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Data Science Interview Challenge
course content

Course Content

Data Science Interview Challenge

Data Science Interview Challenge

1. Python
IntroductionChallenge 1: List ComprehensionChallenge 2: String ManipulationChallenge 3: Dictionary ManipulationChallenge 4: Nested LoopsChallenge 5: ClassesTheoretical Questions
2. NumPy
Challenge 1: Array CreationChallenge 2: Array ManipulationChallenge 3: Statistical InsightsChallenge 4: Handling Missing ValuesChallenge 5: Subarray SortingTheoretical Questions
3. Pandas
Challenge 1: DataFrame CreationChallenge 2: Data GroupingChallenge 3: Indexing and MultiIndexingChallenge 4: Altering DataFrameChallenge 5: Iterating Over DataTheoretical Questions
4. Matplotlib
Challenge 1: Fundamentals of PlottingChallenge 2: Other Graph TypesChallenge 3: SubplotsChallenge 4: Customizing PlotsChallenge 5: Adding Text to PlotsTheoretical Questions
5. Seaborn
Challenge 1: Visualizing DistributionsChallenge 2: Exploring Categorical DataChallenge 3: Relational PlotsChallenge 4: Regression PlotsChallenge 5: Matrix PlotsTheoretical Questions
6. Statistics
Challenge 1: Probabilities and DistributionsChallenge 2: Bayes' TheoremChallenge 3: Hypothesis TestingChallenge 4: Confidence IntervalsChallenge 5: CorrelationTheoretical Questions
7. Scikit-learn
Challenge 1: Data ScalingChallenge 2: Basic Model CreationChallenge 3: PipelinesChallenge 4: Cross-validationChallenge 5: Hyperparameter TuningTheoretical Questions

bookChallenge 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.

Task

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.

  1. Extract the central subarray.
  2. Flatten the extracted array.
  3. Sort the subarray.
  4. Integrate it back into the original array.

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Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 5
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bookChallenge 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.

Task

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.

  1. Extract the central subarray.
  2. Flatten the extracted array.
  3. Sort the subarray.
  4. Integrate it back into the original array.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 5
toggle bottom row

bookChallenge 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.

Task

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.

  1. Extract the central subarray.
  2. Flatten the extracted array.
  3. Sort the subarray.
  4. Integrate it back into the original array.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

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.

Task

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.

  1. Extract the central subarray.
  2. Flatten the extracted array.
  3. Sort the subarray.
  4. Integrate it back into the original array.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 5
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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