Home Course Data Analysis Course Using Stata About the Course Stata is arguably the best software for data management and statistical analysis. It is a fast, powerful, complete, and integrated statistical package. Stata provides everything for data analysis, from data management to basic analysis and advanced analysis. Stata also makes it easy to generate publication-quality, customisable, distinctly styled graphs, including descriptive graphs, regression fit graphs, and more. The vast family of users of Stata across many disciplines is another reason it is the software of choice. Stata is distributed in more than 150 countries and continues to satisfy the needs of professionals in both research and business. The course uses a fine blend of interactive discussions, group exercises for every session, and two project reports that simulate real-life scenarios. At the end of the course, you will be able to produce and interpret basic and intermediate descriptive and inferential statistics using Stata. As a result, you will be able to take raw data, clean it, summarise it, analyse it, and take appropriate action. You will also be able to appraise and interpret research publications. Course Objectives This course equips participants with four essential skills for conducting rigorous, reproducible data analysis using Stata:  Understand data and learn Stata: The ability to use Stata efficiently for basic and intermediate data analysis. The course will introduce participants to Stata and Stata files, including do-files (which allow for quality control and quick reproducibility of data management and analysis) and log-files (which store data management and analysis outputs).  Data management: Participants will learn to use Stata for data cleaning and management and be able to import other types of data files, e.g. Excel, into Stata.  Descriptive statistics and subgroup analysis: Participants will understand the theory and use of descriptive statistics. They will be able to test for normality and conduct appropriate descriptive statistics in Stata. They will also learn to conduct sub-group (stratified) analysis in Stata. Inferential statistics: They will also be introduced to inferential statistics, including such concepts as confidence intervals, p-values and bivariate inferential statistics (hypothesis testing). The theory behind the statistics will be explained, and they will be able to conduct analysis in Stata and interpret statistical outputs. Course Outcomes After the training, participants will be able to take raw data collected in their settings, clean,  summarise, and analyse them, and take appropriate action. They will be proficient in descriptive and inferential statistics. Consequently, they will be able to use Stata in their professional work to produce neat, reproducible analyses and graphics. In addition, they will improve their ability to critically review research papers. Course Content In-person (5 Days) Virtual (4 Weeks) Day 1: Introduction to Data and Stata Understand how research questions shape data structure and analysis decisions Navigate Stata’s interface and work efficiently using commands, do-files and log files Bring your own data into Stata and export results in formats your workflow requires Day 2: Data Management using Stata Combine datasets from multiple sources with confidence Apply systematic data cleaning practices that protect the integrity of your analysis Day 3: Descriptive Statistics and Subgroup Analysis Produce and interpret descriptive statistics and publication-ready graphs Analyse patterns across subgroups and apply findings in a hands-on mini project Day 4: Confidence Intervals and Chi-Squared Test Calculate and interpret confidence intervals for your estimates Select and apply the right statistical test for categorical data comparisons Introduction to AI-assisted coding Day 5: Parametric and Non-Parametric Tests Choose between parametric and non-parametric approaches based on your data Interpret measures of effect and take your first steps into multiple regression Week 1: Introduction to Data and Stata Understand how research questions shape data structure and analysis decisions Navigate Stata’s interface and work efficiently using commands, do-files and log files Bring your own data into Stata and export results in formats your workflow requires Week 2: Data Cleaning and Descriptive Analysis Clean and prepare your data systematically before and during analysis Produce descriptive statistics, subgroup summaries and publication-ready graphs Analyse patterns across subgroups and apply findings in a hands-on mini project Week 3: Confidence Intervals and Chi-Squared Test Calculate, interpret and communicate confidence intervals with precision Understand the landscape of statistical tests and apply the chi-squared test correctly Introduction to AI-assisted coding Week 4: Inferential Statistics Apply parametric and non-parametric tests appropriate to your research context Interpret measures of effect and build a foundation for multiple regression analysis Day 1: Introduction to Data and Stata Understand how research questions shape data structure and analysis decisions Navigate Stata’s interface and work efficiently using commands, do-files and log files Bring your own data into Stata and export results in formats your workflow requires Day 2: Data Management using Stata Combine datasets from multiple sources with confidence Apply systematic data cleaning practices that protect the integrity of your analysis Day 3: Descriptive Statistics and Subgroup Analysis Produce and interpret descriptive