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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 3: Pipelines

Pipelines play a crucial role in streamlining machine learning workflows, ensuring the coherent and efficient transition of data from one processing stage to another. Essentially, a pipeline bundles together a sequence of data processing steps and modeling into a single, unified structure. The primary advantage of using pipelines is the minimization of common workflow errors, such as data leakage when standardizing or normalizing data.

Taak

Swipe to start coding

Apply data scaling to the wine dataset, and then use the KMeans algorithm for clustering wines based on their chemical properties.

  1. Apply data standard scaling to the features of the wine dataset.
  2. Use the KMeans algorithm to cluster wines based on their chemical properties. You need 3 clusters.
  3. Apply the pipeline to the data

Oplossing

Switch to desktopSchakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties
Was alles duidelijk?

Hoe kunnen we het verbeteren?

Bedankt voor je feedback!

Sectie 7. Hoofdstuk 3
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book
Challenge 3: Pipelines

Pipelines play a crucial role in streamlining machine learning workflows, ensuring the coherent and efficient transition of data from one processing stage to another. Essentially, a pipeline bundles together a sequence of data processing steps and modeling into a single, unified structure. The primary advantage of using pipelines is the minimization of common workflow errors, such as data leakage when standardizing or normalizing data.

Taak

Swipe to start coding

Apply data scaling to the wine dataset, and then use the KMeans algorithm for clustering wines based on their chemical properties.

  1. Apply data standard scaling to the features of the wine dataset.
  2. Use the KMeans algorithm to cluster wines based on their chemical properties. You need 3 clusters.
  3. Apply the pipeline to the data

Oplossing

Switch to desktopSchakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties
Was alles duidelijk?

Hoe kunnen we het verbeteren?

Bedankt voor je feedback!

Sectie 7. Hoofdstuk 3
Switch to desktopSchakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties
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