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 1: Probabilities and Distributions

In the vast expanse of statistics, two foundational concepts reign supreme: probabilities and distributions. These twin pillars serve as the bedrock upon which much of statistical theory and application are built.

Probability is a measure of uncertainty. It quantifies the likelihood of an event or outcome occurring, always within the range of 0 to 1.

Distributions, on the other hand, provide a holistic view of all possible outcomes of a random variable and the associated probabilities of each outcome. They chart out the behavior of data, be it in the form of a series of coin tosses, heights of individuals in a population, or the time taken for a bus to arrive. Two primary categories of distributions exist:

Discrete Distributions: These depict scenarios where the set of possible outcomes is distinct and finite. An example is the Binomial distribution, which could represent the number of heads obtained in a set number of coin tosses.
Continuous Distributions: Here, the outcomes can take on any value within a given range. The Normal or Gaussian distribution is a classic example, representing data that clusters around a mean or central value.

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


              12345678910111213
            
import matplotlib.pyplot as plt
import seaborn as sns

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

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

# Visualize the distribution of 'total_bill'
sns.displot(data['total_bill'])
plt.title('Distribution of Total Bill')
plt.show()

Завдання

Using the Seaborn's tips dataset, you will:

Extract key statistical metrics for the total_bill column to comprehend its central tendencies and spread.
Use a Q-Q plot to visualize how the total_bill data conforms to a normal distribution.
Utilize the Shapiro-Wilk test to statistically assess the normality of the total_bill distribution.
Determine the probability that a randomly selected bill from the dataset is more than $20.

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Дякуємо за ваш відгук!

Секція 6. Розділ 1

Challenge 1: Probabilities and Distributions

Probability is a measure of uncertainty. It quantifies the likelihood of an event or outcome occurring, always within the range of 0 to 1.

Discrete Distributions: These depict scenarios where the set of possible outcomes is distinct and finite. An example is the Binomial distribution, which could represent the number of heads obtained in a set number of coin tosses.
Continuous Distributions: Here, the outcomes can take on any value within a given range. The Normal or Gaussian distribution is a classic example, representing data that clusters around a mean or central value.

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


              12345678910111213
            
import matplotlib.pyplot as plt
import seaborn as sns

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

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

# Visualize the distribution of 'total_bill'
sns.displot(data['total_bill'])
plt.title('Distribution of Total Bill')
plt.show()

Завдання

Using the Seaborn's tips dataset, you will:

Extract key statistical metrics for the total_bill column to comprehend its central tendencies and spread.
Use a Q-Q plot to visualize how the total_bill data conforms to a normal distribution.
Utilize the Shapiro-Wilk test to statistically assess the normality of the total_bill distribution.
Determine the probability that a randomly selected bill from the dataset is more than $20.

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 6. Розділ 1

Challenge 1: Probabilities and Distributions

Probability is a measure of uncertainty. It quantifies the likelihood of an event or outcome occurring, always within the range of 0 to 1.

Discrete Distributions: These depict scenarios where the set of possible outcomes is distinct and finite. An example is the Binomial distribution, which could represent the number of heads obtained in a set number of coin tosses.
Continuous Distributions: Here, the outcomes can take on any value within a given range. The Normal or Gaussian distribution is a classic example, representing data that clusters around a mean or central value.

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


              12345678910111213
            
import matplotlib.pyplot as plt
import seaborn as sns

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

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

# Visualize the distribution of 'total_bill'
sns.displot(data['total_bill'])
plt.title('Distribution of Total Bill')
plt.show()

Завдання

Using the Seaborn's tips dataset, you will:

Extract key statistical metrics for the total_bill column to comprehend its central tendencies and spread.
Use a Q-Q plot to visualize how the total_bill data conforms to a normal distribution.
Utilize the Shapiro-Wilk test to statistically assess the normality of the total_bill distribution.
Determine the probability that a randomly selected bill from the dataset is more than $20.

Все було зрозуміло?

Дякуємо за ваш відгук!

Probability is a measure of uncertainty. It quantifies the likelihood of an event or outcome occurring, always within the range of 0 to 1.

Discrete Distributions: These depict scenarios where the set of possible outcomes is distinct and finite. An example is the Binomial distribution, which could represent the number of heads obtained in a set number of coin tosses.
Continuous Distributions: Here, the outcomes can take on any value within a given range. The Normal or Gaussian distribution is a classic example, representing data that clusters around a mean or central value.

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


              12345678910111213
            
import matplotlib.pyplot as plt
import seaborn as sns

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

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

# Visualize the distribution of 'total_bill'
sns.displot(data['total_bill'])
plt.title('Distribution of Total Bill')
plt.show()

Завдання

Using the Seaborn's tips dataset, you will:

Extract key statistical metrics for the total_bill column to comprehend its central tendencies and spread.
Use a Q-Q plot to visualize how the total_bill data conforms to a normal distribution.
Utilize the Shapiro-Wilk test to statistically assess the normality of the total_bill distribution.
Determine the probability that a randomly selected bill from the dataset is more than $20.

Секція 6. Розділ 1