Зміст курсу

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 1: List Comprehension

List comprehensions in Python provide an elegant and concise way to create lists. Their advantages are:

Readability and conciseness: List comprehensions allow you to reduce the amount of code, making it more readable and concise. Instead of using multiple lines of code with loops and conditional statements, you can get the same result in one line.
Performance: In many cases, list comprehensions are faster than traditional loops, especially when processing large amounts of data, which is key in Data Science.
Built-in filtering capabilities: These allow you to easily apply conditional expressions to filter data, which is especially useful when preprocessing and cleaning datasets.

Thus, list comprehensions are a powerful tool in the hands of a data scientist, allowing you to quickly and efficiently process and transform data.

Завдання

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Given a list of numbers, write a Python function to square all even numbers in the list. List [1, 2, 3, 4, 5] should result into [4, 16].

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Секція 1. Розділ 2

Challenge 1: List Comprehension

List comprehensions in Python provide an elegant and concise way to create lists. Their advantages are:

Readability and conciseness: List comprehensions allow you to reduce the amount of code, making it more readable and concise. Instead of using multiple lines of code with loops and conditional statements, you can get the same result in one line.
Performance: In many cases, list comprehensions are faster than traditional loops, especially when processing large amounts of data, which is key in Data Science.
Built-in filtering capabilities: These allow you to easily apply conditional expressions to filter data, which is especially useful when preprocessing and cleaning datasets.

Thus, list comprehensions are a powerful tool in the hands of a data scientist, allowing you to quickly and efficiently process and transform data.

Завдання

Swipe to show code editor

Given a list of numbers, write a Python function to square all even numbers in the list. List [1, 2, 3, 4, 5] should result into [4, 16].

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 1. Розділ 2

Challenge 1: List Comprehension

List comprehensions in Python provide an elegant and concise way to create lists. Their advantages are:

Readability and conciseness: List comprehensions allow you to reduce the amount of code, making it more readable and concise. Instead of using multiple lines of code with loops and conditional statements, you can get the same result in one line.
Performance: In many cases, list comprehensions are faster than traditional loops, especially when processing large amounts of data, which is key in Data Science.
Built-in filtering capabilities: These allow you to easily apply conditional expressions to filter data, which is especially useful when preprocessing and cleaning datasets.

Thus, list comprehensions are a powerful tool in the hands of a data scientist, allowing you to quickly and efficiently process and transform data.

Завдання

Swipe to show code editor

Given a list of numbers, write a Python function to square all even numbers in the list. List [1, 2, 3, 4, 5] should result into [4, 16].

Все було зрозуміло?

Дякуємо за ваш відгук!

List comprehensions in Python provide an elegant and concise way to create lists. Their advantages are:

Readability and conciseness: List comprehensions allow you to reduce the amount of code, making it more readable and concise. Instead of using multiple lines of code with loops and conditional statements, you can get the same result in one line.
Performance: In many cases, list comprehensions are faster than traditional loops, especially when processing large amounts of data, which is key in Data Science.
Built-in filtering capabilities: These allow you to easily apply conditional expressions to filter data, which is especially useful when preprocessing and cleaning datasets.

Thus, list comprehensions are a powerful tool in the hands of a data scientist, allowing you to quickly and efficiently process and transform data.

Завдання

Swipe to show code editor

Given a list of numbers, write a Python function to square all even numbers in the list. List [1, 2, 3, 4, 5] should result into [4, 16].

Секція 1. Розділ 2