update README.md-s with student outcomes

lecture05-graphs-basics/README.md

@@ -32,6 +32,7 @@ Case studies used/related in/to this lecture:
 ## Learning outcomes
 After completing [`plotnine_intro.ipynb`](https://github.com/gabors-data-analysis/da-coding-python/blob/main/lecture05-graphs-basics/plotnine_intro.ipynb) students should be able to:
 
+  - create `ggplot` objects with different types of figures using `geoms` 
   - manipulating axis with `scale_*_continuous` and `scale_*_discrete`, where `*` stands for `y` or `x`
     - set limits
     - set break points
@@ -47,6 +48,17 @@ After completing [`plotnine_intro.ipynb`](https://github.com/gabors-data-analysi
 
 After completing [`matplotlib_intro.ipynb`](https://github.com/gabors-data-analysis/da-coding-python/blob/main/lecture05-graphs-basics/matplotlib_intro.ipynb) students should be able to:
 
+- the two key object in a `matplotlib` plot
+  - `Figure` 
+  - `Axes`
+- set
+  - y-axis limits
+  - legends
+  - log scale
+- using a second axis
+- spacing between the bars and horizontal grids
+- chart within a chart
+
 ## Datasets used
 * [Hotel Europe](https://gabors-data-analysis.com/datasets/#hotels-europe)
 

lecture06-conditionals/README.md

@@ -6,12 +6,17 @@ Deciding what to do on a case by case is widely used in decision making and also
 
 ## This lecture
 
-
-## Learning outcomes
+This lecture introduces students to conditional programming with `if-else` statements. It covers the essentials control flows as `for` and `while` loop and list comprehension.
 
 
+## Learning outcomes
+After successfully live-coding the material (see: [`conditionals_and_control_flows.ipynb`](https://github.com/gabors-data-analysis/da-coding-python/blob/main/lecture06-conditionals/conditionals_and_control_flows.ipynb)), students will have knowledge on
 
-## Datasets used
+- how a conditional statement works
+- what are the crucial elements of an `if-else` statement
+- what is a `for` loop
+- what is a `while` loop
+- wow to create a list comprehension
 
 
 ## Lecture Time

lecture08-functions/README.md

@@ -18,7 +18,11 @@ This lecture introduces functions, how they are structured and how to write them
 ## Learning outcomes
 After successfully live-coding the material (see: [`functions.ipynb`](https://github.com/gabors-data-analysis/da-coding-python/blob/main/lecture08-functions/functions.ipynb)), students will know on
 
-
+- how to create user definded functions
+- what is the structure of a function
+- the use of `docstring`s to document functions
+- the use of the `help` function to retreive function descriptions
+- the use of `lambda` function
 
 ## Lecture Time