Research and Data Analysis

Data Munging and Wrangling with Pandas/dplyr in Research Training Course

Introduction

In today's data-driven world, researching sensitive topics—such as gender-based violence, mental health, minority rights, or health disparities—requires not only ethical handling but also advanced data management skills. Data Munging and Wrangling with Pandas/dplyr Training Course equips learners with the tools to extract, clean, transform, and structure complex and delicate datasets using Python’s Pandas and R’s dplyr libraries. The course emphasizes data integrity, confidentiality, bias minimization, and analytical accuracy, making it ideal for researchers working with critical, confidential, or socially impactful datasets.

By integrating real-world case studies, hands-on coding sessions, and contextual learning, this course empowers participants to handle noisy, incomplete, and ethically challenging data with precision. Whether you're a public health researcher, social scientist, journalist, or data analyst, this course provides an essential bridge between technical data processing and socially responsible research practices.

Programme Curriculum

Data Munging and Wrangling with Pandas/dplyr Training Course

Introduction

Course Objectives

Understand the principles of ethical research with sensitive data.
Gain proficiency in data wrangling using Pandas and dplyr.
Identify and mitigate biases and outliers in sensitive datasets.
Learn best practices for data anonymization and privacy protection.
Automate data cleaning workflows for reproducibility.
Master handling of missing, incomplete, or corrupted data.
Utilize descriptive statistics for exploratory data analysis.
Apply grouping, filtering, and summarization techniques for insights.
Visualize sensitive data using safe, aggregated plots.
Conduct data validation and integrity checks.
Learn cross-platform coding (Python & R) for wrangling sensitive datasets.
Develop custom functions and pipelines for repeatable wrangling.
Apply knowledge to real-life ethical data case studies.

Target Audience

Public Health Researchers
Social Science Academics
NGO & Policy Analysts
Journalists & Investigative Reporters
Clinical Researchers
Data Analysts working with surveys
Graduate Students & Scholars
Government & Development Agency Researchers

Course Duration: 5 days

Course Modules

Module 1: Introduction to Sensitive Data Research

Understanding ethical concerns in sensitive research
Examples of sensitive topics in real-world studies
Legal frameworks: GDPR, HIPAA, local laws
Risks and responsibilities of data handling
Introduction to anonymization techniques
Case Study: Handling data on domestic violence reports

Module 2: Introduction to Pandas and dplyr

Setting up environments in Python (Pandas) and R (dplyr)
DataFrames: creation, structure, and access
Importing and exporting datasets (CSV, Excel, JSON)
Syntax comparisons: Pandas vs. dplyr
Choosing tools based on data context
Case Study: Comparing health survey data in Pandas and dplyr

Module 3: Cleaning and Preparing Sensitive Data

Removing duplicates, fixing structural issues
Formatting dates, strings, and numeric values
Handling incorrect or inconsistent labels
Dealing with missing and null data
Creating cleaning scripts for reproducibility
Case Study: Preprocessing mental health survey data

Module 4: Exploratory Data Analysis for Sensitive Data

Descriptive statistics with context
Detecting trends while avoiding disclosure
Boxplots, histograms, and safe aggregations
Identifying and addressing outliers
Masking identifiable information
Case Study: Analyzing depression trends without violating privacy

Module 5: Advanced Data Wrangling Techniques

Merging and joining data from multiple sources
Filtering by conditions relevant to sensitive cases
Grouping and summarizing for subpopulations
Reshaping data with pivot/melt/gather/spread
Writing reusable wrangling functions
Case Study: Combining hospital and community data on HIV

Module 6: Data Privacy and Anonymization

Types of identifiers and quasi-identifiers
De-identification, pseudonymization, and k-anonymity
Balancing utility with privacy
Tools for data masking and encryption
Ensuring ethical publication practices
Case Study: Publishing anonymized gender-based violence data

Module 7: Workflow Automation and Reproducibility

Building wrangling pipelines in Pandas and dplyr
Using Jupyter Notebooks and RMarkdown
Version control and documentation
Parameterizing scripts for multiple datasets
Best practices in collaborative research coding
Case Study: Reproducing a data wrangling pipeline for refugee camp data

Module 8: Final Project and Integrated Case Study

Selecting and importing a sensitive dataset
Applying full wrangling and munging pipeline
Producing ethical EDA and summary report
Peer reviewing anonymization decisions
Presenting results using safe visualizations
Capstone Case Study: Wrangling data on school bullying and self-harm

Training Methodology

Hands-on exercises and live coding using Pandas and dplyr
Ethical scenario simulations with guided discussions
Group case study reviews to apply theoretical frameworks
Cross-platform labs (Python and R) for code diversity
Interactive Q&A and peer critique sessions

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Data munging Pandas/dplyr preprocessing

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000