Learn to integrate AI tools into your Python workflows for practical data tasks. This course covers using ChatGPT effectively for development work, then shows you how to use OpenAI's API to automate data cleaning and categorization tasks directly from Python.

• Using ChatGPT for Python development assistance
• Setting up OpenAI API authentication
• Automating data cleaning with AI
• Building text categorization systems

Ce cours intermédiaire vise à approfondir vos compétences analytiques en R, en utilisant des données réelles sur des maladies comme le VIH, la tuberculose et le paludisme. Vous apprendrez des méthodes sophistiquées de traitement et d'analyse des données. Le programme comprend :

• Utilisation des dates, des facteurs et des chaînes de caractères.
• Techniques de nettoyage des données.
• Mise en œuvre de fonctions et conditionnelles.
• Jointure de sources de données.
• Utilisation de boucles pour un traitement systématique des données.

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Test description

Start your Python journey with essential programming concepts and data visualization techniques. You'll learn the fundamentals of Python syntax, work with different data structures, and create publication-quality visualizations using Plotly Express - all through hands-on exercises using real-world datasets.

• Google Colab basics and keyboard shortcuts
• Python fundamentals: variables, arithmetic, comments
• Functions, methods, and working with libraries
• Data structures: lists, dictionaries, pandas Series/DataFrames
• Basic loops and f-strings
• Creating visualizations with Plotly Express (histograms, scatter plots, box plots)

Master pandas, Python's powerful data manipulation library. Through practical examples and exercises, you'll learn how to efficiently process, clean, and transform data. This course covers:

• Loading data from various sources (CSV, Excel)
• Column selection and filtering techniques
• Creating and transforming columns
• Value replacement and cleaning
• Grouping and aggregation operations
• Complex queries and conditional operations

This course will teach you how to harness the power of visualization in R to explore data and to present your findings to others. In addition to learning the mechanics of learning to write code for visualizations, you will also learn how to use the art of visualization to tell a story with your data. We will be focusing on learning how to use ggplot2, one of the the most popular R packages, to produce a variety of high quality visualizations.

Topics include:

• Building a plot in layers with ggplot2
• Implementing the Grammar of Graphics in R
• Visualization strategies for univariate, bivariate, and multivariate data
• Histograms, boxplots, and kernel density plots for visualizing the distribution of continuous variables
• Simple, stacked, and grouped barplots for visualizing the distribution of discrete variables
• Scatterplots and linegraphs for visualizing the relationship between two continuous variables
• Time series plots to visualize epidemic trends
• Grouping and faceting plots to visualize relationships among three or more variables
• Customizing plots with labels, color palettes, and themes

During the beta release, new lessons will be made public periodically.

Dive into the world of data reporting with this comprehensive course, designed to harness the power of R for complex reporting needs. You'll explore a range of techniques and tools essential for depicting and interpreting data effectively. The curriculum includes:

• Building demographic pyramids to represent population structures
• Creating data tables for comprehensive data overview
• Developing choropleth maps to visualize patterns across regions
• Automating the generation of visualizations
• Analyzing time series trends
• Parametrizing reports for tailored data presentation

• Selecting specific columns or rows of a data frame based on names, positions, or logical conditions
• Filtering out unwanted observations or variables
• Creating new variables through mathematical operations or recombination of existing variables
• Handling missing data by dropping, replacing, or imputing values
• Grouping rows by a variable and calculating summary statistics for each group
• Reshaping data between wide and long formats

Ce cours s'appuie sur les bases établies dans le cours R foundations en approfondissant le travail avec les data frames. Vous acquerrez une pratique pratique de la sélection, du filtrage, de l'arrangement, du renommage et de la modification des colonnes afin de préparer les données brutes pour l'analyse statistique et la visualisation. Les sujets abordés sont les suivants:

• Sélection de colonnes ou de lignes spécifiques d'un data frame sur la base de noms, de positions ou de conditions logiques
• Filtrage des observations ou des variables indésirables
• Création de nouvelles variables par le biais d'opérations mathématiques ou par la recombinaison de variables existantes
• Traitement des données manquantes par l'élimination, le remplacement ou l'imputation de valeurs
• Regroupement des lignes en fonction d'une variable et calcul de statistiques sommaires pour chaque groupe
• Remodelage des données entre les formats large et long

 

Ce cours vous apprendra à exploiter la puissance de la visualisation en R pour explorer des données et présenter vos résultats à d'autres personnes. En plus d'apprendre les mécanismes de l'écriture de code pour les visualisations, vous apprendrez également à utiliser l'art de la visualisation pour raconter une histoire avec vos données. Nous nous concentrerons sur l'apprentissage de l'utilisation de ggplot2, l'un des packages R les plus populaires, pour produire une variété de visualisations de haute qualité.

