Health Data Science, Python & Machine Learning Training Workshop

Course overview

The Health Data Science, Python & Machine Learning Training Workshop equips healthcare professionals, public health researchers, epidemiologists and data analysts with practical skills in using Python, statistical analysis and machine learning to solve healthcare and population health challenges. The training covers health data management, exploratory data analysis, statistical modelling, predictive analytics, disease surveillance, healthcare service demand forecasting and machine learning model evaluation. Participants learn to transform raw health data into meaningful insights, develop predictive models, interpret analytical results and communicate findings through visualisations and dashboards. The course also addresses responsible AI, health data privacy, algorithmic bias and ethical data use to support evidence-based healthcare planning, public health research and resource allocation.

The Health Data Science, Python & Machine Learning Training Workshop provides practical skills in Python programming, healthcare data analysis, statistical modelling and machine learning. Participants learn to clean health datasets, analyse epidemiological trends, develop predictive models, evaluate model performance and communicate findings through visualisations and Power BI dashboards. The course covers classification, regression, clustering, disease surveillance, healthcare demand forecasting and responsible AI applications. Designed for healthcare professionals, public health researchers, epidemiologists and health data analysts, the training emphasises practical projects, data privacy and evidence-based decision-making to improve health research, programme planning and healthcare resource management.

Course objectives

• Understand the principles of health data science and its applications in healthcare and public health.
• Apply data protection, ethical AI and responsible modelling principles in health data science projects.
• Create data visualisations and dashboards to communicate health analytics findings.
• Evaluate model performance, validate predictions and interpret analytical limitations.
• Develop predictive models for selected health research and healthcare planning applications.
• Apply machine learning techniques to classification, regression, clustering and forecasting problems.
• Conduct exploratory data analysis and statistical analysis of healthcare and epidemiological data.
• Import, clean, validate and transform health datasets using Python.

Who should attend

Course content

• Module 1: Introduction to Health Data Science and Machine Learning Fundamentals of health data science, applications of Python in healthcare, descriptive versus predictive analytics, machine learning concepts, healthcare and public health use cases, types
• Module 2: Health Data Sources, Research Design and Data Management Electronic health records, disease surveillance systems, clinical research datasets, demographic and health surveys, laboratory data, hospital administrative records, health facility data,
• Module 3: Python Fundamentals for Health Data Analysis Python environment and notebooks, variables and data types, lists and dictionaries, conditional statements, loops, functions, reading and writing files, working with structured datasets, basic data pr
• Module 4: Health Data Cleaning and Preprocessing Importing CSV and Excel datasets, inspecting data structures, handling missing values, detecting duplicates, correcting inconsistent entries, identifying outliers, converting data types, encoding categorica
• Module 5: Exploratory Data Analysis and Statistical Methods Descriptive statistics, frequency distributions, measures of central tendency and variability, correlation analysis, cross-tabulation, identifying disease patterns, demographic comparisons, visua
• Module 6: Data Visualisation and Health Analytics Reporting Visualising categorical and continuous variables, bar charts and histograms, trend charts, scatter plots, correlation visualisation, disease distribution analysis, presenting healthcare utilisati
• Module 7: Supervised Machine Learning for Health Data Supervised learning principles, classification and regression, training and testing datasets, logistic regression, decision trees, random forests, support vector machines, predicting defined health-rel
• Module 8: Unsupervised Learning and Health Pattern Discovery Clustering fundamentals, k-means clustering, hierarchical clustering concepts, segmentation of population health indicators, grouping healthcare facilities by service patterns, identifying simil
• Module 9: Predictive Modelling and Healthcare Risk Analysis Defining prediction targets, selecting relevant predictors, building baseline models, developing risk prediction models, population-level risk stratification, healthcare utilisation prediction, i
• Module 10: Disease Surveillance and Time-Series Forecasting Analysing disease incidence trends, temporal health data, seasonality and trend patterns, forecasting healthcare service demand, disease surveillance indicators, outbreak signal detection, time-s
• Module 11: Model Evaluation, Validation and Interpretation Train-test splitting, cross-validation, confusion matrices, sensitivity and specificity, precision and recall, F1-score, ROC-AUC, regression error metrics, overfitting and underfitting, model cali
• Module 12: Healthcare Data Integration, Databases and Dashboards Introduction to SQL and relational databases, querying health datasets, combining data from multiple sources, data integration challenges, data consistency, preparing datasets for reporting,
• Module 13: Advanced Applications of AI in Healthcare AI-assisted health data analysis, natural language processing concepts for health research, extracting insights from unstructured health information, generative AI for research documentation and reporti
• Module 14: Health Data Ethics, Privacy and Responsible AI Health data confidentiality, de-identification and pseudonymisation, informed consent and lawful data use, access control, secure data handling, algorithmic bias, representative datasets, fairness
• Module 15: Capstone Project in Health Data Science Defining a healthcare or public health problem, selecting an appropriate dataset, data cleaning and preprocessing, exploratory data analysis, selecting a machine learning approach, training and evaluating

General notes

• 5. Practical Exercises and Expected Outputs Health dataset import exercise — structured analysis-ready dataset, data cleaning exercise — cleaned dataset and quality report, exploratory analysis exercise — statistical summary and health trend visualisations, Python programming exercise — reusable data analysis script, classification exercise — trained predictive model and evaluation report, regression exercise — outcome prediction model and error assessment, clustering exercise — population or facility segments with interpretation, disease surveillance exercise — trend analysis and early-warning indicators, forecasting exercise — healthcare demand projections and uncertainty assessment, model evaluation exercise — comparison of model performance metrics, database exercise — SQL queries and integrated dataset, dashboard exercise — interactive health analytics dashboard, AI-assisted reporting exercise — reviewed analytical summary, capstone project — documented health data science workflow and final predictive analytics report.

Frequently asked questions

Schedule & fees