Course Content

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

Data Science Interview Challenge

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:

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

Code Description

sns.regplot(x=year, y=passengers)

This code utilizes the regplot function from Seaborn to draw a regression line, indicating the linear relationship between the variables year and passengers. It aids in visualizing the trend and potential predictive capacity between these two variables.

sns.lmplot(x='year', y='passengers', hue='month', data=data)

The lmplot function is a powerful tool that extends the capability of regplot. In this instance, it plots a regression line between year and passengers but with a twist: the data points and regression lines are differentiated by months using the hue parameter, allowing for a nuanced understanding of monthly variations.

Everything was clear?

Section 5. Chapter 4