Python for Data Science & AI — Comprehensive Bootcamp

5-Day Professional Training Course | PDSAI5001

KSA · GCC · Africa

Course Overview

This intensive 5-day bootcamp equips professionals, engineers, analysts, and aspiring data scientists with the Python programming competency, data science toolkit, and applied AI implementation skills needed to work confidently with data and build intelligent systems from the ground up. Python has become the undisputed lingua franca of data science and artificial intelligence — the language in which the world's most consequential AI systems are built, the analytical frameworks that power modern business intelligence are coded, and the machine learning models that are reshaping every industry are trained, evaluated, and deployed. Yet across Saudi Arabia, the GCC, and Africa, the gap between the demand for Python-literate data and AI professionals and the supply of genuinely competent practitioners remains one of the most consequential talent deficits in the regional technology economy. This bootcamp bridges that gap — taking participants from Python fundamentals through data manipulation, statistical analysis, machine learning, deep learning, and generative AI integration in a single, intensive, hands-on programme designed for professionals who learn by building rather than by watching. Across Saudi Arabia's National AI Strategy where SDAIA has identified Python competency as foundational to the kingdom's AI talent development agenda, GCC organisations competing to build indigenous data science capability rather than perpetually importing it from overseas talent markets, and Africa's rapidly expanding technology ecosystems where Python skills are the single most reliable gateway to high-value roles in data, AI, and software engineering — the professionals who complete this bootcamp with genuine Python data science competency are positioned to contribute immediately and meaningfully to the organisations and markets that need them most. Aligned with industry-recognised Python data science curricula, the Anaconda ecosystem, and the practical requirements of data science and AI roles across regional industries, this bootcamp delivers programming competency through relentless practice on real data.

Keywords: Python Data Science Bootcamp Saudi Arabia | Python AI Training GCC | Data Science Python Course Africa | Machine Learning Python Riyadh · Dubai · Nairobi · Cairo

Course Information

Target Audience

• Analysts and reporting professionals ready to move beyond Excel into programmatic data analysis

• Engineers and technical professionals applying Python to industrial, operational, and scientific data

• Business professionals in finance, procurement, and operations automating repetitive data tasks

• Aspiring data scientists building the Python foundation for a career transition into data and AI

• IT professionals expanding their technical repertoire into data science and machine learning

• Government data officers in KSA and GCC national data and AI initiatives requiring programming competency

• University graduates and early-career professionals across African technology markets seeking Python data science skills

• Any professional who works with data and recognises that Python literacy is now a baseline professional requirement

Learning Outcomes

Upon successful completion, participants will be able to:

• Write clean, efficient Python code for data collection, manipulation, analysis, and automation tasks

• Use NumPy, Pandas, and SciPy to perform statistical analysis and data transformation on real-world datasets

• Create compelling data visualisations using Matplotlib, Seaborn, and Plotly that communicate insight clearly to technical and non-technical audiences

• Build, evaluate, and interpret machine learning models using scikit-learn across classification, regression, and clustering applications

• Implement deep learning models using TensorFlow and Keras for image recognition and natural language processing tasks

• Integrate large language model APIs and generative AI tools into Python workflows for AI-augmented data science applications

Learning Methods

5-Day Programme Outline

Day 1 — Python Foundations & the Data Science Environment

1. Python setup and environment: Anaconda, Jupyter notebooks, VS Code, and Google Colab — configuring the professional Python data science environment and understanding when to use each tool

2. Python fundamentals: variables, data types, operators, string manipulation, and the basic building blocks that every Python programme requires — taught through data-relevant examples from the first line of code

3. Control flow and functions: conditional logic, loops, list comprehensions, and writing reusable functions — the programming patterns that transform scripts into maintainable, professional Python code

4. Python data structures: lists, tuples, dictionaries, and sets — selecting and manipulating the right structure for each data science task with confidence and efficiency

5. File handling and data ingestion: reading CSV, Excel, JSON, and API data into Python — the practical data loading skills that connect Python to the real-world data sources analysts encounter daily

6. Lab session: Participants complete a Day 1 mini-project — building a Python data ingestion and summary script that reads a regional industry dataset, performs basic calculations, and outputs a formatted summary report

Day 2 — NumPy, Pandas & Data Wrangling

1. NumPy foundations: arrays, vectorised operations, broadcasting, and linear algebra functions — the numerical computing layer that makes Python data science computationally efficient at scale

2. Pandas Series and DataFrames: creating, indexing, slicing, and manipulating tabular data — the core Pandas operations that data scientists use in every working session

