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 4: Nested Loops

Nested loops in Python are a cornerstone for advanced iteration over data sets. Their importance lies in:

Multi-dimensional Data Handling: They allow for iterating over complex data structures like matrices or multi-dimensional lists, enabling deep data traversal.
Combination and Permutation: Nested loops are the go-to solution when you need to evaluate all possible combinations or permutations of multiple lists.
Granular Control: They offer fine-tuned control over iteration, allowing programmers to dictate the exact flow and conditions of looping.

Using nested loops, developers can harness the power of iteration to its fullest potential.

Task

The goal is to generate a matrix (a list of lists) where the outer list contains n lists and each inner list contains m integers. These integers should be in ascending order starting from 1.

Set up for loop.
Set up while loop.
Append inner row.
Increase counter.
Increase row index.
Append matrix.

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 1. Chapter 5

Challenge 4: Nested Loops

Nested loops in Python are a cornerstone for advanced iteration over data sets. Their importance lies in:

Multi-dimensional Data Handling: They allow for iterating over complex data structures like matrices or multi-dimensional lists, enabling deep data traversal.
Combination and Permutation: Nested loops are the go-to solution when you need to evaluate all possible combinations or permutations of multiple lists.
Granular Control: They offer fine-tuned control over iteration, allowing programmers to dictate the exact flow and conditions of looping.

Using nested loops, developers can harness the power of iteration to its fullest potential.

Task

The goal is to generate a matrix (a list of lists) where the outer list contains n lists and each inner list contains m integers. These integers should be in ascending order starting from 1.

Set up for loop.
Set up while loop.
Append inner row.
Increase counter.
Increase row index.
Append matrix.

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 1. Chapter 5

Challenge 4: Nested Loops

Nested loops in Python are a cornerstone for advanced iteration over data sets. Their importance lies in:

Multi-dimensional Data Handling: They allow for iterating over complex data structures like matrices or multi-dimensional lists, enabling deep data traversal.
Combination and Permutation: Nested loops are the go-to solution when you need to evaluate all possible combinations or permutations of multiple lists.
Granular Control: They offer fine-tuned control over iteration, allowing programmers to dictate the exact flow and conditions of looping.

Using nested loops, developers can harness the power of iteration to its fullest potential.

Task

The goal is to generate a matrix (a list of lists) where the outer list contains n lists and each inner list contains m integers. These integers should be in ascending order starting from 1.

Set up for loop.
Set up while loop.
Append inner row.
Increase counter.
Increase row index.
Append matrix.

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

Everything was clear?

Thanks for your feedback!

Nested loops in Python are a cornerstone for advanced iteration over data sets. Their importance lies in:

Multi-dimensional Data Handling: They allow for iterating over complex data structures like matrices or multi-dimensional lists, enabling deep data traversal.
Combination and Permutation: Nested loops are the go-to solution when you need to evaluate all possible combinations or permutations of multiple lists.
Granular Control: They offer fine-tuned control over iteration, allowing programmers to dictate the exact flow and conditions of looping.

Using nested loops, developers can harness the power of iteration to its fullest potential.

Task

The goal is to generate a matrix (a list of lists) where the outer list contains n lists and each inner list contains m integers. These integers should be in ascending order starting from 1.

Set up for loop.
Set up while loop.
Append inner row.
Increase counter.
Increase row index.
Append matrix.

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

Section 1. Chapter 5

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