Applied Data Science Fundamentals

 This module focuses on exploring datasets to uncover trends, anomalies, and patterns before formal modeling. Learners will use descriptive statistics and visual tools for EDA.

This capstone-style module introduces participants to global open datasets (World Bank, UN, AfDB). Learners will source, clean, analyze, and visualize development data for applied practice.

 The capstone for Block 2 requires participants to apply all learned skills. In teams, learners will collect/obtain a dataset, clean and wrangle it, perform exploratory analysis, query it with SQL/Python/R, and present an interactive dashboard.

 Participants will gain mastery of Excel’s advanced analytical functions, pivot tables, and automation tools. This session emphasizes building clean datasets, summarizing with pivot tables, and designing dashboards for non-technical audiences.

This module introduces coding for data science. Learners will explore Python (Jupyter Notebook) and R (RStudio), covering basics of syntax, data manipulation, and visualization.

Learners will explore the basics of relational databases and structured query language (SQL) for querying and managing datasets.

 Learners will design and publish professional dashboards using Power BI or Tableau. Focus is on effective communication and storytelling with data.

 This practical module addresses how to prepare messy real-world datasets for analysis. Learners will work with missing values, duplicates, and inconsistencies, applying both spreadsheet and code-based methods.

This module equips learners with advanced survey and workflow design using XLSForms. It covers skip logic, validations, constraints, and cascading questions to create robust forms deployable on KoboToolbox, ODK, and SurveyCTO. Participants will practice designing and deploying surveys for both online and offline use, ensuring quality and reliability in data capture.