Targeted audience: New researchers, undergraduates, or those new to quantitative methods
Foundation of Python for Data Science Workshop
​Duration: 4 hours ​
Target Audience: Beginners in data science, students, or professionals with basic programming knowledge ​
​Learning Outcomes: ​
By the end of the workshop, participants will be able to: ​
- Understand core Python concepts for data analysis ​
- Manipulate and clean data using Pandas ​
- Perform basic numerical operations with NumPy ​
- Conduct preliminary data exploration and visualisation ​
​Course Outline: ​
- Introduction to Python for Data Science ​
- Introduction to NumPy ​
- Data Manipulation with Pandas ​
- Exploratory Data Analysis (EDA) ​
First Steps with SPSS: A Practical Workshop for Beginners
​Duration: 4 hours ​
Target Audience: Beginners with little to no experience in using SPSS data analysis ​
​Learning Outcomes: ​
By the end of the workshop, participants will be able to: ​
- Confidently navigate and manage data in SPSS ​
- Recode, compute, and transform variables effectively ​
- Conduct basic descriptive and inferential analyses ​
- Interpret and report SPSS results clearly and accurately ​
​
Course Outline: ​
- Introduction to SPSS and Its Interface ​
- Entering and Managing Data ​
- Descriptive Statistics Made Easy ​
- Basic Statistical Analysis in SPSS ​
- Reporting and Exporting Results​
Getting Started with Data Analysis in R ​
​Duration: 4 hours ​
Target Audience: Beginners in data analysis, students, or professionals with no prior experience in R ​
​
Learning Outcomes: ​
By the end of the workshop, participants will be able to: ​
- Set up and navigate the R and RStudio environment ​
- Use basic R syntax for data operations and analysis ​
- Import, explore, and manage data using R ​
- Build confidence in continuing self-learning and working
with R for data analysis​
​
Course Outline: ​
- Getting Started with R and RStudio ​
- R Basics ​
- Working with Data in R ​
Manipulation with dplyr: A Hands-On WorkshopÂ
​Duration: 6 hours ​
Target Audience: R users with basic knowledge who want toimprove their data manipulation skills using the dplyrpackage. ​
​Pre-requisite: Familiarity with R and data frames ​
​
Learning Outcomes:
By the end of the workshop, participants will be able to: ​
Use the pipe operator to build readable data pipelines ​
Apply key dplyr verbs for data manipulation ​
Group and summarize data effectively ​
Join multiple datasets using tidyverse conventions ​
Build efficient, clean code for reproducible data analysis ​
​
Course Outline: ​
Introduction to dplyr and the Pipe Operator ​
Core Verbs in dplyr ​
Aggregating and Summarizing Data ​
Evaluating Your dplyr Skills ​
Joining Datasets with dplyr ​