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

Challenge 3: Subplots

With Matplotlib, not only can you create diverse and intricate visualizations, but you can also effectively organize and display multiple plots together using subplots and advanced layout management techniques. Grasping these techniques can offer multiple advantages:

Comparison: By visualizing multiple plots side by side, you can make quicker comparisons and observations.
Organization: Rather than having multiple separate figures, you can combine related visualizations into one cohesive figure.
Flexibility: Matplotlib allows for a high degree of customization when it comes to positioning and aligning subplots, ensuring your visualizations are both functional and aesthetically pleasing.

Mastering subplots and layout management is pivotal for conveying multi-faceted insights and constructing comprehensive visual narratives.

Note

Some subplots have been intentionally left empty.

Task

Swipe to show code editor

Using Matplotlib, perform the following tasks related to subplots and layout management:

Create a 2x2 grid of subplots.
Plot a line graph on the top-left subplot.
Plot a scatter plot on the bottom-right subplot.

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

Everything was clear?

Thanks for your feedback!

Section 4. Chapter 3

Challenge 3: Subplots

Comparison: By visualizing multiple plots side by side, you can make quicker comparisons and observations.
Organization: Rather than having multiple separate figures, you can combine related visualizations into one cohesive figure.
Flexibility: Matplotlib allows for a high degree of customization when it comes to positioning and aligning subplots, ensuring your visualizations are both functional and aesthetically pleasing.

Mastering subplots and layout management is pivotal for conveying multi-faceted insights and constructing comprehensive visual narratives.

Note

Some subplots have been intentionally left empty.

Task

Swipe to show code editor

Using Matplotlib, perform the following tasks related to subplots and layout management:

Create a 2x2 grid of subplots.
Plot a line graph on the top-left subplot.
Plot a scatter plot on the bottom-right subplot.

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

Everything was clear?

Thanks for your feedback!

Section 4. Chapter 3

Challenge 3: Subplots

Comparison: By visualizing multiple plots side by side, you can make quicker comparisons and observations.
Organization: Rather than having multiple separate figures, you can combine related visualizations into one cohesive figure.
Flexibility: Matplotlib allows for a high degree of customization when it comes to positioning and aligning subplots, ensuring your visualizations are both functional and aesthetically pleasing.

Mastering subplots and layout management is pivotal for conveying multi-faceted insights and constructing comprehensive visual narratives.

Note

Some subplots have been intentionally left empty.

Task

Swipe to show code editor

Using Matplotlib, perform the following tasks related to subplots and layout management:

Create a 2x2 grid of subplots.
Plot a line graph on the top-left subplot.
Plot a scatter plot on the bottom-right subplot.

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

Everything was clear?

Thanks for your feedback!

Comparison: By visualizing multiple plots side by side, you can make quicker comparisons and observations.
Organization: Rather than having multiple separate figures, you can combine related visualizations into one cohesive figure.
Flexibility: Matplotlib allows for a high degree of customization when it comes to positioning and aligning subplots, ensuring your visualizations are both functional and aesthetically pleasing.

Mastering subplots and layout management is pivotal for conveying multi-faceted insights and constructing comprehensive visual narratives.

Note

Some subplots have been intentionally left empty.

Task

Swipe to show code editor

Using Matplotlib, perform the following tasks related to subplots and layout management:

Create a 2x2 grid of subplots.
Plot a line graph on the top-left subplot.
Plot a scatter plot on the bottom-right subplot.

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

Section 4. Chapter 3

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