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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 4: Regression Plots

Exploring the degree and nature of the relationship between variables is foundational in data science. One efficient way to discern this relationship, especially when predicting the behavior of one variable based on another, is through regression plots. Seaborn stands out with its extensive API, equipping users with intuitive tools to visualize regression lines and the spread of data around them.

Regression plots in Seaborn are designed to:

  • Analyze the linear relationships between two numeric variables.
  • Project potential outcomes based on the regression analysis.
  • Highlight the spread or deviation of data points from the regression line.

By harnessing the power of Seaborn's regression plots, practitioners can understand linear relationships, assess the goodness of fit, and make informed predictions.

Task

Using Seaborn, showcase the linear relationship in a dataset:

  1. Plot a regression line to see how two variables linearly relate.
  2. Differentiate data scatter and the regression line based on a categorical variable.

Task

Using Seaborn, showcase the linear relationship in a dataset:

  1. Plot a regression line to see how two variables linearly relate.
  2. Differentiate data scatter and the regression line based on a categorical variable.

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

Everything was clear?

Section 5. Chapter 4
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Challenge 4: Regression Plots

Exploring the degree and nature of the relationship between variables is foundational in data science. One efficient way to discern this relationship, especially when predicting the behavior of one variable based on another, is through regression plots. Seaborn stands out with its extensive API, equipping users with intuitive tools to visualize regression lines and the spread of data around them.

Regression plots in Seaborn are designed to:

  • Analyze the linear relationships between two numeric variables.
  • Project potential outcomes based on the regression analysis.
  • Highlight the spread or deviation of data points from the regression line.

By harnessing the power of Seaborn's regression plots, practitioners can understand linear relationships, assess the goodness of fit, and make informed predictions.

Task

Using Seaborn, showcase the linear relationship in a dataset:

  1. Plot a regression line to see how two variables linearly relate.
  2. Differentiate data scatter and the regression line based on a categorical variable.

Task

Using Seaborn, showcase the linear relationship in a dataset:

  1. Plot a regression line to see how two variables linearly relate.
  2. Differentiate data scatter and the regression line based on a categorical variable.

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

Everything was clear?

Section 5. Chapter 4
toggle bottom row

Challenge 4: Regression Plots

Exploring the degree and nature of the relationship between variables is foundational in data science. One efficient way to discern this relationship, especially when predicting the behavior of one variable based on another, is through regression plots. Seaborn stands out with its extensive API, equipping users with intuitive tools to visualize regression lines and the spread of data around them.

Regression plots in Seaborn are designed to:

  • Analyze the linear relationships between two numeric variables.
  • Project potential outcomes based on the regression analysis.
  • Highlight the spread or deviation of data points from the regression line.

By harnessing the power of Seaborn's regression plots, practitioners can understand linear relationships, assess the goodness of fit, and make informed predictions.

Task

Using Seaborn, showcase the linear relationship in a dataset:

  1. Plot a regression line to see how two variables linearly relate.
  2. Differentiate data scatter and the regression line based on a categorical variable.

Task

Using Seaborn, showcase the linear relationship in a dataset:

  1. Plot a regression line to see how two variables linearly relate.
  2. Differentiate data scatter and the regression line based on a categorical variable.

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

Everything was clear?

Exploring the degree and nature of the relationship between variables is foundational in data science. One efficient way to discern this relationship, especially when predicting the behavior of one variable based on another, is through regression plots. Seaborn stands out with its extensive API, equipping users with intuitive tools to visualize regression lines and the spread of data around them.

Regression plots in Seaborn are designed to:

  • Analyze the linear relationships between two numeric variables.
  • Project potential outcomes based on the regression analysis.
  • Highlight the spread or deviation of data points from the regression line.

By harnessing the power of Seaborn's regression plots, practitioners can understand linear relationships, assess the goodness of fit, and make informed predictions.

Task

Using Seaborn, showcase the linear relationship in a dataset:

  1. Plot a regression line to see how two variables linearly relate.
  2. Differentiate data scatter and the regression line based on a categorical variable.

Switch to desktop for real-world practiceContinue from where you are using one of the options below
Section 5. Chapter 4
Switch to desktop for real-world practiceContinue from where you are using one of the options below
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