fix mistake

Author: KaviHShah

materials/03_python_data_types_and_operators.md

@@ -357,3 +357,8 @@ For further reading you can investigate why sets are generally faster to find un
 - Comment your code for clarity
 
 :::
+
+<!--
+
+Notes to change - more very basic exercisees on primitive data types - exercise to add subtract etc....
+--->

materials/04_python_functions.md

@@ -307,3 +307,7 @@ Docstring comments. While this may not be super important here, when you start u
 - Python has many functions that can be used
 - you can write your own functions using the set syntax
 :::
+
+<!--
+Notes to change - exercise too difficult for participants - simplify - either remove all the code or...
+-->

materials/09_importing_libraries.md

@@ -51,7 +51,7 @@ To update a package to the latest version:
 
 **Cons of Using `pip`:**
 
-- **Dependency management**: Pip doesn't handle package dependencies as robustly as `other tools like `conda`. This can result in conflicts or missing dependencies.
+- **Dependency management**: Pip doesn't handle package dependencies as robustly as other tools like `conda`. This can result in conflicts or missing dependencies.
 By this we mean packages may not have the correct packages they require or wrong versions of packages.
 
 - **Python-only**: Pip is mainly designed for Python packages. If a package has non-Python dependencies, you may have to install them manually. Many packages used in python may actually use other languages under the hood, resulting in them requiring non-python tools to be installed.

materials/12_working_with_numpy.md

@@ -197,15 +197,13 @@ c. Find the determinant of a 3x3 subarray of your choice.
 
 6. 
 
-a. Load in the student scores from student_scores.csv. Understand the data
+a. Load in the student scores from expenses.csv. Understand the data
 
-b. Compute the average score for each student and store it in a 1D array called student_avg.
+b. Compute the average cost for each catagory and store it in a 1D array called avg.
 
-c. Compute the average score for each subject and store it in a 1D array called subject_avg.
+c. Find the category with the highest average score.
 
-d. Find the student(s) with the highest average score.
-
-e. Normalize the scores array so that each subject (column) has values between 0 and 1.
+e. Normalize the array so that each subject (column) has values between 0 and 1.
 
 f. Bonus: Create a boolean mask array indicating which scores are above the average for their subject.