Start your AI Journey with Our Data Scientist Course in Abu Dhabi

Module 1: Python for Data Science

4 hrs

• Variables, data types, operators, control flow (if/else, loops), functions, and lambda expressions
• Lists, tuples, dictionaries, sets — when to use each
• File I/O — reading CSV, JSON, and text files
• NumPy — arrays, vectorized operations, broadcasting, and linear algebra
• Pandas — Series, DataFrames, indexing, filtering, and aggregation
• Matplotlib & Seaborn — publication-quality charts and statistical plots
• Jupyter Notebooks & Google Colab — reproducible data science workflows
• List comprehensions, generators, decorators, virtual environments, and PEP-8 best practices

• Writing production-ready Python using NumPy, Pandas, and scikit-learn
• Setting up reproducible data science workflows in Jupyter and Colab
• Applying Pythonic best practices for clean, maintainable code
• Activity: Load a real-world dataset and produce a 5-chart exploratory report

Module 2: Data Wrangling, EDA & Visualization

5 hrs

• Ingesting data from CSV, Excel, SQL databases, REST APIs, and web scraping
• Connecting to cloud data sources (AWS S3, Google BigQuery basics)
• Identifying and handling missing values — imputation strategies
• Detecting and treating outliers — IQR, Z-score, and domain logic
• Fixing data types, encoding errors, inconsistent formatting, and deduplication
• Univariate analysis — distributions, skewness, and kurtosis
• Bivariate analysis — correlation matrices, scatter plots, and pair plots
• Feature relationships — heatmaps, pivot tables, and group-by analytics
• Interactive dashboards with Plotly; geospatial visualization with Folium and GeoPandas

• Cleaning and exploring any dataset to uncover patterns before modeling
• Applying EDA techniques and visualization to derive business insights
• Storytelling with data by choosing the right chart for the right insight
• Activity: Full EDA on a messy e-commerce or healthcare dataset — deliver a visual insights report

Module 3: Statistics & Probability for ML

3 hrs

• Mean, median, mode, variance, standard deviation, and percentiles
• Probability distributions — Normal, Binomial, Poisson, and Uniform
• Central Limit Theorem — why it matters for ML sampling
• Hypothesis testing — null hypothesis, p-values, and significance levels
• t-tests, chi-squared tests, and ANOVA — choosing the right test
• Confidence intervals and margin of error
• Bayes’ theorem and conditional probability — the foundation of Naive Bayes
• Maximum Likelihood Estimation (MLE) and MAP estimation
• Information theory — entropy, cross-entropy, and KL divergence

• Applying statistical reasoning to evaluate ML model performance and data distributions
• Selecting the appropriate hypothesis test for business scenarios
• Understanding Bayesian probability and information theory as ML foundations
• Activity: A/B test analysis on simulated marketing experiment data

Module 4: Supervised Learning — Regression & Classification

6 hrs

• Linear Regression — OLS, gradient descent, and assumptions; Polynomial and Ridge/Lasso/ElasticNet regularization
• Model evaluation — MAE, MSE, RMSE, R-squared, and adjusted R-squared
• Logistic Regression — decision boundary, odds ratio, and sigmoid function
• k-Nearest Neighbors (kNN), Decision Trees — Gini impurity, entropy, and pruning
• Support Vector Machines (SVM) — kernel trick and margin maximization
• Naive Bayes — text and spam classification
• Confusion matrix, precision, recall, F1-score, and ROC-AUC curves
• Cross-validation — k-fold, stratified, and leave-one-out; bias-variance tradeoff
• Hyperparameter tuning — GridSearchCV and RandomizedSearchCV
• Feature engineering — one-hot encoding, scaling, selection, and RFE

• Building regression and classification models with proper evaluation and validation
• Engineering features and selecting the best model for each business problem
• Tuning hyperparameters to optimize model performance
• Activity: Customer churn prediction model — end-to-end pipeline with scikit-learn

Module 5: Unsupervised Learning — Clustering & Reduction

4 hrs

• K-Means clustering — the elbow method, inertia, and silhouette score
• Hierarchical clustering — dendrograms and linkage criteria
• DBSCAN — density-based clustering and noise handling
• Gaussian Mixture Models (GMM) — soft assignment and EM algorithm
• Principal Component Analysis (PCA) — explained variance and scree plots
• t-SNE and UMAP — visualizing high-dimensional data in 2D/3D
• Linear Discriminant Analysis (LDA) — supervised dimensionality reduction
• Isolation Forest and One-Class SVM for outlier detection
• Autoencoders for anomaly detection (intro)

• Segmenting data and detecting anomalies without labeled data
• Reducing dimensionality to improve model performance and visualization
• Applying unsupervised techniques to real-world business scenarios
• Activity: Mall customer segmentation — identify actionable customer personas

