Data Analytics & Data Science

5-Day Professional Training Course | DADS5001

KSA · GCC · Africa

Course Overview

This intensive 5-day training programme equips analysts, engineers, business professionals, and technology leaders with the statistical foundations, programming tools, machine learning techniques, and data storytelling competencies needed to extract, analyse, and communicate insight from data at the speed and scale that modern organisations demand. Data is the defining resource of the twenty-first century economy — but raw data without analytical competency is simply noise. Across Saudi Arabia's Vision 2030 digital economy transformation where the National Data Management Office is establishing data as a sovereign strategic asset, GCC organisations competing on analytical intelligence across financial services, energy, and retail, and African economies where mobile data proliferation, fintech innovation, and digital government initiatives are generating datasets of extraordinary richness and policy consequence — the professionals who can turn data into decisions are the most valuable people in any room. Aligned with industry-recognised frameworks from the Data Management Association (DAMA), the Data Science Council of America (DASCA), and leading platform certifications across Python, SQL, and Tableau, this programme delivers the comprehensive data analytics and data science competency that the intelligence economy demands.

Keywords: Data Analytics Training Saudi Arabia | Data Science Course GCC | Business Intelligence Africa | Python Analytics Training Riyadh · Dubai · Nairobi · Cairo

Course Information

Target Audience

• Business analysts and reporting specialists transitioning into data science roles

• Data analysts seeking structured upskilling in machine learning and statistical methods

• Engineers and operations professionals applying analytics to industrial and infrastructure data

• Finance, commercial, and supply chain professionals making data-driven decisions

• IT and digital transformation leaders governing data platforms and analytics programmes

• Government data officers in KSA and GCC national data management initiatives

• Consultants and strategy professionals leveraging data for client insight and recommendation

• Entrepreneurs and startup founders across African growth markets building data-driven products

Learning Outcomes

Upon successful completion, participants will be able to:

• Apply statistical analysis and exploratory data techniques to extract meaningful patterns from complex datasets

• Write Python and SQL code to collect, clean, transform, and analyse structured and unstructured data

• Build and evaluate machine learning models for classification, regression, clustering, and forecasting applications

• Design and publish interactive data visualisations and dashboards that communicate insight to non-technical audiences

• Apply data ethics, privacy regulation, and governance frameworks relevant to KSA, GCC, and African data environments

• Develop and present a complete data analytics project from business problem through insight to organisational recommendation

Learning Methods

5-Day Programme Outline

Day 1 — Data Foundations, Statistical Thinking & Exploratory Analysis

1. The data analytics and data science landscape: roles, tools, and the distinction between analytics, data science, and machine learning

2. Data types and data structures: structured, semi-structured, and unstructured data — sources, formats, and the data ecosystems of GCC and African organisations

3. Statistical foundations: descriptive statistics, distributions, probability, correlation, and hypothesis testing — the mathematical thinking underpinning all analytical work

4. Exploratory data analysis (EDA): profiling datasets, identifying missing values, detecting outliers, and forming analytical hypotheses before modelling begins

5. Data quality and data governance: the DAMA framework, data quality dimensions, and data management obligations under Saudi Arabia's PDPL and African data protection legislation

6. Lab session: Participants conduct an exploratory data analysis on a real regional dataset using Python and Pandas — summarising distributions, visualising relationships, and identifying analytical opportunities

Day 2 — SQL, Python & Data Wrangling

1. SQL for data analysts: SELECT, JOIN, GROUP BY, subqueries, window functions, and the query patterns that answer 80% of real business data questions

2. Python for data science: the Anaconda ecosystem, Jupyter notebooks, NumPy, and Pandas — the technical foundation every data professional requires

3. Data collection and ingestion: APIs, web scraping, database connections, flat file processing, and the data pipeline patterns that feed analytical workflows

4. Data cleaning and transformation: handling missing data, encoding categorical variables, normalising and scaling, and the feature engineering decisions that determine model quality

5. Merging and reshaping data: combining datasets from multiple sources — the practical skill that separates analysts who can work with real organisational data from those who can only work with clean textbook datasets

6. Lab session: Participants write SQL queries against a relational database and build a Python data cleaning and transformation pipeline for a messy real-world dataset

Day 3 — Machine Learning: Supervised & Unsupervised Methods

1. Machine learning fundamentals: the bias-variance tradeoff, overfitting, underfitting, train-test splitting, and cross-validation — the conceptual framework that prevents analytical malpractice

2. Supervised learning — classification: logistic regression, decision trees, random forests, and gradient boosting — building models that predict categorical outcomes across fraud detection, customer churn, and equipment failure applications

3. Supervised learning — regression: linear regression, regularisation techniques, and ensemble methods for predicting continuous outcomes including demand forecasting and asset valuation

4. Model evaluation and selection: accuracy, precision, recall, F1, AUC-ROC, RMSE, and the evaluation metrics that match model performance measurement to business problem requirements

5. Unsupervised learning: K-means clustering, hierarchical clustering, and dimensionality reduction using PCA — segmentation and pattern discovery without labelled training data

6. Lab session: Participants build, train, and evaluate a classification model and a clustering analysis using scikit-learn on a regional industry dataset — interpreting outputs for a business audience

Day 4 — Time Series, NLP & Advanced Analytics

1. Time series analysis: trend decomposition, seasonality, autocorrelation, and the specific characteristics of temporal data that standard machine learning methods cannot handle correctly

2. Forecasting methods: ARIMA, exponential smoothing, and Prophet — building demand, financial, and operational forecasting models applicable across GCC energy and African agricultural markets

3. Introduction to natural language processing: text preprocessing, TF-IDF, sentiment analysis, and topic modelling — extracting insight from the unstructured text data that constitutes 80% of organisational information

4. Large language model APIs in data science: using OpenAI, Anthropic, and open-source LLM APIs to augment analytical workflows with generative AI capability

5. Advanced visualisation: Plotly interactive charts, geographic mapping with Folium, and network graph visualisation for relationship data — going beyond bar charts to communicate complex analytical findings

6. Lab session: Participants build a time series forecasting model and conduct a sentiment analysis on a regional text dataset — interpreting and communicating findings to a simulated executive audience

Day 5 — Data Storytelling, Ethics, Strategy & Capstone

1. Data storytelling principles: structuring analytical narratives, selecting the right visualisation for each insight, and designing presentations that drive decisions rather than merely report findings

2. Dashboard design and publication: building interactive Tableau and Power BI dashboards that enable non-technical stakeholders to explore data independently and confidently

3. Data ethics and responsible analytics: algorithmic bias, fairness in predictive models, privacy-preserving analytical techniques, and the ethical obligations of data professionals working with sensitive organisational and personal data

4. Building a data-driven organisation: data literacy strategy, analytics centre of excellence design, and the cultural and governance investments that convert analytical capability into sustained competitive advantage

5. Data science career development: the skills landscape, certification pathways, portfolio development, and the regional data science talent market across KSA, GCC, and Africa

6. Capstone: Participants present their complete end-to-end data analytics project — from business problem framing through data collection, cleaning, modelling, and visualisation to organisational insight and recommendation — for peer and facilitator review

Regional Relevance

Content is specifically contextualised for KSA, GCC, and African data environments — integrating Saudi Arabia's National Data Management Office framework and PDPL data protection obligations, the UAE's data economy strategy and smart government analytics initiatives, and the analytics applications transforming African agriculture, health, fintech, and infrastructure sectors. Case datasets and industry examples are drawn directly from the energy, financial services, government, and development sectors that define analytical demand across these markets.

Assessment & Certification

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