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. How can you filter rows in a DataFrame `df` where column 'A' values are greater than 10?

2. What does the `df.describe()` method return?

3. If `series` is a Pandas Series, what will `series.value_counts()` do?

4. Which of the following methods is used to concatenate two DataFrames, `df1` and `df2`?

5. Which method is used to check the number of `None` observations across columns?

6. What does the following code return: `df.dtypes`?

7. Which method is used to drop duplicates from a DataFrame?

8. How can you transpose a DataFrame?

9. Which Pandas method is used to combine two DataFrames side by side?

10. What does `df.head(3)` return?

How can you filter rows in a DataFrame df where column 'A' values are greater than 10?

Select the correct answer

df.get(df['A'] > 10)

df[df['A'] > 10]

df.where('A' > 10)

df.find(df['A'] > 10)

What does the df.describe() method return?

Select the correct answer

Summary statistics of the DataFrame

The top 5 rows of the DataFrame

The data types of columns in the DataFrame

A plot describing the DataFrame

If series is a Pandas Series, what will series.value_counts() do?

Select the correct answer

Return the frequency of each unique value in the series

Sort the series based on values

Count the total number of values in the series

Return unique values in the series

Which of the following methods is used to concatenate two DataFrames, df1 and df2?

Select the correct answer

pd.append(df1, df2)

df1.concat(df2)

pd.concat([df1, df2])

df1.combine(df2)

Which method is used to check the number of None observations across columns?

Select the correct answer

df.check()

df.isnull().sum()

df.na_count()

df.observe()

What does the following code return: df.dtypes?

Select the correct answer

The first five rows of the DataFrame

The last five rows of the DataFrame

A boolean mask of the DataFrame based on null values

The data types of each column

Which method is used to drop duplicates from a DataFrame?

Select the correct answer

df.dropna()

df.delete_duplicates()

df.remove_duplicates()

df.drop_duplicates()

How can you transpose a DataFrame?

Select the correct answer

df.T

df.transpose = True

df.rotate()

df.flip()

Which Pandas method is used to combine two DataFrames side by side?

Select the correct answer

df.join()

df.bind()

df.concat()

df.union()

What does df.head(3) return?

Select the correct answer

The first three rows of df

The last three rows of df

A random sample of three rows from df

The three most frequent values from df

Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 3. Chapter 6

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