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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 5: Subarray Sorting

As you dive deeper into data science, you often encounter the need for advanced data manipulations. NumPy caters to this with:

Sophisticated Reshaping Tools: Beyond the basics, you can flatten, tile, and even stack arrays in intricate ways.
Seamless Integration: You can easily mix and match reshaping tools with other NumPy functions for fluid data processing.
Maintained Data Integrity: Despite complex operations, NumPy ensures the underlying data remains consistent.

For intricate data transformation tasks, NumPy's advanced reshaping tools prove invaluable.

Завдання

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Given a 5x5 NumPy array filled with random integers between 1 and 100, extract the central 3x3 subarray. Flatten this subarray, sort it, and then put it back into the original 5x5 array while maintaining its original structure.

Extract the central subarray.
Flatten the extracted array.
Sort the subarray.
Integrate it back into the original array.

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Секція 2. Розділ 5

Challenge 5: Subarray Sorting

As you dive deeper into data science, you often encounter the need for advanced data manipulations. NumPy caters to this with:

Sophisticated Reshaping Tools: Beyond the basics, you can flatten, tile, and even stack arrays in intricate ways.
Seamless Integration: You can easily mix and match reshaping tools with other NumPy functions for fluid data processing.
Maintained Data Integrity: Despite complex operations, NumPy ensures the underlying data remains consistent.

For intricate data transformation tasks, NumPy's advanced reshaping tools prove invaluable.

Завдання

Swipe to show code editor

Extract the central subarray.
Flatten the extracted array.
Sort the subarray.
Integrate it back into the original array.

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 2. Розділ 5

Challenge 5: Subarray Sorting

As you dive deeper into data science, you often encounter the need for advanced data manipulations. NumPy caters to this with:

Sophisticated Reshaping Tools: Beyond the basics, you can flatten, tile, and even stack arrays in intricate ways.
Seamless Integration: You can easily mix and match reshaping tools with other NumPy functions for fluid data processing.
Maintained Data Integrity: Despite complex operations, NumPy ensures the underlying data remains consistent.

For intricate data transformation tasks, NumPy's advanced reshaping tools prove invaluable.

Завдання

Swipe to show code editor

Extract the central subarray.
Flatten the extracted array.
Sort the subarray.
Integrate it back into the original array.

Все було зрозуміло?

Дякуємо за ваш відгук!

As you dive deeper into data science, you often encounter the need for advanced data manipulations. NumPy caters to this with:

Sophisticated Reshaping Tools: Beyond the basics, you can flatten, tile, and even stack arrays in intricate ways.
Seamless Integration: You can easily mix and match reshaping tools with other NumPy functions for fluid data processing.
Maintained Data Integrity: Despite complex operations, NumPy ensures the underlying data remains consistent.

For intricate data transformation tasks, NumPy's advanced reshaping tools prove invaluable.

Завдання

Swipe to show code editor

Extract the central subarray.
Flatten the extracted array.
Sort the subarray.
Integrate it back into the original array.

Секція 2. Розділ 5