Data Science Interview Challenge

Data Science Interview Challenge

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1. Python

Let's take a look at what we'll be working with in this course. The first section will acquaint you with Python, a flexible and advanced programming language known for its clear syntax and readability.

Challenge 1: List Comprehension

Challenge 2: String Manipulation

Challenge 3: Dictionary Manipulation

Challenge 4: Nested Loops

Challenge 5: Classes

Theoretical Questions

2. NumPy

NumPy is a fundamental library in Python that facilitates efficient numerical computations with powerful n-dimensional arrays and mathematical functions.

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

Pandas provides intuitive and versatile data structures for efficient data manipulation and analysis, streamlining the initial stages of the data science pipeline.

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

Matplotlib is a comprehensive Python library for creating static, animated, and interactive visualizations in Python.

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

Seaborn is a Python data visualization library based on Matplotlib that provides a high-level interface for creating informative and attractive statistical graphics.

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

Statistics provides data scientists with foundational techniques and tools to extract meaningful insights from data, allowing them to make informed decisions and predictions based on empirical evidence.

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

Scikit-learn is an open-source Python library that provides simple and efficient tools for data analysis and modeling, particularly for machine learning. Data scientists use it extensively for its comprehensive collection of algorithms and processing techniques, enabling them to quickly develop and deploy predictive models.

Challenge 1: Data Scaling

Challenge 2: Basic Model Creation

Challenge 3: Pipelines

Challenge 4: Cross-validation

Challenge 5: Hyperparameter Tuning

Theoretical Questions

Course Content

Data Science Interview Challenge

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

Theoretical Questions

1. What is the output of the following code?

2. In which of the following scenarios would you use the `pass` statement?

3. What is the output of the following code?

4. Which function is used to read input from the user in Python 3?

5. How can you get the length of the list `a`?

6. What will the code display?

7. Which of the following is **NOT** a valid variable name in Python?

8. Which of the following will raise an exception?

9. What will the following code return?

10. Which of the following methods will **NOT** alter the original list but will return a new sorted list?

What is the output of the following code?

Select the correct answer

<class 'tuple'>

<class 'list'>

<class 'dict'>

NoneType

In which of the following scenarios would you use the pass statement?

Select the correct answer

When you want to exit a loop.

When you want an empty block that does nothing.

When you want to pass a function as a parameter.

When you want to pass a value to a function.

What is the output of the following code?

Select the correct answer

[3, 4, 5, 6, 7]

[3, 5, 7]

[3, 5, 7, 9]

[5, 7]

Which function is used to read input from the user in Python 3?

Select the correct answer

get()

inputString()

input()

read()

How can you get the length of the list a?

Select the correct answer

len.list(a)

a.len()

len(a)

length(a)

What will the code display?

Select the correct answer

False

True

None

TypeError

Which of the following is NOT a valid variable name in Python?

Select the correct answer

_my_var

my_var2

VAR

2my_var

Which of the following will raise an exception?

Select the correct answer

print("Python"[10:])

''.join(['P', 'y', 't', 'h', 'o', 'n'])

print("2" + "3")

print(int("2.0") + int("3"))

What will the following code return?

Select the correct answer

15

21

12

18

Which of the following methods will NOT alter the original list but will return a new sorted list?

Select the correct answer

list.sort()

sorted(list)

list.sorted()

sort(list)

Everything was clear?

Section 1. Chapter 7

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