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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: Relational Plots

Understanding relationships between variables is essential in data analysis. A robust way to visualize these relationships is through relational plots. Seaborn, with its intricate API, provides an array of tools to showcase how variables interact with one another.

Relational plots in Seaborn can:

Identify patterns, correlations, and outliers among two variables.
Present the relationship between multiple variables across complex datasets.
Delineate data over time or other common variables using hue semantics.

By delving into Seaborn's relational plots, analysts can derive insights into multivariate relationships and how they evolve across parameters.

Завдання

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Using Seaborn, visualize the relationships in a dataset:

Create a line plot to track changes in a variable over time or sequential order.
Display the relationship between two numeric variables with a scatter plot and differentiate data using color semantics.

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Секція 5. Розділ 3

Challenge 3: Relational Plots

Relational plots in Seaborn can:

Identify patterns, correlations, and outliers among two variables.
Present the relationship between multiple variables across complex datasets.
Delineate data over time or other common variables using hue semantics.

By delving into Seaborn's relational plots, analysts can derive insights into multivariate relationships and how they evolve across parameters.

Завдання

Swipe to show code editor

Using Seaborn, visualize the relationships in a dataset:

Create a line plot to track changes in a variable over time or sequential order.
Display the relationship between two numeric variables with a scatter plot and differentiate data using color semantics.

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 5. Розділ 3

Challenge 3: Relational Plots

Relational plots in Seaborn can:

Identify patterns, correlations, and outliers among two variables.
Present the relationship between multiple variables across complex datasets.
Delineate data over time or other common variables using hue semantics.

By delving into Seaborn's relational plots, analysts can derive insights into multivariate relationships and how they evolve across parameters.

Завдання

Swipe to show code editor

Using Seaborn, visualize the relationships in a dataset:

Create a line plot to track changes in a variable over time or sequential order.
Display the relationship between two numeric variables with a scatter plot and differentiate data using color semantics.

Все було зрозуміло?

Дякуємо за ваш відгук!

Relational plots in Seaborn can:

Identify patterns, correlations, and outliers among two variables.
Present the relationship between multiple variables across complex datasets.
Delineate data over time or other common variables using hue semantics.

By delving into Seaborn's relational plots, analysts can derive insights into multivariate relationships and how they evolve across parameters.

Завдання

Swipe to show code editor

Using Seaborn, visualize the relationships in a dataset:

Create a line plot to track changes in a variable over time or sequential order.
Display the relationship between two numeric variables with a scatter plot and differentiate data using color semantics.

Секція 5. Розділ 3