.
Everything you need to succeed as a Data Scientist
Official Data Science Diploma from Elevorix Academy. Recognized credential for your career.
5+ completed ML projects, deep learning models, NLP systems, and a comprehensive capstone.
Help organizing your projects and notebooks into a professional GitHub profile employers love.
Unlimited access to all course recordings. Rewatch, review, and refresh your knowledge anytime.
Personalized guidance on projects, debugging, and charting your Data Science career path.
Join a network of Elevorix learners and alumni pursuing Data Science and AI careers.
Professional CV and LinkedIn profile review tailored for ML/Data Science roles.
Mock interviews, portfolio defense, and technical Q&A preparation for DS/ML positions.
Job search guidance, application tips, and advice on presenting your projects to employers.
Join the Data Science Diploma and transform from learning ML to shipping ML systems.
Limited seats available • Next cohort starting soon
Flexible Payment Plans Available
Scholarship Opportunities
Start Learning Immediately
Master ML from fundamentals to production