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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 1: Fundamentals of Plotting

Matplotlib, a cornerstone visualization library in Python, provides a vast array of plotting capabilities that are both expressive and efficient. Some compelling advantages of using Matplotlib for your data visualization tasks are:

  • Versatility: Matplotlib supports a diverse range of plots, from basic line plots to more complex visualizations like contour plots.
  • Customization: Every aspect of a plot, from its colors to its labels, can be tailored, providing full control to the user.
  • Integration: It works seamlessly with other libraries, especially Pandas and NumPy, making it a fundamental tool in the data analysis workflow.

For budding data scientists, analysts, or anyone keen on visual representation, Matplotlib's plotting functions act as a bridge between raw data and insights.

Oppgave

Swipe to start coding

Plot three foundational graph types using Matplotlib:

  1. Plot a simple line graph.
  2. Create a scatter plot.
  3. Generate a histogram.

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 4. Kapittel 1
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book
Challenge 1: Fundamentals of Plotting

Matplotlib, a cornerstone visualization library in Python, provides a vast array of plotting capabilities that are both expressive and efficient. Some compelling advantages of using Matplotlib for your data visualization tasks are:

  • Versatility: Matplotlib supports a diverse range of plots, from basic line plots to more complex visualizations like contour plots.
  • Customization: Every aspect of a plot, from its colors to its labels, can be tailored, providing full control to the user.
  • Integration: It works seamlessly with other libraries, especially Pandas and NumPy, making it a fundamental tool in the data analysis workflow.

For budding data scientists, analysts, or anyone keen on visual representation, Matplotlib's plotting functions act as a bridge between raw data and insights.

Oppgave

Swipe to start coding

Plot three foundational graph types using Matplotlib:

  1. Plot a simple line graph.
  2. Create a scatter plot.
  3. Generate a histogram.

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 4. Kapittel 1
Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
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