statistics and publication-ready graphs Analyse patterns across subgroups and apply findings in a hands-on mini project Day 4: Confidence Intervals and Chi-Squared Test Calculate and interpret confidence intervals for your estimates Select and apply the right statistical test for categorical data comparisons Introduction to AI-assisted coding Day 5: Parametric and Non-Parametric Tests Choose between parametric and non-parametric approaches based on your data Interpret measures of effect and take your first steps into multiple regression Understand how research questions shape data structure and analysis decisions Navigate Stata’s interface and work efficiently using commands, do-files and log files Bring your own data into Stata and export results in formats your workflow requires Combine datasets from multiple sources with confidence Apply systematic data cleaning practices that protect the integrity of your analysis Produce and interpret descriptive statistics and publication-ready graphs Analyse patterns across subgroups and apply findings in a hands-on mini project Calculate and interpret confidence intervals for your estimates Select and apply the right statistical test for categorical data comparisons Introduction to AI-assisted coding Choose between parametric and non-parametric approaches based on your data Interpret measures of effect and take your first

Home Course Data Analysis Course Using R About the Course R is a programming language and free software environment for statistical computing and beautiful data visualisation. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques. Being a full programming language, R is highly extensible. R is now widely regarded as the best software for statistical analysis and data science. It is a fast, powerful statistical package designed by statisticians for data analysts of all disciplines. With Base R and a library of packages, the analyst has everything for data management, analysis and data visualisation. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.  In this course, participants will experience the desired qualities and functionalities that make R widely preferred. R is absolutely free. It compiles and runs on a wide variety of UNIX platforms, Windows and macOS. Our facilitators are experienced, intentional, interactive and friendly. We invite you to join this course and take your data analysis and visualisation skills to the next level. Course Objectives Whether you are new to R or have dabbled without structure, this course takes you from the fundamentals of R programming through to confident, independent data analysis: R programming foundations: Participants will learn to navigate RStudio, understand R’s data structures, write and apply functions, access packages and organise clean, well-commented R scripts that support quality control and reproducibility. Data management in R: Participants will be able to import and export data between R and common formats, including Excel, CSV and Stata, and apply systematic data cleaning and management procedures in preparation for analysis. Descriptive statistics and subgroup analysis: Participants will understand the theory and application of descriptive statistics, conduct appropriate descriptive and stratified analyses in R, and produce publication-ready graphics using Base R and GGPLOT functions. Inferential statistics: Participants will be introduced to confidence intervals, p-values and hypothesis testing, and will be able to select and apply appropriate parametric and non-parametric tests, and they will understand the rationale for multiple regression models. Course Outcomes R is only as powerful as the analyst using it. After completing this course, participants will be able to take raw data from their own research settings, clean and structure it properly, and produce neat, reproducible analyses and publication-quality graphics entirely in R. They will be proficient in both descriptive and inferential statistics, confident in interpreting and communicating statistical outputs, and better equipped to critically evaluate the analyses they encounter in research papers and reports. Course Content In-Person (5 Days) Virtual (4 Weeks) Day 1: Introduction to Data and R Understand how R works and why it has become the tool of choice for researchers and data scientists Introduction to R programming.  Begin working with R’s core data structures, functions and packages Day 2: Data Management using R Write clean, organised R scripts that make your work reproducible and easy to revisit Import, export and manage data across formats including Excel, CSV and Stata Day 3: Descriptive Statistics and Subgroup Analysis Develop a solid conceptual understanding of descriptive statistics and apply it confidently in R Produce subgroup analyses and publication-ready graphics Day 4: Confidence Intervals and Chi-Squared Test Calculate and interpret confidence intervals and understand what they tell you about your data Select and apply the right statistical test for categorical data comparisons Day 5: Parametric and Non-Parametric Tests Choose between parametric and non-parametric approaches based on the nature of your data Interpret measures of effect, take your first steps into multiple regression, and consolidate learning through a final project Week 1: Introduction to Data and R Understand how R works and why it has become the tool of choice for researchers and data scientists Introduction to R programming Set up your R environment and begin working with R’s core data structures, functions and packages Week 2: Data Management using R Write clean, organised R scripts that make your work reproducible and easy to revisit Import, export and manage data across formats including Excel, CSV and Stata Week 3: Descriptive Statistics and Subgroup Analysis Develop a solid conceptual understanding of descriptive statistics and apply it confidently in R Produce subgroup analyses and publication-ready graphics from your own data Week 4: Inferential Statistics Calculate and interpret confidence intervals, and select and apply the right statistical test for your data Choose between parametric and non-parametric approaches, interpret measures of effect, and take your first steps into multiple regression Day 1: Introduction to Data and R Understand how R works and why it has become the tool of choice for researchers and data scientists Introduction to R programming.  