3. Data cleaning in Pandas: handling missing values, removing duplicates, correcting data types, and the systematic data quality workflow that prepares raw data for reliable analysis

4. Data transformation and feature engineering: applying functions, grouping and aggregating, merging and joining datasets, reshaping with pivot tables and melt — the advanced Pandas techniques that handle the messy, multi-source data reality of regional organisations

5. Time series data in Pandas: datetime indexing, resampling, rolling windows, and the time series manipulation capabilities essential for energy, financial, and operational data analysis across GCC and African markets

6. Lab session: Participants complete a comprehensive data wrangling challenge — receiving a deliberately messy regional dataset and producing a clean, analysis-ready DataFrame through a systematic Pandas cleaning and transformation pipeline

Day 3 — Data Visualisation & Exploratory Data Analysis

1. Matplotlib fundamentals: figure architecture, axes, plot types, formatting, and the low-level visualisation control that underlies all Python plotting — building the foundation before moving to higher-level libraries

2. Seaborn for statistical visualisation: distribution plots, relationship plots, categorical plots, and the elegant statistical graphics that communicate analytical findings with minimum code and maximum clarity

3. Plotly for interactive visualisation: building interactive charts, geographic maps, and dashboards that allow non-technical stakeholders to explore data findings without requiring Python knowledge

4. Exploratory data analysis workflow: univariate analysis, bivariate analysis, correlation matrices, outlier investigation, and the systematic EDA process that transforms a raw dataset into a set of analytical hypotheses worth testing

5. Data storytelling with Python: structuring analytical narratives, selecting the right visualisation for each insight type, and designing Jupyter notebook presentations that communicate findings persuasively to decision-making audiences

6. Lab session: Participants conduct a complete exploratory data analysis on a real regional dataset — producing a portfolio-quality EDA notebook with statistical summaries, visualisations, and written analytical commentary suitable for a professional audience

Day 4 — Machine Learning with Scikit-Learn

1. Machine learning in Python: the scikit-learn API — fit, predict, transform, and the consistent interface that makes switching between algorithms a matter of changing two lines of code

2. Supervised learning — classification: implementing logistic regression, random forests, gradient boosting, and support vector machines — building classifiers for fraud detection, equipment failure prediction, and customer behaviour applications

3. Supervised learning — regression: linear regression, Ridge, Lasso, and ensemble regressors for demand forecasting, price prediction, and continuous outcome modelling across GCC energy and African agricultural datasets

4. Model evaluation and selection: train-test split, cross-validation, confusion matrices, ROC curves, and the evaluation discipline that prevents the overfitting that makes models fail in production

5. Unsupervised learning: K-means and hierarchical clustering, PCA dimensionality reduction, and the segmentation techniques that reveal hidden structure in customer, operational, and geographic data

6. Lab session: Participants build a complete machine learning pipeline using scikit-learn Pipeline objects — integrating preprocessing, feature engineering, model training, cross-validated evaluation, and hyperparameter tuning in a single reproducible workflow

Day 5 — Deep Learning, Generative AI Integration & Capstone

1. Deep learning with TensorFlow and Keras: building, compiling, training, and evaluating neural networks through Keras' sequential and functional APIs — moving from scikit-learn to deep learning without unnecessary complexity

2. Convolutional Neural Networks in Keras: building image classifiers for computer vision applications — quality inspection, satellite image analysis, and medical imaging relevant to GCC industrial and African health contexts

3. Transfer learning in practice: loading pre-trained models including MobileNet and ResNet, freezing layers, and fine-tuning on small domain-specific datasets — the technique that makes deep learning accessible without massive compute budgets

4. Generative AI API integration in Python: calling OpenAI, Anthropic Claude, and open-source LLM APIs from Python code — building AI-augmented data science workflows that combine traditional ML with large language model intelligence

5. Building a simple RAG pipeline in Python: document loading, text chunking, embedding generation, vector storage, and retrieval-augmented generation — the most practically valuable LLM engineering pattern implemented from scratch in Python

6. Capstone: Participants present their complete Python data science project — covering data ingestion, cleaning, EDA, machine learning or deep learning modelling, visualisation, and business insight — for peer and facilitator review

Regional Relevance

Content is grounded in the Python data science and AI talent landscape of KSA, GCC, and Africa — integrating Saudi Arabia's SDAIA Python competency development agenda, the UAE's AI talent strategy, and the Python skill development priorities of Africa's fastest-growing technology ecosystems in Nigeria, Kenya, Ghana, and Egypt. Real datasets and industry examples are drawn from energy, financial services, agriculture, and government domains directly relevant to participants' working environments across these markets.

Assessment & Certification

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