Зміст курсу

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 5: Correlation

Distinguishing between correlation and causation is a cornerstone concept in statistics. While correlation denotes a relationship between two variables, it doesn't imply that one variable causes the other. Causation, on the other hand, suggests a direct relationship where a change in one variable results in a change in another.

For example, consider an ice cream shop that notices sales increasing in the summer months and decreasing in the winter. While there's a correlation between temperature and ice cream sales, it doesn't mean higher temperatures cause an increase in sales. There could be confounding variables, such as people preferring cold treats in hot weather. People don't buy ice cream just because the temperature increased; they buy it because they find it refreshing in the heat.

So, while there's a clear correlation between temperature and ice cream sales, we cannot definitively say that higher temperatures cause an increase in sales without considering other factors. Making causal statements requires more rigorous examination and, ideally, controlled experiments to rule out or account for potential confounding variables.

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


              1234567
            
import seaborn as sns

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

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

Завдання

Using Seaborn's tips dataset, perform the following tasks:

Determine the Pearson correlation coefficient between the total_bill and tip columns, which gives a measure of the linear association between the two numerical variables.
Visualize the relationship between total_bill (for X-axis) and tip (for Y-axis) with a linear regression plot, allowing you to observe how changes in the total_bill might predict changes in the tip.
Create a matrix of correlations for the categorical variables in the dataset using Cramér's V, a measure based on the chi-squared statistic which quantifies the association between two categorical variables.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 6. Розділ 5

Challenge 5: Correlation

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


              1234567
            
import seaborn as sns

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

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

Завдання

Using Seaborn's tips dataset, perform the following tasks:

Determine the Pearson correlation coefficient between the total_bill and tip columns, which gives a measure of the linear association between the two numerical variables.
Visualize the relationship between total_bill (for X-axis) and tip (for Y-axis) with a linear regression plot, allowing you to observe how changes in the total_bill might predict changes in the tip.
Create a matrix of correlations for the categorical variables in the dataset using Cramér's V, a measure based on the chi-squared statistic which quantifies the association between two categorical variables.

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 6. Розділ 5

Challenge 5: Correlation

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


              1234567
            
import seaborn as sns

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

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

Завдання

Using Seaborn's tips dataset, perform the following tasks:

Determine the Pearson correlation coefficient between the total_bill and tip columns, which gives a measure of the linear association between the two numerical variables.
Visualize the relationship between total_bill (for X-axis) and tip (for Y-axis) with a linear regression plot, allowing you to observe how changes in the total_bill might predict changes in the tip.
Create a matrix of correlations for the categorical variables in the dataset using Cramér's V, a measure based on the chi-squared statistic which quantifies the association between two categorical variables.

Все було зрозуміло?

Дякуємо за ваш відгук!

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


              1234567
            
import seaborn as sns

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

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

Завдання

Using Seaborn's tips dataset, perform the following tasks:

Determine the Pearson correlation coefficient between the total_bill and tip columns, which gives a measure of the linear association between the two numerical variables.
Visualize the relationship between total_bill (for X-axis) and tip (for Y-axis) with a linear regression plot, allowing you to observe how changes in the total_bill might predict changes in the tip.
Create a matrix of correlations for the categorical variables in the dataset using Cramér's V, a measure based on the chi-squared statistic which quantifies the association between two categorical variables.

Секція 6. Розділ 5