Data Science Interview Challenge

Data Science Interview Challenge

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1. Python

Let's take a look at what we'll be working with in this course. The first section will acquaint you with Python, a flexible and advanced programming language known for its clear syntax and readability.

Challenge 1: List Comprehension

Challenge 2: String Manipulation

Challenge 3: Dictionary Manipulation

Challenge 4: Nested Loops

Challenge 5: Classes

Theoretical Questions

2. NumPy

NumPy is a fundamental library in Python that facilitates efficient numerical computations with powerful n-dimensional arrays and mathematical functions.

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

Pandas provides intuitive and versatile data structures for efficient data manipulation and analysis, streamlining the initial stages of the data science pipeline.

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

Matplotlib is a comprehensive Python library for creating static, animated, and interactive visualizations in Python.

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

Seaborn is a Python data visualization library based on Matplotlib that provides a high-level interface for creating informative and attractive statistical graphics.

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

Statistics provides data scientists with foundational techniques and tools to extract meaningful insights from data, allowing them to make informed decisions and predictions based on empirical evidence.

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

Scikit-learn is an open-source Python library that provides simple and efficient tools for data analysis and modeling, particularly for machine learning. Data scientists use it extensively for its comprehensive collection of algorithms and processing techniques, enabling them to quickly develop and deploy predictive models.

Challenge 1: Data Scaling

Challenge 2: Basic Model Creation

Challenge 3: Pipelines

Challenge 4: Cross-validation

Challenge 5: Hyperparameter Tuning

Theoretical Questions

Course Content

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:

Select the correct answer

<class 'tuple'>

<class 'list'>

<class 'numpy.array'>

<class 'numpy.ndarray'>

What does the following code do:

Select the correct answer

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?

Select the correct answer

numpy.extract(array > 5)

array[array > 5]

numpy.pull(array, > 5)

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

What is the result of the following:

Select the correct answer

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?

Select the correct answer

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?

Select the correct answer

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?

Select a few correct answers

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?

Select the correct answer

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?

Select the correct answer

numpy.index()

numpy.nonzero()

numpy.find()

numpy.search()

Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 6

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