About
The aiQal Tech Data Science Bootcamp turns you into a data-driven problem solver capable of building real-world, end-to-end solutions. We start from first principles and guide you all the way to designing full analytics and machine-learning workflows used by top companies today. You’ll learn to think like a data scientist — not just run code. By the end, you’ll confidently collect, clean, analyze, visualize, and model data using modern tools and best industry practices. What You Will Learn Python for Data Science — NumPy, pandas, data wrangling, feature engineering Statistical Thinking — probability, inference, hypothesis testing Exploratory Data Analysis (EDA) — uncovering insights hidden in raw data Data Visualization — Matplotlib, Seaborn, storytelling with insights Machine Learning Essentials — regression, classification, clustering Metrics & Evaluation — A/B testing, model validation, performance checks SQL & Databases — querying, joining, aggregating, and building analytical pipelines Deployment Basics — turning notebooks into real, usable tools Real-World Projects You’ll Build You don’t just learn concepts — you build serious, portfolio-ready projects: 📊 Customer Churn Predictor — predict who will leave a subscription service using real data 🛍️ E-commerce Sales Insights Dashboard — analyze trends and create a business-ready dashboard 🧠 Credit Risk Classifier — score loan applicants using real ML workflows 🛰️ Airbnb Price Prediction Model — use regression to estimate listing prices ⚙️ A/B Testing Simulation Tool — build experiments and evaluate real marketing decisions 🗄️ SQL Analytics Engine — design analytical queries for a real database schema Why This Bootcamp Works Live instruction — Arabic & English, so language is never a barrier Step-by-step, beginner-friendly approach, but with deeply technical content Hands-on coding from day one Scholarships & flexible pricing to make world-class tech education accessible
Overview
- Understand the definitions and differences between AI and ML.
- Explore the various types of machine learning: supervised, unsupervised, and reinforcement learning.
- Discuss real-world applications of AI and ML in business.
- Learn about the data science workflow and its importance in ML projects.
- Get hands-on experience with data collection, preprocessing, and feature selection.