Course Details

M&E with R and Python for Data Analysis Training Course

INTRODUCTION

This essential training course provides comprehensive knowledge and practical skills for leveraging the powerful programming languages R and Python for robust Monitoring & Evaluation (M&E) data analysis. Recognizing that advanced analytical capabilities are crucial for deriving deeper insights, automating processes, and enhancing the credibility of M&E findings, this program equips participants with a systematic approach to data manipulation, statistical analysis, and compelling visualization using industry-standard open-source tools. Participants will gain deep insights into data wrangling, descriptive and inferential statistics, reproducible reporting, and the fundamentals of machine learning for M&E, all crucial for maximizing data utilization and demonstrating project impact. This course is ideal for M&E specialists, data analysts, researchers, and anyone seeking to elevate their analytical skills beyond traditional spreadsheet software.

DURATION

10 DAYS

TARGET AUDIENCE

This course is specifically designed for M&E and data professionals who wish to significantly enhance their analytical capabilities using R and Python, including:

• Monitoring and Evaluation (M&E) Specialists.
• Data Analysts and Data Scientists in development/social sectors.
• Researchers and Academics working with quantitative data.
• Program Managers seeking deeper data insights.
• Advanced students in statistics, economics, or social sciences.

OBJECTIVES

Upon completion of this course, participants will be able to:

• Understand the fundamental concepts of R and Python for data analysis.
• Perform efficient data import, cleaning, and transformation using both languages.
• Conduct descriptive and inferential statistical analyses relevant to M&E.
• Create high-quality, reproducible data visualizations and reports.
• Apply basic machine learning concepts for M&E insights.

MODULES

Module 1: Introduction to R & Python for M&E

• Understand the role and advantages of R and Python in modern M&E.
• Explore the integrated development environments (e.g., RStudio for R, Jupyter/VS Code for Python).
• Discuss basic data types, variables, and operators in both languages.
• Examine the concept of packages/libraries for extending functionality.
• Learn about the open-source community and resources for R and Python.

Module 2: Data Import and Management (R & Python)

• Master techniques for importing various M&E data formats (e.g., CSV, Excel, SPSS, Stata, databases).
• Understand data structures in R (data frames, tibbles) and Python (Pandas DataFrames).
• Explore methods for inspecting and understanding M&E datasets.
• Discuss basic data manipulation (e.g., selecting columns, filtering rows, sorting) in both languages.
• Learn about exporting cleaned and transformed data.

Module 3: Data Cleaning and Transformation (R with Tidyverse & Python with Pandas)

• Understand advanced data cleaning techniques in R (e.g., dplyr, tidyr) and Python (e.g., pandas).
• Explore methods for handling missing values, outliers, and duplicates.
• Discuss strategies for reshaping data (e.g., wide to long format) for analysis.
• Examine techniques for creating new variables and aggregating data.
• Practice transforming messy M&E datasets into analysis-ready formats.

Module 4: Descriptive Statistics for M&E (R & Python)

• Calculate central tendency (mean, median, mode) and dispersion (std dev, variance, quartiles).
• Understand frequency distributions and cross-tabulations for categorical data.
• Explore methods for summarizing and grouping M&E data.
• Discuss techniques for exploring relationships between variables.
• Learn about generating descriptive summary tables and basic statistics.

Module 5: Data Visualization for M&E (R with ggplot2 & Python with Matplotlib/Seaborn)

• Master the principles of effective data visualization for M&E communication.
• Explore powerful visualization libraries: ggplot2 in R and Matplotlib/Seaborn in Python.
• Discuss creating various chart types for M&E data (e.g., bar, line, scatter, box plots, histograms).
• Examine techniques for customizing plot aesthetics and themes.
• Learn about saving high-quality visualizations for reports and presentations.

Module 6: Inferential Statistics for M&E (R & Python)

• Understand the concepts of hypothesis testing, p-values, and confidence intervals.
• Discuss performing common statistical tests relevant to M&E (e.g., t-tests, chi-squared tests, ANOVA).
• Explore correlation and simple linear regression for understanding relationships.
• Examine basic non-parametric tests when assumptions are not met.
• Learn about interpreting statistical outputs and drawing valid conclusions for M&E.

Module 7: Introduction to Reproducible Reporting (R Markdown & Jupyter Notebooks)

• Understand the importance of reproducible research and reporting in M&E.
• Explore R Markdown for creating dynamic reports that combine code, output, and narrative in R.
• Discuss Jupyter Notebooks for interactive coding, analysis, and documentation in Python.
• Examine techniques for generating various report formats (e.g., HTML, PDF, Word).
• Learn about version control basics (e.g., Git) for collaborative M&E projects.

Module 8: Advanced Topics and Applications in M&E (R & Python)

• Discuss the fundamentals of GIS integration for spatial M&E data (e.g., sf in R, Geopandas in Python).
• Explore web scraping basics for collecting online M&E data.
• Examine an introduction to machine learning concepts (e.g., classification, clustering) for M&E prediction.
• Learn about packaging and sharing custom functions or tools.
• Discuss continuous learning strategies and staying updated with R and Python ecosystems.

CERTIFICATION

• Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

• Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

• Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com