Les thèmes abordés sont les suivants:

• • Construction d'un graphique en couches avec ggplot2
  • Stratégies de visualisation pour les données univariées, bivariées et multivariées
  • Tracés de séries chronologiques pour visualiser les tendances épidémiques
  • Personnaliser les graphiques à l'aide d'étiquettes, de palettes de couleurs et de thèmes

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Ce cours d'introduction offre un aperçu complet du langage de programmation R. À travers une pratique pratique, vous apprendrez comment configurer votre environnement de programmation R, importer et explorer des données, effectuer des analyses de données de base et de la visualisation de données, et créer des rapports pour communiquer vos résultats. Les sujets incluent :

• Installer R et l'IDE RStudio
• Importer des données de divers formats dans R
• Les fondamentaux de la programmation en R
• Organiser votre projet et espace de travail R
• Explorer des données à travers des statistiques résumées et de la visualisation de données
• Créer des rapports reproductibles en R Markdown pour communiquer votre analyse

Bienvenue au Bootcamp "Formation de Base sur R | Introduction à l'analyse des données de santé" ! Dans ce programme intensif de 3 mois, vous maîtriserez les fondamentaux de R, travaillerez sur des projets pratiques en utilisant de véritables ensembles de données de santé, et collaborerez avec des professionnels de la santé du monde entier. Ce cours est conçu pour vous doter des compétences suivantes :

• Installer et utiliser R & RStudio
• Naviguer dans les scripts R & Rmarkdown et les projets RStudio
• Importer et inspecter des jeux de données dans R
• Manipuler des données en filtrant, résumant, transformant et joignant
• Visualiser des données avec le package ggplot2
• Publier divers formats de sortie, y compris des rapports
• Utiliser ChatGPT comme assistant de codage

À la fin du cours, vous serez prêt à gérer un large éventail de tâches d'analyse de données de base et intermédiaires en utilisant R, applicables à vos recherches académiques, rôles professionnels ou intérêts personnels. Ce bootcamp est votre tremplin vers la maîtrise des données.

Bienvenue au Bootcamp "Formation de Base sur R | Introduction à l'analyse des données de santé" ! Dans ce programme intensif de 3 mois, vous maîtriserez les fondamentaux de R, travaillerez sur des projets pratiques en utilisant de véritables ensembles de données de santé, et collaborerez avec des professionnels de la santé du monde entier. Ce cours est conçu pour vous doter des compétences suivantes :

• Installer et utiliser R & RStudio
• Naviguer dans les scripts R & Rmarkdown et les projets RStudio
• Importer et inspecter des jeux de données dans R
• Manipuler des données en filtrant, résumant, transformant et joignant
• Visualiser des données avec le package ggplot2
• Publier divers formats de sortie, y compris des rapports
• Utiliser ChatGPT comme assistant de codage

À la fin du cours, vous serez prêt à gérer un large éventail de tâches d'analyse de données de base et intermédiaires en utilisant R, applicables à vos recherches académiques, rôles professionnels ou intérêts personnels. Ce bootcamp est votre tremplin vers la maîtrise des données.

Bienvenue au Bootcamp "Formation de Base sur R | Introduction à l'analyse des données de santé" ! Dans ce programme intensif de 3 mois, vous maîtriserez les fondamentaux de R, travaillerez sur des projets pratiques en utilisant de véritables ensembles de données de santé, et collaborerez avec des professionnels de la santé du monde entier. Ce cours est conçu pour vous doter des compétences suivantes :

• Installer et utiliser R & RStudio
• Naviguer dans les scripts R & Rmarkdown et les projets RStudio
• Importer et inspecter des jeux de données dans R
• Manipuler des données en filtrant, résumant, transformant et joignant
• Visualiser des données avec le package ggplot2
• Publier divers formats de sortie, y compris des rapports
• Utiliser ChatGPT comme assistant de codage

À la fin du cours, vous serez prêt à gérer un large éventail de tâches d'analyse de données de base et intermédiaires en utilisant R, applicables à vos recherches académiques, rôles professionnels ou intérêts personnels. Ce bootcamp est votre tremplin vers la maîtrise des données.