Module 6: Advanced ML — Ensemble Methods & Boosting

4 hrs

• Bootstrap aggregation — how bagging reduces variance
• Random Forest — feature randomness, out-of-bag error, and feature importance
• AdaBoost — sequential learning and weak learner combination
• Gradient Boosting Machines (GBM) — loss functions and shrinkage
• XGBoost — regularization, tree pruning, and handling missing data
• LightGBM — leaf-wise growth, categorical features, and speed optimization
• CatBoost — ordered boosting and native categorical support
• Stacking (meta-learning) and voting classifiers — hard vs. soft voting
• SHAP values — SHapley Additive Explanations for global and local interpretability
• LIME — Local Interpretable Model-agnostic Explanations; Partial Dependence Plots

• Building and stacking ensemble models to achieve state-of-the-art performance
• Interpreting complex models using SHAP, LIME, and PDP for explainability
• Applying competition-winning strategies with XGBoost, LightGBM, and CatBoost
• Activity: Loan default prediction — build and stack multiple models, beat a baseline

Module 7: Deep Learning & Neural Networks

5 hrs

• Perceptron and MLP — forward pass and backpropagation; activation functions (ReLU, sigmoid, tanh, GELU)
• Loss functions and optimizers — SGD, Adam, AdaGrad, and learning rate scheduling
• Batch normalization, dropout, and weight regularization
• Convolutional Neural Networks (CNN) — convolution, pooling, and hierarchical feature learning
• Landmark CNN architectures — VGG, ResNet, EfficientNet; transfer learning and fine-tuning
• RNNs and the vanishing gradient problem; LSTMs and GRUs for sequential data
• Attention mechanism and self-attention — the core of modern LLMs
• BERT and GPT architecture overview — encoder vs. decoder
• Fine-tuning a pre-trained Hugging Face model for classification
• Introduction to Generative AI — diffusion models, LLMs, and prompt engineering

• Designing CNNs, LSTMs, and fine-tuning transformer models for real tasks
• Applying transfer learning to achieve high accuracy with limited data
• Building an image classifier and a text sentiment model for deployment
• Activity: Build an image classifier and a text sentiment model — deploy both to a web interface

Module 8: Natural Language Processing (NLP)

4 hrs

• Tokenization, stop word removal, stemming, and lemmatization
• Regular expressions for text cleaning; N-grams, bag-of-words, and TF-IDF vectorization
• Text classification — spam detection, topic labeling, intent recognition
• Named Entity Recognition (NER) and Part-of-Speech (POS) tagging
• Sentiment analysis with VADER, TextBlob, and custom models
• Word embeddings — Word2Vec, GloVe, and FastText; contextual embeddings — BERT, RoBERTa
• Zero-shot and few-shot classification using pre-trained LLMs
• Text summarization and question-answering with Hugging Face Pipelines
• RAG (Retrieval-Augmented Generation) — building a Q&A bot over your own data
• LangChain basics — chaining LLM calls and tools

• Analyzing text with classical and transformer-based NLP methods
• Building chatbots, summarizers, and semantic search engines
• Applying prompt engineering and RAG to production LLM systems
• Activity: Build a product review sentiment dashboard and a mini RAG chatbot

Module 9: MLOps — Model Deployment & Production

4 hrs

• scikit-learn Pipeline objects — preprocessing + model in one deployable unit
• MLflow — experiment tracking, model registry, and artifact management
• DVC (Data Version Control) — versioning datasets and model artifacts
• FastAPI — building a production-grade REST API for model inference
• Streamlit and Gradio — rapid ML web app development
• Model serialization — pickle, joblib, and ONNX format
• Docker — containerizing ML applications for reproducible deployment
• Deploying models to AWS (SageMaker), GCP (Vertex AI), and Azure ML
• Data drift and concept drift detection — EvidentlyAI overview
• CI/CD for ML — GitHub Actions pipeline for automated retraining

• Serving models via APIs, Docker containers, and cloud platforms
• Monitoring models for data drift and performance degradation in production
• Building CI/CD pipelines that automatically retrain and deploy models
• Activity: Package a churn model as a Docker container, deploy as a FastAPI, and add monitoring

Module 10: Capstone Project & Career Readiness

6 hrs

• Option A — Predictive Analytics: build an end-to-end business forecasting pipeline
• Option B — Computer Vision: train and deploy an image recognition API
• Option C — NLP/LLM: build a document Q&A chatbot with RAG
• Option D — Open Choice: propose your own real-world problem for instructor approval
• Deliverables: GitHub repo, Jupyter notebook, deployed Streamlit/FastAPI app, project presentation
• Building a standout data science portfolio on GitHub; crafting a data-focused CV and LinkedIn profile
• Cracking DS/ML interviews — coding tests, case studies, and system design
• Kaggle competitions — how to start, improve, and get noticed

• Completing an end-to-end capstone project showcasing all course skills
• Building a job-ready portfolio and presenting findings with business impact
• Approaching DS/ML interviews and freelancing opportunities with confidence
• Activity: Graduates receive a course completion certificate and optional portfolio review session