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Data Science Interview Challenge
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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

book
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.

Opgave

Swipe to start coding

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.

Løsning

Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 2. Kapitel 5
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book
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.

Opgave

Swipe to start coding

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.

Løsning

Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 2. Kapitel 5
Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
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