   

Bienvenue au cours Fondamentaux des logiciels statistiques et de l'analyse statistique (FoSSA)

Le cours comprend 7 modules :

• A1: Introduction aux statistiques à l'aide de R & SPSS
• A2: Calculs de puissance et de taille d'échantillon
• B1: Régression linéaire
• B2: Comparaisons multiples et mesures répétées
• B3: Méthodes non paramétriques
• C1: Données binaires et régression logistique
• C2: Données de survie

Chaque module comprend de courtes vidéos pour présenter et expliquer les concepts et les tests statistiques, ainsi que des exercices pratiques à réaliser. Les exercices sont proposés dans deux logiciels statistiques différents : Stata et R. Vous pouvez suivre le cours en utilisant l'un ou l'autre de ces deux logiciels. Si vous ne savez pas encore lequel choisir, vous pouvez parcourir les introductions à chacun d'eux dans le module A1 avant de prendre votre décision.

Questionnaire de fin de module

À la fin de chaque module, vous trouverez un court questionnaire à choix multiples. Si vous souhaitez recevoir un certificat pour ce cours, vous devez consulter toutes les pages et compléter l’ensemble des questionnaires avec une note moyenne (moyenne arithmétique) de 50 % ou plus. L’équipe administrative de FoSSA vérifiera chaque mois les résultats des questionnaires et enverra les certificats à toute nouvelle personne ayant terminé le cours.

Progression dans le cours

Vous devriez étudier l’ensemble du module A1 avant de passer aux autres modules afin de vous assurer de bien maîtriser les concepts clés. Avant de commencer le premier module, consulter la section Informations sur le cours. Vous y trouverez les jeux de données nécessaires pour les exercices pratiques ainsi que d’autres informations utiles pour vous accompagner dans vos études.

Welcome to Fundamentals of Statistical Software and Analysis, or FoSSA for short! The course comprises of 7 modules:

• A1: Introduction to Statistics using R, Stata & SPSS
• A2: Power and Sample Size Calculations
• B1: Linear Regression
• B2: Multiple Comparisons and Repeated Measures
• B3: Non-Parametric Measures
• C1: Binary Data and Logistic Regression
• C2: Survival Data

Each module contains short videos to introduce and explain the statistical concepts and tests, along with practical exercises to work through.

The practicals are provided in three different statistical software packages, Stata, R, and SPSS. You can follow through the course using any one of these three packages. If you are unsure which package you would like to use, you may want to work through the introductions to each of them in Module A1 before making your decision.

End of Module Quiz

If you wish to receive a certificate for the course you must mark all lessons complete, and pass all of the quizzes, with a mark of 80% or greater.

Proceeding through the Course

You should study all of Module A1 before moving on to the other modules to make sure you have a firm grasp of the key concepts.

Before you start on the first module, take a look at the Course Information section. Here you will find the data sets you will need for your practicals and other useful information to support you in your studies.

• Installing R and the RStudio IDE
• Importing data from various formats into R
• Fundamentals of R programming
• Organizing your R project and workspace
• Exploring data through summary statistics and data visualization
• Creating reproducible reports in R Markdown to communicate your analysis

This intermediate course aims to further your analytical skills in R. You will cover:

• Working with dates, factors and strings
• Techniques for thorough data cleaning
• Implementing conditional functions for advanced data examination
• Joining data sources for enriched analysis
• Employing loops for systematic data processing
• Advanced string manipulation

Whether you want to enhance your productivity, explore innovative research applications, or develop new solutions for your clients, this course gives you the practical skills to start using AI effectively today. In this hands-on 8-week program, you'll learn how to deploy the latest AI tools to accelerate your work and improve outcomes—without any coding required. This course will teach you how to:

• Create professional websites, documents, and presentations with AI assistance
• Perform literature reviews, analyze data and build dashboards using intuitive AI tools
• Design custom chatbots that can answer domain-specific questions
• Set up automated workflows that operate around the clock
• Utilize AI for creating and editing images, videos, and design materials
• Develop practical AI solutions that integrate into your daily projects

By the end of the program, you'll know how to harness AI to save time on routine tasks, produce higher quality work, and solve real-world challenges—whether in research, creative industries, or business.

