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Data Science Interview Challenge
Data Science Interview Challenge
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:
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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.
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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.
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()
Swipe to show code editor
Using the Seaborn's tips dataset, you will:
- Extract key statistical metrics for the
total_billcolumn to comprehend its central tendencies and spread. - Use a Q-Q plot to visualize how the
total_billdata conforms to a normal distribution. - Utilize the Shapiro-Wilk test to statistically assess the normality of the
total_billdistribution. - Determine the probability that a randomly selected bill from the dataset is more than $20.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
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.
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()
Swipe to show code editor
Using the Seaborn's tips dataset, you will:
- Extract key statistical metrics for the
total_billcolumn to comprehend its central tendencies and spread. - Use a Q-Q plot to visualize how the
total_billdata conforms to a normal distribution. - Utilize the Shapiro-Wilk test to statistically assess the normality of the
total_billdistribution. - Determine the probability that a randomly selected bill from the dataset is more than $20.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів