Course Content

Data Science Interview Challenge

1. Python

Introduction Challenge 1: List Comprehension Challenge 2: String Manipulation Challenge 3: Dictionary Manipulation Challenge 4: Nested Loops Challenge 5: Classes Theoretical Questions

2. NumPy

Challenge 1: Array Creation Challenge 2: Array Manipulation Challenge 3: Statistical Insights Challenge 4: Handling Missing Values Challenge 5: Subarray Sorting Theoretical Questions

3. Pandas

Challenge 1: DataFrame Creation Challenge 2: Data Grouping Challenge 3: Indexing and MultiIndexing Challenge 4: Altering DataFrame Challenge 5: Iterating Over Data Theoretical Questions

4. Matplotlib

Challenge 1: Fundamentals of Plotting Challenge 2: Other Graph Types Challenge 3: Subplots Challenge 4: Customizing Plots Challenge 5: Adding Text to Plots Theoretical Questions

5. Seaborn

Challenge 1: Visualizing Distributions Challenge 2: Exploring Categorical Data Challenge 3: Relational Plots Challenge 4: Regression Plots Challenge 5: Matrix Plots Theoretical Questions

6. Statistics

Challenge 1: Probabilities and Distributions Challenge 2: Bayes' Theorem Challenge 3: Hypothesis Testing Challenge 4: Confidence Intervals Challenge 5: Correlation Theoretical Questions

7. Scikit-learn

Challenge 1: Data Scaling Challenge 2: Basic Model Creation Challenge 3: Pipelines Challenge 4: Cross-validation Challenge 5: Hyperparameter Tuning Theoretical Questions

Challenge 3: Hypothesis Testing

The fascinating realm of statistics houses the intricate process of hypothesis testing. At its core, hypothesis testing is about making inferences regarding populations based on sample data. We formulate hypotheses and test them, drawing conclusions about broader datasets by analyzing a subset.

For instance, if you're studying the impact of a new teaching method in a classroom and observe a significant improvement in students' grades, can you conclusively say that the method is effective? The answer lies in hypothesis testing.

Here's the dataset we'll be using in this chapter. Feel free to dive in and explore it before tackling the task.


              123456789101112131415161718
            
import matplotlib.pyplot as plt
import seaborn as sns

# Load the dataset
data = sns.load_dataset('tips')

# Sample of data
display(data.head())

# Total bill amounts grouped by smoking status
sns.boxplot(x='smoker', y='total_bill', data=data)
plt.title('Total Bill Amounts Grouped by Smoking Status')
plt.show()

# Number of smokers vs. non-smokers by gender
sns.countplot(x='sex', hue='smoker', data=data)
plt.title('Number of Smokers vs. Non-Smokers by Gender')
plt.show()

Task

Swipe to show code editor

In this exercise, leveraging the Seaborn's tips dataset, you'll:

Test if there's a significant difference in the total_bill amounts between smokers and non-smokers. Use Mann–Whitney U test.
Examine the relationship between the sex and smoker columns, determining if these two categorical variables are independent of each other.

Note

In this task, the significance level (alpha) for the p-value is set at 0.1, rather than the conventional 0.05. The choice of alpha can vary across tasks based on the context, the level of rigor required, or specific industry practices; commonly adopted values include 0.01, 0.05, and 0.1.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 6. Chapter 3

Challenge 3: Hypothesis Testing

Here's the dataset we'll be using in this chapter. Feel free to dive in and explore it before tackling the task.


              123456789101112131415161718
            
import matplotlib.pyplot as plt
import seaborn as sns

# Load the dataset
data = sns.load_dataset('tips')

# Sample of data
display(data.head())

# Total bill amounts grouped by smoking status
sns.boxplot(x='smoker', y='total_bill', data=data)
plt.title('Total Bill Amounts Grouped by Smoking Status')
plt.show()

# Number of smokers vs. non-smokers by gender
sns.countplot(x='sex', hue='smoker', data=data)
plt.title('Number of Smokers vs. Non-Smokers by Gender')
plt.show()

Task

Swipe to show code editor

In this exercise, leveraging the Seaborn's tips dataset, you'll:

Test if there's a significant difference in the total_bill amounts between smokers and non-smokers. Use Mann–Whitney U test.
Examine the relationship between the sex and smoker columns, determining if these two categorical variables are independent of each other.

Note

In this task, the significance level (alpha) for the p-value is set at 0.1, rather than the conventional 0.05. The choice of alpha can vary across tasks based on the context, the level of rigor required, or specific industry practices; commonly adopted values include 0.01, 0.05, and 0.1.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 6. Chapter 3

Challenge 3: Hypothesis Testing

Here's the dataset we'll be using in this chapter. Feel free to dive in and explore it before tackling the task.


              123456789101112131415161718
            
import matplotlib.pyplot as plt
import seaborn as sns

# Load the dataset
data = sns.load_dataset('tips')

# Sample of data
display(data.head())

# Total bill amounts grouped by smoking status
sns.boxplot(x='smoker', y='total_bill', data=data)
plt.title('Total Bill Amounts Grouped by Smoking Status')
plt.show()

# Number of smokers vs. non-smokers by gender
sns.countplot(x='sex', hue='smoker', data=data)
plt.title('Number of Smokers vs. Non-Smokers by Gender')
plt.show()

Task

Swipe to show code editor

In this exercise, leveraging the Seaborn's tips dataset, you'll:

Test if there's a significant difference in the total_bill amounts between smokers and non-smokers. Use Mann–Whitney U test.
Examine the relationship between the sex and smoker columns, determining if these two categorical variables are independent of each other.

Note

In this task, the significance level (alpha) for the p-value is set at 0.1, rather than the conventional 0.05. The choice of alpha can vary across tasks based on the context, the level of rigor required, or specific industry practices; commonly adopted values include 0.01, 0.05, and 0.1.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Here's the dataset we'll be using in this chapter. Feel free to dive in and explore it before tackling the task.


              123456789101112131415161718
            
import matplotlib.pyplot as plt
import seaborn as sns

# Load the dataset
data = sns.load_dataset('tips')

# Sample of data
display(data.head())

# Total bill amounts grouped by smoking status
sns.boxplot(x='smoker', y='total_bill', data=data)
plt.title('Total Bill Amounts Grouped by Smoking Status')
plt.show()

# Number of smokers vs. non-smokers by gender
sns.countplot(x='sex', hue='smoker', data=data)
plt.title('Number of Smokers vs. Non-Smokers by Gender')
plt.show()

Task

Swipe to show code editor

In this exercise, leveraging the Seaborn's tips dataset, you'll:

Test if there's a significant difference in the total_bill amounts between smokers and non-smokers. Use Mann–Whitney U test.
Examine the relationship between the sex and smoker columns, determining if these two categorical variables are independent of each other.

Note

In this task, the significance level (alpha) for the p-value is set at 0.1, rather than the conventional 0.05. The choice of alpha can vary across tasks based on the context, the level of rigor required, or specific industry practices; commonly adopted values include 0.01, 0.05, and 0.1.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 6. Chapter 3

Switch to desktop for real-world practiceContinue from where you are using one of the options below