Whether you want to enhance your productivity, explore innovative research applications, or develop new solutions for your clients, this course gives you the practical skills to start using AI effectively today. In this hands-on 8-week program, you'll learn how to deploy the latest AI tools to accelerate your work and improve outcomes—without any coding required. This course will teach you how to:

• Create professional websites, documents, and presentations with AI assistance
• Perform literature reviews, analyze data and build dashboards using intuitive AI tools
• Design custom chatbots that can answer domain-specific questions
• Set up automated workflows that operate around the clock
• Utilize AI for creating and editing images, videos, and design materials
• Develop practical AI solutions that integrate into your daily projects

By the end of the program, you'll know how to harness AI to save time on routine tasks, produce higher quality work, and solve real-world challenges—whether in research, creative industries, or business.

Whether you want to enhance your productivity, explore innovative research applications, or develop new solutions for your clients, this course gives you the practical skills to start using AI effectively today. In this hands-on 8-week program, you'll learn how to deploy the latest AI tools to accelerate your work and improve outcomes—without any coding required. This course will teach you how to:

• Create professional websites, documents, and presentations with AI assistance
• Perform literature reviews, analyze data and build dashboards using intuitive AI tools
• Design custom chatbots that can answer domain-specific questions
• Set up automated workflows that operate around the clock
• Utilize AI for creating and editing images, videos, and design materials
• Develop practical AI solutions that integrate into your daily projects

By the end of the program, you'll know how to harness AI to save time on routine tasks, produce higher quality work, and solve real-world challenges—whether in research, creative industries, or business.

Whether you want to enhance your productivity, explore innovative research applications, or develop new solutions for your clients, this course gives you the practical skills to start using AI effectively today. In this hands-on 8-week program, you'll learn how to deploy the latest AI tools to accelerate your work and improve outcomes—without any coding required. This course will teach you how to:

• Create professional websites, documents, and presentations with AI assistance
• Perform literature reviews, analyze data and build dashboards using intuitive AI tools
• Design custom chatbots that can answer domain-specific questions
• Set up automated workflows that operate around the clock
• Utilize AI for creating and editing images, videos, and design materials
• Develop practical AI solutions that integrate into your daily projects

By the end of the program, you'll know how to harness AI to save time on routine tasks, produce higher quality work, and solve real-world challenges—whether in research, creative industries, or business.

Whether you want to enhance your productivity, explore innovative research applications, or develop new solutions for your clients, this course gives you the practical skills to start using AI effectively today. In this hands-on 8-week program, you'll learn how to deploy the latest AI tools to accelerate your work and improve outcomes—without any coding required. This course will teach you how to:

• Create professional websites, documents, and presentations with AI assistance
• Perform literature reviews, analyze data and build dashboards using intuitive AI tools
• Design custom chatbots that can answer domain-specific questions
• Set up automated workflows that operate around the clock
• Utilize AI for creating and editing images, videos, and design materials
• Develop practical AI solutions that integrate into your daily projects

By the end of the program, you'll know how to harness AI to save time on routine tasks, produce higher quality work, and solve real-world challenges—whether in research, creative industries, or business.

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Learn version control fundamentals and collaborate on code projects with confidence. This beginner-friendly course introduces you to Git and GitHub using GitHub Desktop's intuitive interface, making version control accessible without complex command-line operations. Perfect for Python developers, data scientists, and anyone working on coding projects.

• Creating and managing repositories
• Tracking changes and committing code
• Collaborating with team members on GitHub
• Publishing and sharing your projects online

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The Harmonized Health Facility Assessment (HHFA) is a comprehensive health facility survey assessing the availability of health facility services and the capacities of facilities to deliver quality services. This course introduces the HHFA purpose, content, and methods, as well as the WHO resources available to support countries in conducting high quality HHFAs.

The HHFA analysis platform allows country teams to make use of their survey data, by generating a full set of analytical outputs to populate a country report. This course introduces the analysis platform, showing how to use the platform to calculate indicators, create tables, charts and maps, and customize these outputs to suit country needs.

Self-paced 

Language: English

The usefulness of a Harmonized Health Facility Assessment (HHFA) depends on the quality of the data collected. One of the best ways to ensure quality data is through proper training of data collectors. This course equips qualified health professionals at country level with the core knowledge to facilitate training sessions for collection of HHFA data from health facilities.

