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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 1: Data Scaling

In the realm of data science and machine learning, data scaling is a critical preprocessing step. It primarily involves transforming the features (variables) of the dataset to a standard scale, ensuring that each feature has a similar scale or range. This is especially significant for algorithms that rely on distances or gradients, as it ensures that all features contribute equally to the outcome and the algorithm converges more efficiently.

Here's a demonstration of how the scaling utilities from scikit-learn modify the data distribution:

Task

In this task, you will be working with the popular Iris dataset. Your objective is to apply two types of scalers to the data and compare the resulting datasets.

  1. Use the StandardScaler class to standardize the dataset, which means transforming it to have a mean of 0 and a standard deviation of 1.
  2. Use the MinMaxScaler class to rescale the dataset. Ensure that after scaling, the feature values lie between -1 and 1.

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Section 7. Chapter 1
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bookChallenge 1: Data Scaling

In the realm of data science and machine learning, data scaling is a critical preprocessing step. It primarily involves transforming the features (variables) of the dataset to a standard scale, ensuring that each feature has a similar scale or range. This is especially significant for algorithms that rely on distances or gradients, as it ensures that all features contribute equally to the outcome and the algorithm converges more efficiently.

Here's a demonstration of how the scaling utilities from scikit-learn modify the data distribution:

Task

In this task, you will be working with the popular Iris dataset. Your objective is to apply two types of scalers to the data and compare the resulting datasets.

  1. Use the StandardScaler class to standardize the dataset, which means transforming it to have a mean of 0 and a standard deviation of 1.
  2. Use the MinMaxScaler class to rescale the dataset. Ensure that after scaling, the feature values lie between -1 and 1.

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 7. Chapter 1
toggle bottom row

bookChallenge 1: Data Scaling

In the realm of data science and machine learning, data scaling is a critical preprocessing step. It primarily involves transforming the features (variables) of the dataset to a standard scale, ensuring that each feature has a similar scale or range. This is especially significant for algorithms that rely on distances or gradients, as it ensures that all features contribute equally to the outcome and the algorithm converges more efficiently.

Here's a demonstration of how the scaling utilities from scikit-learn modify the data distribution:

Task

In this task, you will be working with the popular Iris dataset. Your objective is to apply two types of scalers to the data and compare the resulting datasets.

  1. Use the StandardScaler class to standardize the dataset, which means transforming it to have a mean of 0 and a standard deviation of 1.
  2. Use the MinMaxScaler class to rescale the dataset. Ensure that after scaling, the feature values lie between -1 and 1.

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!

In the realm of data science and machine learning, data scaling is a critical preprocessing step. It primarily involves transforming the features (variables) of the dataset to a standard scale, ensuring that each feature has a similar scale or range. This is especially significant for algorithms that rely on distances or gradients, as it ensures that all features contribute equally to the outcome and the algorithm converges more efficiently.

Here's a demonstration of how the scaling utilities from scikit-learn modify the data distribution:

Task

In this task, you will be working with the popular Iris dataset. Your objective is to apply two types of scalers to the data and compare the resulting datasets.

  1. Use the StandardScaler class to standardize the dataset, which means transforming it to have a mean of 0 and a standard deviation of 1.
  2. Use the MinMaxScaler class to rescale the dataset. Ensure that after scaling, the feature values lie between -1 and 1.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 7. Chapter 1
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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