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 2: Array Manipulation

NumPy offers a comprehensive set of tools for modifying and reshaping arrays. The key benefits include:

Diverse Capabilities: From reshaping to splitting, NumPy offers a wide range of functions to modify arrays as per requirements.
In-place Operations: Many NumPy functions can perform modifications in-place, meaning without creating a copy of the array, ensuring efficient memory usage.
Intuitive Syntax: Array manipulations often have a very readable and straightforward syntax, making the code self-explanatory.

The ability to seamlessly manipulate arrays is a cornerstone of many numerical and data science tasks, making these functions indispensable.

Task

Swipe to show code editor

Manipulate arrays to change their shape and get desired elements.

Extract the diagonal elements.
Retrieve the elements in the second row.
Extract the 2nd and 3rd rows and change their shape to (5, 2).

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 2

Challenge 2: Array Manipulation

NumPy offers a comprehensive set of tools for modifying and reshaping arrays. The key benefits include:

Diverse Capabilities: From reshaping to splitting, NumPy offers a wide range of functions to modify arrays as per requirements.
In-place Operations: Many NumPy functions can perform modifications in-place, meaning without creating a copy of the array, ensuring efficient memory usage.
Intuitive Syntax: Array manipulations often have a very readable and straightforward syntax, making the code self-explanatory.

The ability to seamlessly manipulate arrays is a cornerstone of many numerical and data science tasks, making these functions indispensable.

Task

Swipe to show code editor

Manipulate arrays to change their shape and get desired elements.

Extract the diagonal elements.
Retrieve the elements in the second row.
Extract the 2nd and 3rd rows and change their shape to (5, 2).

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 2

Challenge 2: Array Manipulation

NumPy offers a comprehensive set of tools for modifying and reshaping arrays. The key benefits include:

Diverse Capabilities: From reshaping to splitting, NumPy offers a wide range of functions to modify arrays as per requirements.
In-place Operations: Many NumPy functions can perform modifications in-place, meaning without creating a copy of the array, ensuring efficient memory usage.
Intuitive Syntax: Array manipulations often have a very readable and straightforward syntax, making the code self-explanatory.

The ability to seamlessly manipulate arrays is a cornerstone of many numerical and data science tasks, making these functions indispensable.

Task

Swipe to show code editor

Manipulate arrays to change their shape and get desired elements.

Extract the diagonal elements.
Retrieve the elements in the second row.
Extract the 2nd and 3rd rows and change their shape to (5, 2).

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

NumPy offers a comprehensive set of tools for modifying and reshaping arrays. The key benefits include:

Diverse Capabilities: From reshaping to splitting, NumPy offers a wide range of functions to modify arrays as per requirements.
In-place Operations: Many NumPy functions can perform modifications in-place, meaning without creating a copy of the array, ensuring efficient memory usage.
Intuitive Syntax: Array manipulations often have a very readable and straightforward syntax, making the code self-explanatory.

The ability to seamlessly manipulate arrays is a cornerstone of many numerical and data science tasks, making these functions indispensable.

Task

Swipe to show code editor

Manipulate arrays to change their shape and get desired elements.

Extract the diagonal elements.
Retrieve the elements in the second row.
Extract the 2nd and 3rd rows and change their shape to (5, 2).

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 2. Chapter 2

Switch to desktop for real-world practiceContinue from where you are using one of the options below