Update A1_Intro_to_DataScience_and_ML.md

Author: clizarraga-UAD7

docs/mlpaths/A1_Intro_to_DataScience_and_ML.md

@@ -181,9 +181,6 @@ This quiz helps you self-assess your understanding. Answers can be verified by r
     *(Answer: (1) :eyes:)*
     { .annotate }
     1. Answer is: <b>b</b>
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-       2. Answer is: <b>c</b>
-
 
 2. Consider the following Python code using Pandas:
     ```python
@@ -198,17 +195,17 @@ This quiz helps you self-assess your understanding. Answers can be verified by r
     b)  0<br>
     c)  Approximately 2.67<br>
     d)  6<br>
-    *(Answer: (2) :eyes:)*
+    *(Answer: (a) :eyes:)*
     { .annotate }
-    2. Answer is: <b>c</b> 
+    a. Answer is: <b>c</b> 
 
 
 3. Which Python library is primarily used for creating statistical visualizations like heatmaps and pair plots with concise syntax?<br>
     a)  NumPy<br>
     b)  Seaborn<br>
     c)  Pandas<br>
     d)  Scikit-learn<br>
-    *(Answer:(1) :eyes:)*
+    *(Answer: (1) :eyes:)*
     { .annotate }
     1. Answer is: <b>b</b> 
 
@@ -218,7 +215,7 @@ This quiz helps you self-assess your understanding. Answers can be verified by r
     b)  It's the categorical label or class that the model aims to predict.<br>
     c)  It's a numerical value the model tries to estimate.<br>
     d)  It's a technique for reducing the number of features.<br>
-    *(Answer:(1) :eyes:)*
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     { .annotate }
     1. Answer is: <b>b</b> 
 
@@ -227,34 +224,34 @@ This quiz helps you self-assess your understanding. Answers can be verified by r
     b)  To separate features from the target variable within a single dataset.<br>
     c)  To divide a dataset into one part for training the model and another, unseen part for evaluating its performance.<br>
     d)  To visualize the distribution of data.<br>
-    *(Answer:(2) :eyes:)*
+    *(Answer: (a) :eyes:)*
     { .annotate }    
-    2. Answer is: <b>c</b> 
+    a. Answer is: <b>c</b> 
 
 6. If you want to create a scatter plot in Python to visualize the relationship between 'Height' and 'Weight' columns in a Pandas DataFrame `df`, which line of code is most appropriate using Seaborn?<br>
     a)  `sns.histplot(data=df, x='Height', y='Weight')`<br>
     b)  `sns.boxplot(data=df, x='Height', y='Weight')`<br>
     c)  `sns.scatterplot(data=df, x='Height', y='Weight')`<br>
     d)  `df.plot(kind='scatter', x='Height', y='Weight')` (This is Pandas plotting, not Seaborn directly)<br>
-    *(Answer:(2) :eyes:)*
+    *(Answer: (a) :eyes:)*
     { .annotate }    
-    2. Answer is: <b>c</b> 
+    a. Answer is: <b>c</b> 
 
 7. You have loaded a dataset into a Pandas DataFrame called `sales_df`. How would you display the first 10 rows of this DataFrame?<br>
     a)  `sales_df.show(10)`<br>
     b)  `sales_df.display_head(10)`<br>
     c)  `sales_df.head(10)`<br>
     d)  `sales_df.first(10)`<br>
-    *(Answer:(2) :eyes:)*
+    *(Answer: (a) :eyes:)*
     { .annotate }    
-    2. Answer is: <b>c</b> 
+    a. Answer is: <b>c</b> 
 
 8. When you encounter a Python error message that you don't understand while working in a Jupyter Notebook, how can an LLM assist you most effectively?<br>
     a)  By automatically fixing the code in your notebook.<br>
     b)  By explaining what the error message typically means, suggesting possible causes, and providing examples of how to fix similar errors.<br>
     c)  By providing a link to the full Python documentation without context.<br>
     d)  By advising you to restart your computer.<br>
-    *(Answer:(1) :eyes:)*
+    *(Answer: (1) :eyes:)*
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     1. Answer is: <b>b</b> 
 
@@ -263,18 +260,18 @@ This quiz helps you self-assess your understanding. Answers can be verified by r
     b)  The first five rows of the DataFrame.<br>
     c)  A concise summary of the DataFrame, including data types of columns and non-null counts.<br>
     d)  The correlation matrix of numerical columns.<br>
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+    *(Answer: (a) :eyes:)*
     { .annotate }    
-    2. Answer is: <b>c</b> 
+    a. Answer is: <b>c</b> 
 
 10. Which of these tasks falls under the 'Data Cleaning/Preparation' stage of the data science workflow?<br>
     a)  Defining business objectives.<br>
     b)  Training a machine learning model.<br>
     c)  Handling missing values and transforming variables.<br>
     d)  Presenting results to stakeholders.<br>
-    *(Answer:(2) :eyes:)*
+    *(Answer: (a) :eyes:)*
     { .annotate }    
-    2. Answer is: <b>c</b> 
+    a. Answer is: <b>c</b> 
 
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