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 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.

Завдання

Using Seaborn, showcase the linear relationship in a dataset:

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

Завдання

Using Seaborn, showcase the linear relationship in a dataset:

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

Все було зрозуміло?

Секція 5. Розділ 4

Challenge 4: Regression Plots

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.

Завдання

Using Seaborn, showcase the linear relationship in a dataset:

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

Завдання

Using Seaborn, showcase the linear relationship in a dataset:

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

Все було зрозуміло?

Секція 5. Розділ 4

Challenge 4: Regression Plots

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.

Завдання

Using Seaborn, showcase the linear relationship in a dataset:

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

Завдання

Using Seaborn, showcase the linear relationship in a dataset:

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

Все було зрозуміло?

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.

Завдання

Using Seaborn, showcase the linear relationship in a dataset:

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

Секція 5. Розділ 4

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