Data Science Interview Challenge

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Data Science Interview Challenge

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

Theoretical Questions

1. What will the following code output:

2. What does the following code do:

3. How can you extract all items from a NumPy array that are greater than 5?

4. What is the result of the following:

5. If `arr` is a 1-dimensional NumPy array, what will `arr[::2]` do?

6. Which of the following will raise an error?

7. How do you calculate the dot product of two arrays `a` and `b`?

8. How do you get the unique values from a NumPy array `arr`?

9. Which method can be used to find the indices of non-zero elements in a NumPy array?

What will the following code output:

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<class 'tuple'>

<class 'list'>

<class 'numpy.array'>

<class 'numpy.ndarray'>

What does the following code do:

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Creates an array [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

Creates an array [0, 2, 4, 6, 8]

Creates an array [2, 4, 6, 8]

Raises an error

How can you extract all items from a NumPy array that are greater than 5?

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numpy.extract(array > 5)

array[array > 5]

numpy.pull(array, > 5)

numpy.get(array, ">", 5)

What is the result of the following:

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array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

array([ 0., 2.5, 5., 7.5, 10.])

array([ 0., 2., 4., 6., 8., 10.])

array([0, 5, 10])

If arr is a 1-dimensional NumPy array, what will arr[::2] do?

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Get the first two elements

Duplicate the array

Select every second element from the array

Split the array into 2 equal parts

Which of the following will raise an error?

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numpy.array([1, 2, 3], dtype='float64')

numpy.full((3, 3), 7)

numpy.zeros(3, 3)

numpy.ones_like([1, 2, 3])

How do you calculate the dot product of two arrays a and b?

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numpy.dot(a, b)

numpy.multiply(a, b)

numpy.prod(a, b)

a.dot(b)

How do you get the unique values from a NumPy array arr?

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numpy.get(arr)

arr.values()

numpy.unique(arr)

numpy.nunique(arr)

Which method can be used to find the indices of non-zero elements in a NumPy array?

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numpy.index()

numpy.nonzero()

numpy.find()

numpy.search()

War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 2. Kapitel 6

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