Course Content

Data Science Interview Challenge

1. Python

Introduction Challenge 1: List Comprehension Challenge 2: String Manipulation Challenge 3: Dictionary Manipulation Challenge 4: Nested Loops Challenge 5: Classes Theoretical Questions

2. NumPy

Challenge 1: Array Creation Challenge 2: Array Manipulation Challenge 3: Statistical Insights Challenge 4: Handling Missing Values Challenge 5: Subarray Sorting Theoretical Questions

3. Pandas

Challenge 1: DataFrame Creation Challenge 2: Data Grouping Challenge 3: Indexing and MultiIndexing Challenge 4: Altering DataFrame Challenge 5: Iterating Over Data Theoretical Questions

4. Matplotlib

Challenge 1: Fundamentals of Plotting Challenge 2: Other Graph Types Challenge 3: Subplots Challenge 4: Customizing Plots Challenge 5: Adding Text to Plots Theoretical Questions

5. Seaborn

Challenge 1: Visualizing Distributions Challenge 2: Exploring Categorical Data Challenge 3: Relational Plots Challenge 4: Regression Plots Challenge 5: Matrix Plots Theoretical Questions

6. Statistics

Challenge 1: Probabilities and Distributions Challenge 2: Bayes' Theorem Challenge 3: Hypothesis Testing Challenge 4: Confidence Intervals Challenge 5: Correlation Theoretical Questions

7. Scikit-learn

Challenge 1: Data Scaling Challenge 2: Basic Model Creation Challenge 3: Pipelines Challenge 4: Cross-validation Challenge 5: Hyperparameter Tuning Theoretical Questions

Challenge 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.

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

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.

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

Everything was clear?

Section 7. Chapter 1

Challenge 1: Data Scaling

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.

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

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.

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

Everything was clear?

Section 7. Chapter 1

Challenge 1: Data Scaling

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.

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

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.

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

Everything was clear?

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.

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

Section 7. Chapter 1

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