Begin working with R’s core data structures, functions and packages Day 2: Data Management using R Write clean, organised R scripts that make your work reproducible and easy to revisit Import, export and manage data across formats including Excel, CSV and Stata Day 3: Descriptive Statistics and Subgroup Analysis Develop a solid conceptual understanding of descriptive statistics and apply it confidently in R Produce subgroup analyses and publication-ready graphics Day 4: Confidence Intervals and Chi-Squared Test Calculate and interpret confidence intervals and understand what they tell you about your data Select and apply the right statistical test for categorical data comparisons Day 5: Parametric and Non-Parametric Tests Choose between parametric and non-parametric approaches based on the nature of your data Interpret measures of effect, take your first steps into multiple regression, and consolidate learning through a final project Understand how R works and why it has become the tool of choice for researchers and data scientists Introduction to R programming.  Begin working with R’s core data structures, functions and packages Write clean, organised R scripts that make your work reproducible and easy to revisit Import, export and manage data across formats including Excel, CSV and Stata Develop a solid conceptual understanding of descriptive statistics and apply it confidently in R Produce subgroup analyses and publication-ready graphics Calculate and

Home Course Data Analysis and Visualisation Using Excel (DAVE) About the Course In the rapidly evolving landscape of business and research, the ability to analyse and visualise data is a critical skill for researchers and data analysts of all disciplines. Excel is the most widely used data tool in research and professional environments across Africa and beyond, yet most practitioners use only a fraction of its analytical capability. DAVE is designed for individuals who want to move past the basics and work with data more rigorously, efficiently, and insightfully. This course is designed to enable participants to maximise their use of Excel and to provide a comprehensive understanding of data analysis techniques and the art of creating impactful visualisations. The course builds directly on foundational Excel knowledge — basic formulas, sorting, filtering, and simple charting — and introduces the techniques that researchers, business analysts and data professionals rely on for real-world analysis. Participants move from manual, error-prone data handling to structured, formula-driven workflows that save time and improve the reliability of their outputs. Because most organisations already use Microsoft Excel as their primary data tool, this training enables participants to extract significantly more value from the software they and their institutions already have without requiring investment in new platforms or tools. Course Objectives The course equips participants with six core capabilities: Data cleaning and preparation: Participants will identify and resolve common data quality errors using structured cleaning techniques and functions, and apply logical functions including IF, IFS, AND, and OR to categorise and flag data programmatically. Lookup and reference functions: Participants will use VLOOKUP and XLOOKUP to enrich datasets by drawing information from multiple sources and understand when and how to apply each function appropriately. Descriptive statistics: Participants will learn the appropriate statistics to use for their data depending on the measurement and distribution of data. They will calculate and interpret measures of central tendency, spread, etc., directly within Excel, and summarise data subsets using SUMIFS, COUNTIFS, and AVERAGEIFS. PivotTables: Participants will build, customise, and interpret PivotTables to summarise and cross-tabulate large datasets and configure value display options to derive new metrics from aggregated data. Conditional aggregation: Participants will learn different approaches for subgroup analysis, including filter, subgroup menu, crosstabulation and slicers. Data visualisation: Participants will create publication-quality charts and apply data visualisation principles to communicate findings clearly and accurately. They will learn Excel updates for automating their charts when they receive new data.  