Data Science Interview Challenge

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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?

Sélectionnez la réponse correcte

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?

Sélectionnez la réponse correcte

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?

Sélectionnez la réponse correcte

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?

Sélectionnez la réponse correcte

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?

Sélectionnez la réponse correcte

df.check()

df.isnull().sum()

df.na_count()

df.observe()

What does the following code return: df.dtypes?

Sélectionnez la réponse correcte

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?

Sélectionnez la réponse correcte

df.dropna()

df.delete_duplicates()

df.remove_duplicates()

df.drop_duplicates()

How can you transpose a DataFrame?

Sélectionnez la réponse correcte

df.T

df.transpose = True

df.rotate()

df.flip()

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

Sélectionnez la réponse correcte

df.join()

df.bind()

df.concat()

df.union()

What does df.head(3) return?

Sélectionnez la réponse correcte

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

Tout était clair ?

Comment pouvons-nous l'améliorer ?

Merci pour vos commentaires !

Section 3. Chapitre 6

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