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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

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Challenge 2: Array Manipulation

NumPy offers a comprehensive set of tools for modifying and reshaping arrays. The key benefits include:

  • Diverse Capabilities: From reshaping to splitting, NumPy offers a wide range of functions to modify arrays as per requirements.
  • In-place Operations: Many NumPy functions can perform modifications in-place, meaning without creating a copy of the array, ensuring efficient memory usage.
  • Intuitive Syntax: Array manipulations often have a very readable and straightforward syntax, making the code self-explanatory.

The ability to seamlessly manipulate arrays is a cornerstone of many numerical and data science tasks, making these functions indispensable.

Oppgave

Swipe to start coding

Manipulate arrays to change their shape and get desired elements.

  1. Extract the diagonal elements.
  2. Retrieve the elements in the second row.
  3. Extract the 2nd and 3rd rows and change their shape to (5, 2).

Løsning

Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

Seksjon 2. Kapittel 2
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book
Challenge 2: Array Manipulation

NumPy offers a comprehensive set of tools for modifying and reshaping arrays. The key benefits include:

  • Diverse Capabilities: From reshaping to splitting, NumPy offers a wide range of functions to modify arrays as per requirements.
  • In-place Operations: Many NumPy functions can perform modifications in-place, meaning without creating a copy of the array, ensuring efficient memory usage.
  • Intuitive Syntax: Array manipulations often have a very readable and straightforward syntax, making the code self-explanatory.

The ability to seamlessly manipulate arrays is a cornerstone of many numerical and data science tasks, making these functions indispensable.

Oppgave

Swipe to start coding

Manipulate arrays to change their shape and get desired elements.

  1. Extract the diagonal elements.
  2. Retrieve the elements in the second row.
  3. Extract the 2nd and 3rd rows and change their shape to (5, 2).

Løsning

Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

Seksjon 2. Kapittel 2
Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
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