Course Outcomes Upon completion of this training, participants will have significantly enhanced their data management and Excel capabilities. They will be equipped with the practical skills needed to manage, analyse and present research data in Excel at an intermediate to advanced level. This will enable them to work more independently and efficiently, generating accurate, professional-quality outputs that meet the standards expected in research, monitoring and evaluation, and academic environments. Course Content In-Person (3 Days) Virtual (3 Weeks) Day 1: Data Cleaning and Logical Functions Introduction to data and data formats in Excel Understanding functions and formulas Data management and cleaning Identify and resolve data quality issues using structured cleaning techniques and text functions Apply logical functions to categorise, flag, and transform data programmatically Day 2: Lookups, Statistics, and Conditional Aggregation Enrich datasets using VLOOKUP and XLOOKUP to draw from multiple sources Calculate descriptive statistics and summarise data subsets using menus and functions Learn creative ways to summarise data efficiently  Build and interpret PivotTables and add calculated fields to derive new metrics Subgroup analysis (conditional aggregation) Day 3: PivotTables, Visualisation Create publication-quality charts and implement data validation rules to protect data integrity Create Pivot charts and update with new data Create Excel dashboards Week 1: Data Cleaning and Logical Functions Introduction to data and data formats in Excel Understanding functions and formulas Data management and cleaning Identify and resolve data quality issues using structured cleaning techniques and text functions Apply logical functions to categorise, flag, and transform data programmatically Week 2: Lookups, Statistics, and Conditional Aggregation Enrich datasets using VLOOKUP and XLOOKUP to draw from multiple sources Calculate descriptive statistics and summarise data subsets using menus and functions Learn creative ways to summarise data efficiently  Build and interpret PivotTables and add calculated fields to derive new metrics Subgroup analysis (conditional aggregation) Week 3: PivotTables, Visualisation Create publication-quality charts and implement data validation rules to protect data integrity Create Pivot charts and update with new data Create Excel dashboards Day 1: Data Cleaning and Logical Functions Introduction to data and data formats in Excel Understanding functions and formulas Data management and cleaning Identify and resolve data quality issues using structured cleaning techniques and text functions Apply logical functions to categorise, flag, and transform data programmatically Day 2: Lookups, Statistics, and Conditional Aggregation Enrich datasets using VLOOKUP and XLOOKUP to draw from multiple sources Calculate descriptive statistics and summarise data subsets using menus and functions Learn creative ways to summarise data efficiently  Build and interpret PivotTables and add calculated fields to derive new metrics Subgroup analysis (conditional aggregation) Day 3: PivotTables, Visualisation Create publication-quality charts and implement data validation rules to protect data integrity Create Pivot charts and update with new data Create Excel dashboards Introduction to data and data formats in Excel Understanding functions and formulas Data management and cleaning Identify and resolve data quality issues using structured cleaning techniques and text functions Apply logical functions to categorise, flag, and transform data programmatically Enrich datasets using VLOOKUP and XLOOKUP to draw from multiple sources Calculate descriptive statistics and summarise data subsets using menus and functions Learn creative ways to summarise data efficiently  Build and interpret PivotTables and add calculated fields to derive new metrics Subgroup analysis (conditional aggregation) Create publication-quality charts and implement data validation rules to protect data integrity Create Pivot charts and update with new data Create Excel dashboards Week 1: Data Cleaning and Logical Functions Introduction to data and data formats in Excel Understanding functions and formulas Data management and cleaning Identify and resolve data quality issues using structured cleaning techniques and text functions

Home Course Advanced Course in Multiple Regression and Causal Inference Using Stata About the Course Multiple regression and causal inference provide researchers with the needed assurance that observed associations are valid. After using a bivariate test (such as chi-squared test, t-test, ANOVA, etc.) to demonstrate association between two variables of interest, the next critical question is whether those associations are valid, i.e., whether they can in fact be explained by other variables (confounding) or study methodology (bias). In other words, to strengthen causal inference, it is vital to eliminate confounding and bias. Multiple regression models remain the most widely known approach for controlling for confounding, identifying risk factors and estimating independent effects. They are also commonly used to build predictive models, and the principles underlying them have influenced many modern artificial intelligence methods. Therefore, all quantitative researchers should be able to understand, build and interpret multiple regression models. Yet, many researchers who are comfortable with bivariate tests have never built or fully interpreted a regression model, leaving a critical gap between the analyses they conduct and the conclusions they need to draw. This course closes that gap. This course is designed for quantitative researchers who are already proficient in descriptive statistics. Being comfortable conducting hypothesis tests, such as chi-squared tests, t-tests, and ANOVA, will be a plus. Using Stata as the analytical platform, participants will move from the bivariate tests to the regression models that extend and formalise them. By the