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

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

Task

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?

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

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

Task

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?

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

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

Task

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?

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

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