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Data Science Interview Challenge
course content

Course Content

Data Science Interview Challenge

Data Science Interview Challenge

1. Python

IntroductionChallenge 1: List ComprehensionChallenge 2: String ManipulationChallenge 3: Dictionary ManipulationChallenge 4: Nested LoopsChallenge 5: ClassesTheoretical Questions

2. NumPy

Challenge 1: Array CreationChallenge 2: Array ManipulationChallenge 3: Statistical InsightsChallenge 4: Handling Missing ValuesChallenge 5: Subarray SortingTheoretical Questions

3. Pandas

Challenge 1: DataFrame CreationChallenge 2: Data GroupingChallenge 3: Indexing and MultiIndexingChallenge 4: Altering DataFrameChallenge 5: Iterating Over DataTheoretical Questions

4. Matplotlib

Challenge 1: Fundamentals of PlottingChallenge 2: Other Graph TypesChallenge 3: SubplotsChallenge 4: Customizing PlotsChallenge 5: Adding Text to PlotsTheoretical Questions

5. Seaborn

Challenge 1: Visualizing DistributionsChallenge 2: Exploring Categorical DataChallenge 3: Relational PlotsChallenge 4: Regression PlotsChallenge 5: Matrix PlotsTheoretical Questions

6. Statistics

Challenge 1: Probabilities and DistributionsChallenge 2: Bayes' TheoremChallenge 3: Hypothesis TestingChallenge 4: Confidence IntervalsChallenge 5: CorrelationTheoretical Questions

7. Scikit-learn

Challenge 1: Data ScalingChallenge 2: Basic Model CreationChallenge 3: PipelinesChallenge 4: Cross-validationChallenge 5: Hyperparameter TuningTheoretical 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:

  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.
  2. 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.

Task

Using the Seaborn's tips dataset, you will:

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

Task

Using the Seaborn's tips dataset, you will:

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

Everything was clear?

Section 6. Chapter 1
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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:

  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.
  2. 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.

Task

Using the Seaborn's tips dataset, you will:

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

Task

Using the Seaborn's tips dataset, you will:

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

Everything was clear?

Section 6. Chapter 1
toggle bottom row

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:

  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.
  2. 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.

Task

Using the Seaborn's tips dataset, you will:

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

Task

Using the Seaborn's tips dataset, you will:

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

Everything was clear?

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:

  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.
  2. 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.

Task

Using the Seaborn's tips dataset, you will:

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

Section 6. Chapter 1
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