Self-paced

Language: English

An understanding of the HHFA indicators and questions is needed both for adapting the HHFA to the country context, and for interpreting the survey findings. This course introduces the HHFA indicators, explains the questionnaire structure and content, and equips the HHFA technical team with the knowledge needed to adapt the questionnaire

Self-paced

Language: English

Monitoring health inequalities is essential to achieving health equity: it is essential to know who is being left behind in order to be able to tackle existing inequalities and ensure equitable access to quality health services for all. Health inequality monitoring should be an integral part of a country’s health information system. The Health Equity Assessment Toolkit (HEAT and HEAT Plus) is a software application that facilitates the assessment of health inequalities. This course covers how this software application can support the analysis and use of disaggregated data as an input for equity-oriented policy-making. | Self-paced | Language: English | Not disease specific

Learn about the foundations of health inequality monitoring and get to know a range of World Health Organization (WHO) tools and resources for measuring and reporting health inequality. This is vital to achieve health equity – a situation where everyone, everywhere is able to access the health services they need and attain their full potential of health and well-being.

• Identify the five steps of health inequality monitoring
• Describe how to determine the scope of health inequality monitoring
• Describe how to obtain data for health inequality monitoring
• Describe basic analytical methods to analyse inequality data
• Select good practices in reporting the state of health inequality
• Describe considerations for knowledge translation to inform equitable health programmes and policies

👩‍💻Is this course for you?

The course is suitable for anyone interested in monitoring health inequality. This encompasses a broad audience of individuals working at national and subnational levels in countries, including technical staff (for example in ministries of health or statistical offices), monitoring and evaluation officers, health programme managers and policy-makers, as well as researchers, students and others. The course may equally be of interest to public health professionals working at regional and global levels (for example in global health organizations).

Learn about the foundations of health inequality monitoring and get to know a range of World Health Organization (WHO) tools and resources for measuring and reporting health inequality. This is vital to achieve health equity – a situation where everyone, everywhere is able to access the health services they need and attain their full potential of health and well-being.

• Identify the five steps of health inequality monitoring
• Describe how to determine the scope of health inequality monitoring
• Describe how to obtain data for health inequality monitoring
• Describe basic analytical methods to analyse inequality data
• Select good practices in reporting the state of health inequality
• Describe considerations for knowledge translation to inform equitable health programmes and policies

👩‍💻Is this course for you?

The course is suitable for anyone interested in monitoring health inequality. This encompasses a broad audience of individuals working at national and subnational levels in countries, including technical staff (for example in ministries of health or statistical offices), monitoring and evaluation officers, health programme managers and policy-makers, as well as researchers, students and others. The course may equally be of interest to public health professionals working at regional and global levels (for example in global health organizations).

Learn about the foundations of health inequality monitoring and get to know a range of World Health Organization (WHO) tools and resources for measuring and reporting health inequality. This is vital to achieve health equity – a situation where everyone, everywhere is able to access the health services they need and attain their full potential of health and well-being.

• Identify the five steps of health inequality monitoring
• Describe how to determine the scope of health inequality monitoring
• Describe how to obtain data for health inequality monitoring
• Describe basic analytical methods to analyse inequality data
• Select good practices in reporting the state of health inequality
• Describe considerations for knowledge translation to inform equitable health programmes and policies

👩‍💻Is this course for you?

The course is suitable for anyone interested in monitoring health inequality. This encompasses a broad audience of individuals working at national and subnational levels in countries, including technical staff (for example in ministries of health or statistical offices), monitoring and evaluation officers, health programme managers and policy-makers, as well as researchers, students and others. The course may equally be of interest to public health professionals working at regional and global levels (for example in global health organizations).

Health inequalities exist in all populations. Where the required data are available, monitoring can be done to measure inequalities, make comparisons between populations, and track changes over time – all with the broader aims of equitably strengthening health systems and improving health and well-being for all. This course is a general introduction to health inequality monitoring and serves as an entry point for a series of courses on the foundations of health inequality monitoring. | Self-paced | Language: English | Not disease specific

Health inequality monitoring requires two streams of data: data about health and data about dimensions of inequality (such as socioeconomic, geographic or demographic characteristics). This course examines four common data sources for health inequality monitoring, highlighting their strengths and limitations, as well as opportunities to strengthen them for use in health inequality monitoring. It also guides learners through the processes of data source mapping and linking between data sources. | Self-paced | Language: English | Not disease specific

Disaggregated health data show health indicator estimates by population subgroup, describing, for example, health intervention coverage across subgroups with different education levels or economic status. In this course, learners will examine how disaggregated health data are integral to the process of health inequality monitoring, and gain skills in assessing and reporting disaggregated data. | Self-paced | Language: English | Not disease specific