Change bullet points to numbers (DataTalksClub#243)

Change bullet points to numbers in questions 6 and 7, where an order is implied.
levankankadze · Sep 8, 2022 · 5ed2211 · 5ed2211
1 parent 98c853d
commit 5ed2211
Showing 1 changed file with 13 additions and 13 deletions.
diff --git a/course-zoomcamp/cohorts/2022/01-intro/homework.md b/course-zoomcamp/cohorts/2022/01-intro/homework.md
@@ -72,10 +72,10 @@ How many columns in the dataset have missing values?
 
 ### Question 6
 
-* Find the median value of "Engine Cylinders" column in the dataset.
-* Next, calculate the most frequent value of the same "Engine Cylinders".
-* Use the `fillna` method to fill the missing values in "Engine Cylinders" with the most frequent value from the previous step.
-* Now, calculate the median value of "Engine Cylinders" once again.
+1. Find the median value of "Engine Cylinders" column in the dataset.
+2. Next, calculate the most frequent value of the same "Engine Cylinders".
+3. Use the `fillna` method to fill the missing values in "Engine Cylinders" with the most frequent value from the previous step.
+4. Now, calculate the median value of "Engine Cylinders" once again.
 
 Has it changed?
 
@@ -86,15 +86,15 @@ Has it changed?
 
 ### Question 7
 
-* Select all the "Lotus" cars from the dataset.
-* Select only columns "Engine HP", "Engine Cylinders".
-* Now drop all duplicated rows using `drop_duplicates` method (you should get a dataframe with 9 rows).
-* Get the underlying NumPy array. Let's call it `X`.
-* Compute matrix-matrix multiplication between the transpose of `X` and `X`. To get the transpose, use `X.T`. Let's call the result `XTX`.
-* Invert `XTX`.
-* Create an array `y` with values `[1100, 800, 750, 850, 1300, 1000, 1000, 1300, 800]`.
-* Multiply the inverse of `XTX` with the transpose of `X`, and then multiply the result by `y`. Call the result `w`.
-* What's the value of the first element of `w`?
+1. Select all the "Lotus" cars from the dataset.
+2. Select only columns "Engine HP", "Engine Cylinders".
+3. Now drop all duplicated rows using `drop_duplicates` method (you should get a dataframe with 9 rows).
+4. Get the underlying NumPy array. Let's call it `X`.
+5. Compute matrix-matrix multiplication between the transpose of `X` and `X`. To get the transpose, use `X.T`. Let's call the result `XTX`.
+6. Invert `XTX`.
+7. Create an array `y` with values `[1100, 800, 750, 850, 1300, 1000, 1000, 1300, 800]`.
+8. Multiply the inverse of `XTX` with the transpose of `X`, and then multiply the result by `y`. Call the result `w`.
+9. What's the value of the first element of `w`?
 
 > **Note**: You just implemented linear regression. We'll talk about it in the next lesson.