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 3: Dictionary Manipulation

Dictionaries in Python offer a structured and flexible way to store data. The strengths of dictionary manipulation include:

Key-Value Pairs: Dictionaries store data as key-value pairs, allowing rapid data retrieval by just knowing the key. This can drastically improve performance in look-up operations.
Dynamic and Flexible: Dictionaries in Python can grow and shrink dynamically and can even contain mixed data types, offering great flexibility.
Data Structuring: They provide a way to organize data hierarchically and can be nested, making them perfect for complex data representation.

In essence, dictionary manipulation ensures efficient and organized data storage and retrieval.

Task

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Given a dictionary of items and their prices, write a Python function to return items with prices greater than a given value.

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

Everything was clear?

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Section 1. Chapter 4

Challenge 3: Dictionary Manipulation

Dictionaries in Python offer a structured and flexible way to store data. The strengths of dictionary manipulation include:

Key-Value Pairs: Dictionaries store data as key-value pairs, allowing rapid data retrieval by just knowing the key. This can drastically improve performance in look-up operations.
Dynamic and Flexible: Dictionaries in Python can grow and shrink dynamically and can even contain mixed data types, offering great flexibility.
Data Structuring: They provide a way to organize data hierarchically and can be nested, making them perfect for complex data representation.

In essence, dictionary manipulation ensures efficient and organized data storage and retrieval.

Task

Swipe to show code editor

Given a dictionary of items and their prices, write a Python function to return items with prices greater than a given value.

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 4

Challenge 3: Dictionary Manipulation

Dictionaries in Python offer a structured and flexible way to store data. The strengths of dictionary manipulation include:

Key-Value Pairs: Dictionaries store data as key-value pairs, allowing rapid data retrieval by just knowing the key. This can drastically improve performance in look-up operations.
Dynamic and Flexible: Dictionaries in Python can grow and shrink dynamically and can even contain mixed data types, offering great flexibility.
Data Structuring: They provide a way to organize data hierarchically and can be nested, making them perfect for complex data representation.

In essence, dictionary manipulation ensures efficient and organized data storage and retrieval.

Task

Swipe to show code editor

Given a dictionary of items and their prices, write a Python function to return items with prices greater than a given value.

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

Everything was clear?

Thanks for your feedback!

Dictionaries in Python offer a structured and flexible way to store data. The strengths of dictionary manipulation include:

Key-Value Pairs: Dictionaries store data as key-value pairs, allowing rapid data retrieval by just knowing the key. This can drastically improve performance in look-up operations.
Dynamic and Flexible: Dictionaries in Python can grow and shrink dynamically and can even contain mixed data types, offering great flexibility.
Data Structuring: They provide a way to organize data hierarchically and can be nested, making them perfect for complex data representation.

In essence, dictionary manipulation ensures efficient and organized data storage and retrieval.

Task

Swipe to show code editor

Given a dictionary of items and their prices, write a Python function to return items with prices greater than a given value.

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

Section 1. Chapter 4

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