Fall 2026 workshop

Outline/Outline.md

@@ -1,6 +1,6 @@
 # Data Processing in Python (Part 2)
 
-**Approximate duration:** 4 hours
+**Approximate duration:** 2 hours
 
 # Prerequisites:
 
@@ -18,7 +18,7 @@
 
 ## Summary
 
-In this 4-hour workshop, students will learn basic data processing
+In this 2-hour workshop, students will learn basic data processing
 skills using Python. Attendees will learn how to import code from other
 modules and packages to take advantage of the existing Python ecosystem.
 After seeing how to access packages, we will explore popular data
@@ -39,7 +39,7 @@ new, unfamiliar packages and learn how to use them.
 
 ## Content
 
-1. **Module 1 -- Modules and Packages (40 minutes)**
+1. **Module 1 -- Modules and Packages (20 minutes)**
    1. Using Modules
       1. What is a module?
       2. Importing a module
@@ -52,7 +52,7 @@ new, unfamiliar packages and learn how to use them.
       5. (Optional) Other installation tips
       6. Importing packages
    3. **Exercises**
-2. **Module 2 -- Introduction to NumPy Arrays (50 minutes)**
+2. **Module 2 -- Introduction to NumPy Arrays (30 minutes)**
    1. Introducing NumPy
       1. What is an array?
       1. NumPy fundamentals
@@ -65,7 +65,7 @@ new, unfamiliar packages and learn how to use them.
       3. Combining arrays
       4. Exploring the documentation
    3. **Exercises**
-3. **Module 3 -- Visualising Data with Matplotlib (50 minutes)**
+3. **Module 3 -- Visualising Data with Matplotlib (30 minutes)**
    1. Creating Plots with Matplotlib
       1. Introduction to pyplot
       2. Creating simple plots, bar plots, histograms, ...
@@ -75,7 +75,7 @@ new, unfamiliar packages and learn how to use them.
       6. Generating subplots
    2. Exploring the Matplotlib Documentation
    3. **Exercises**
-4. **Module 4 -- Intro to Tabular Data with Pandas (40 minutes)**
+4. **Module 4 -- Intro to Tabular Data with Pandas (30 minutes)**
    1. Fundamentals of pandas
       1. Motivation: When arrays aren't enough...
       2. Introducing the Series and DataFrame

README.md

@@ -7,7 +7,7 @@
 
 ## Overview
 
-In this 4-hour workshop, students will learn basic data processing skills using Python. Attendees will learn how to import code from other modules and packages to take advantage of the existing Python ecosystem. After seeing how to access packages, we will explore popular data analysis packages. We will see how to use NumPy to perform operations on large data arrays and how to use Matplotlib to generate clear data visualisations. We will also scratch the surface on using pandas to store data in tables. Along the way, we will discuss how to approach new, unfamiliar packages and learn how to use them.
+In this 2-hour workshop, students will learn basic data processing skills using Python. Attendees will learn how to import code from other modules and packages to take advantage of the existing Python ecosystem. After seeing how to access packages, we will explore popular data analysis packages. We will see how to use NumPy to perform operations on large data arrays and how to use Matplotlib to generate clear data visualisations. We will also scratch the surface on using pandas to store data in tables. Along the way, we will discuss how to approach new, unfamiliar packages and learn how to use them.
 
 ## Learning Objectives
 
@@ -43,10 +43,10 @@ In your Python environment, you must have the following packages installed:
 
 ## Links to Colab
 
-If you don't want to install anything locally, you can open the workshop materials using Google Colab:
+If you don't want to install anything locally, you can open the workshop materials using Google Colab. For this 2-hour workshop, we will be using the "Compact student version", since the "Student version" is intended for a 4-hour workshop.
 
-* Student version (with blank fields): <https://colab.research.google.com/github/QLS-MiCM/DataProcessingInPython/blob/main/Exercises/scripts/DataProcessingPython.ipynb>
 * Compact student version (with blank fields and shorter explanations): <https://colab.research.google.com/github/QLS-MiCM/DataProcessingInPython/blob/main/Exercises/scripts/DataProcessingPythonCompact.ipynb>
+* Student version (with blank fields): <https://colab.research.google.com/github/QLS-MiCM/DataProcessingInPython/blob/main/Exercises/scripts/DataProcessingPython.ipynb>
 * Solution version (filled out): <https://colab.research.google.com/github/QLS-MiCM/DataProcessingInPython/blob/main/Exercises/solutions/DataProcessingPython.ipynb>
 
 > &#x26A0; **Warning:** Make sure that `using_colab = True` in the first code cell and run that cell to download all the data files required for this workshop.