end of the course, participants will be equipped to build, interpret and critically evaluate linear and logistic regression models, and to apply them with confidence in their own research. They will also be able to extend their knowledge and skills into other models in the generalised linear model family, including Poisson and binomial models. Course Objectives The course equips participants with four essential capabilities for building and interpreting regression models in Stata. Foundations of regression and causal inference: Participants will consolidate their understanding of inferential statistics, conduct bivariate tests, and examine how these tests connect directly to regression models. They will develop a clear conceptual grasp of confounding and bias and understand why controlling for these is central to valid causal inference. Linear regression: Participants will fit and interpret simple and multiple linear regression models, estimate crude and adjusted effects, assess the relative contribution of predictor variables, and apply variable selection techniques. They will also test the assumptions of linear regression and conduct post-regression diagnostics to evaluate model fit. Logistic regression: Participants will fit and interpret simple and multiple logistic regression models for binary outcomes, work with odds and log odds, and understand the relationship between the chi-squared test and logistic regression. They will apply variable selection strategies and conduct post-regression diagnostics for logistic models. Predictive and causal modelling: Participants will write structured analysis plans, build models for both risk factor identification and outcome prediction, and critically appraise regression analyses in published research. Course Outcomes After completing this course, participants will be able to build and interpret linear and logistic regression models with confidence. They will understand the link between the bivariate tests they already know and the regression models that extend them. They will be able to adjust for confounders, estimate independent effects, and distinguish between causal and predictive modelling objectives — strengthening the rigour and credibility of their quantitative research. Course Content In-person (5 Days) Virtual (5 Weeks) Day 1: Inferential Statistics, Confounding and Simple Linear Regression Consolidate foundational knowledge of inferential statistics and understand the role of confounding, bias and interaction in strengthening causal inference Fit and interpret simple linear regression models and understand their conceptual connection to the t-test, ANOVA and correlation Day 2: Multiple Linear Regression and Model Diagnostics Extend linear regression to multiple predictors, estimate crude and adjusted effects, and assess the relative contribution of predictor variables Apply variable selection techniques, test model assumptions, and evaluate linear model fit through post-regression diagnostics Day 3: Simple and Multiple Logistic Regression Understand odds and log odds and fit simple logistic regression models for binary outcomes Extend to multiple logistic regression, adjust for confounders, and interpret adjusted odds ratios Day 4: Logistic Model Diagnostics and Applied Project Apply variable selection strategies  Conduct post-regression diagnostics for logistic models Day 5: Extensions to Generalised Linear Models Understand the GLM framework and when linear and logistic regression are insufficient for your data Fit and interpret Poisson regression models for count outcomes and binomial regression models for proportional outcomes Week 1: Inferential Statistics, Confounding and Simple Linear Regression Consolidate foundational knowledge of inferential statistics and understand the role of confounding, bias and interaction in strengthening causal inference Fit and interpret simple linear regression models and understand their conceptual connection to the t-test, ANOVA and correlation Week 2: Multiple Linear Regression and Model Diagnostics Extend linear regression to multiple predictors, estimate crude and adjusted effects, and assess the relative contribution of predictor variables Apply variable selection techniques, test model assumptions, and evaluate linear model fit through post-regression diagnostics Week 3: Simple and Multiple Logistic Regression Understand odds and log odds and fit simple logistic regression models for binary outcomes Extend to multiple logistic regression, adjust for confounders, and interpret adjusted odds ratios Week 4: Logistic Model Diagnostics and Applied Project Apply variable selection strategies  Conduct post-regression diagnostics for logistic models Week 5: Extensions to Generalised Linear Models Understand the GLM framework and when linear and logistic regression are insufficient for your data Fit and interpret Poisson regression models for count outcomes and binomial regression models for proportional outcomes Day 1: Inferential Statistics, Confounding and Simple Linear Regression Consolidate foundational knowledge of inferential statistics and understand the role of confounding, bias and interaction in strengthening causal inference Fit and interpret simple linear regression models and understand their conceptual connection to the t-test, ANOVA and correlation Day 2: Multiple Linear Regression and Model Diagnostics Extend linear regression to multiple predictors, estimate crude and adjusted effects, and assess the relative contribution of predictor variables Apply variable selection techniques, test model assumptions, and evaluate