Update README.md

README.md

@@ -15,36 +15,38 @@ Anyway, to perform the assignment(s), you would need to:
 5) make your commits to the scripts in the `learn` folder. This will save your changes/answers to the assignments and trigger the tests to check if you do it correclty. Remember, you shold only do this on the `feedback` branch.
 7) check if the tests passed in either the `Pull requests` tab or the `Actions tab`. The goal is to have the test(s) be successful (green).
 
+If you are unsure about anything, post the questions on slack!
+
 ## lists
 
-The first learning plan is on `lists`.
+The first assignment is on `lists`.
 
 A list is a data structure in Python that is a mutable, or changeable, ordered sequence of elements. Each element or value that is inside of a list is called an item. Just as strings are defined as characters between quotes, lists are defined by having values between square brackets `[]`
 https://www.w3schools.com/python/python_lists.asp
 
-The objective for this learning plan is to learn the simplest way to interact with python which is storing items/elements in a `list`. There are many ways to manipulate a list so this is just an introduction. We use `lists` all the time for single-cell analysis, including for inserting new metadata, forming lists of items we want to plot etc. Imagine you have 80,000 cells and you want to colour them by treatment status and also by cell-type. How would you construct this information so that we can create the relevant treatment status + cell-type information for each cell e.g. "treated_B cell", "untreated_B cell"? You can achieve this creating a `list` that holds this information!
+The objective for this assignment is to learn the simplest way to interact with python which is storing items/elements in a `list`. There are many ways to manipulate a list so this is just an introduction. We use `lists` all the time for single-cell analysis, including for inserting new metadata, forming lists of items we want to plot etc. Imagine you have 80,000 cells and you want to colour them by treatment status and also by cell-type. How would you construct this information so that we can create the relevant treatment status + cell-type information for each cell e.g. "treated_B cell", "untreated_B cell"? You can achieve this creating a `list` that holds this information!
 
 There are 4 simple functions written in `learn/learn_list.py` but the tests are failing for each of them. Can you fix them up so that the tests succeed?
 
 ## dictionary
 
-The second learning plan is on `dictionary`.
+The second assignment is on `dictionary`.
 
 Dictionaries are used to store data values in key:value pairs. Dictionaries are defined by having values between square brackets `{}`
 https://www.w3schools.com/python/python_dictionaries.asp
 
-The objective of this part of the learning plan is to learn how to use dictionaries for matching/changing values to suit our needs, which is very relevant for single-cell analysis.
+The objective of this assignment is to learn how to use dictionaries for matching/changing values to suit our needs, which is very relevant for single-cell analysis.
 Imagine you have 10 clusters that you identified but you want to give each of them a biologically meaningful name. How would you go about changing the individual names? You can achieve this using `dictionaries`!
 
 There are 2 functions written in `learn/learn_dict.py` but the tests are failing for each of them. Can you fix them up so that the tests succeed?
 
 ## dataframes
 
-The third learning plan is on `dataframe`. We use the popular `pandas` package to interact with dataframes.
+The third assignment is on `dataframe`. We use the popular `pandas` package to interact with dataframes.
 
 A `pandas` `DataFrame` is a 2 dimensional data structure, like a 2 dimensional array, or a table with rows and columns.
 https://www.w3schools.com/python/pandas/pandas_dataframes.asp#:~:text=What%20is%20a%20DataFrame%3F,table%20with%20rows%20and%20columns.
 
-The objective of this part of the learning plan is to learn how to slice the `DataFrame` object so that we end up with the information that we want. This is very important for how we deal with single-cell data. Imagine that you have 10,000 cells but you only want to subset to just 2,000 of them as they are from the spleen and not any other organ. How would you go about doing this? You can achieve this by slicing the `DataFrame` to only contain the relevant cells!
+The objective of this assignment is to learn how to slice the `DataFrame` object so that we end up with the information that we want. This is very important for how we deal with single-cell data. Imagine that you have 10,000 cells but you only want to subset to just 2,000 of them as they are from the spleen and not any other organ. How would you go about doing this? You can achieve this by slicing the `DataFrame` to only contain the relevant cells!
 
 There are 2 functions written in `learn/learn_pandas.py` but the tests are failing for each of them. Can you fix them up so that the tests succeed?