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 5: Adding Text to Plots

Annotating and adding textual elements to plots can provide context and clarity to the data being presented. These textual elements can:

Highlight specific data points or trends in the data.
Explain the significance of certain features of the plot.
Provide additional details or supplementary information that doesn't fit neatly into the x or y axis labels.

Matplotlib offers versatile text and annotation functions that allow you to place text in arbitrary positions, adjust its style, and even point to specific parts of the chart.

Enhancing your plots with appropriate textual elements ensures that the viewer can grasp the full story behind the data.

Завдання

Swipe to show code editor

Using Matplotlib, add text and annotations to a simple line plot:

Label the x-axis as X-Axis and the y-axis as Y-Axis.
Determine the maximum and minimum y-values from the given data and annotate these points on the plot. Also, include arrows pointing to the max and min points.
Insert a textual note at the coordinate (2.45, 2) with the content 'The greatest decline', rotated at an angle of -72 degrees.

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Секція 4. Розділ 5

Challenge 5: Adding Text to Plots

Annotating and adding textual elements to plots can provide context and clarity to the data being presented. These textual elements can:

Highlight specific data points or trends in the data.
Explain the significance of certain features of the plot.
Provide additional details or supplementary information that doesn't fit neatly into the x or y axis labels.

Matplotlib offers versatile text and annotation functions that allow you to place text in arbitrary positions, adjust its style, and even point to specific parts of the chart.

Enhancing your plots with appropriate textual elements ensures that the viewer can grasp the full story behind the data.

Завдання

Swipe to show code editor

Using Matplotlib, add text and annotations to a simple line plot:

Label the x-axis as X-Axis and the y-axis as Y-Axis.
Determine the maximum and minimum y-values from the given data and annotate these points on the plot. Also, include arrows pointing to the max and min points.
Insert a textual note at the coordinate (2.45, 2) with the content 'The greatest decline', rotated at an angle of -72 degrees.

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 4. Розділ 5

Challenge 5: Adding Text to Plots

Annotating and adding textual elements to plots can provide context and clarity to the data being presented. These textual elements can:

Highlight specific data points or trends in the data.
Explain the significance of certain features of the plot.
Provide additional details or supplementary information that doesn't fit neatly into the x or y axis labels.

Matplotlib offers versatile text and annotation functions that allow you to place text in arbitrary positions, adjust its style, and even point to specific parts of the chart.

Enhancing your plots with appropriate textual elements ensures that the viewer can grasp the full story behind the data.

Завдання

Swipe to show code editor

Using Matplotlib, add text and annotations to a simple line plot:

Label the x-axis as X-Axis and the y-axis as Y-Axis.
Determine the maximum and minimum y-values from the given data and annotate these points on the plot. Also, include arrows pointing to the max and min points.
Insert a textual note at the coordinate (2.45, 2) with the content 'The greatest decline', rotated at an angle of -72 degrees.

Все було зрозуміло?

Дякуємо за ваш відгук!

Annotating and adding textual elements to plots can provide context and clarity to the data being presented. These textual elements can:

Highlight specific data points or trends in the data.
Explain the significance of certain features of the plot.
Provide additional details or supplementary information that doesn't fit neatly into the x or y axis labels.

Matplotlib offers versatile text and annotation functions that allow you to place text in arbitrary positions, adjust its style, and even point to specific parts of the chart.

Enhancing your plots with appropriate textual elements ensures that the viewer can grasp the full story behind the data.

Завдання

Swipe to show code editor

Using Matplotlib, add text and annotations to a simple line plot:

Label the x-axis as X-Axis and the y-axis as Y-Axis.
Determine the maximum and minimum y-values from the given data and annotate these points on the plot. Also, include arrows pointing to the max and min points.
Insert a textual note at the coordinate (2.45, 2) with the content 'The greatest decline', rotated at an angle of -72 degrees.

Секція 4. Розділ 5