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 4: Altering DataFrame

Pandas provides a plethora of tools that allow for easy modification of both data and structure of DataFrames. These capabilities are essential because:

Data Cleaning: Real-world datasets are often messy. The ability to transform and clean data ensures its readiness for analysis.
Versatility: Frequently, the structure of a dataset may not align with the requirements of a given task. Being able to reshape data can be a lifesaver.
Efficiency: Direct modifications to DataFrames, as opposed to creating new ones, can save memory and improve performance.

Getting familiar with the techniques to alter data and the structure of DataFrames is a key step in becoming proficient with Pandas.

Task

Harness the power of Pandas to alter data and the structure of DataFrames:

Add a new column to a DataFrame with values Engineer, Doctor and Artist.
Rename columns in a DataFrame. Change the Name column into Full Name and the Age column into Age (years).
Drop a column City from a DataFrame.
Sort a DataFrame based on the Age column (descending).

Task

Harness the power of Pandas to alter data and the structure of DataFrames:

Add a new column to a DataFrame with values Engineer, Doctor and Artist.
Rename columns in a DataFrame. Change the Name column into Full Name and the Age column into Age (years).
Drop a column City from a DataFrame.
Sort a DataFrame based on the Age column (descending).

Everything was clear?

Section 3. Chapter 4

Challenge 4: Altering DataFrame

Pandas provides a plethora of tools that allow for easy modification of both data and structure of DataFrames. These capabilities are essential because:

Data Cleaning: Real-world datasets are often messy. The ability to transform and clean data ensures its readiness for analysis.
Versatility: Frequently, the structure of a dataset may not align with the requirements of a given task. Being able to reshape data can be a lifesaver.
Efficiency: Direct modifications to DataFrames, as opposed to creating new ones, can save memory and improve performance.

Getting familiar with the techniques to alter data and the structure of DataFrames is a key step in becoming proficient with Pandas.

Task

Harness the power of Pandas to alter data and the structure of DataFrames:

Add a new column to a DataFrame with values Engineer, Doctor and Artist.
Rename columns in a DataFrame. Change the Name column into Full Name and the Age column into Age (years).
Drop a column City from a DataFrame.
Sort a DataFrame based on the Age column (descending).

Task

Harness the power of Pandas to alter data and the structure of DataFrames:

Add a new column to a DataFrame with values Engineer, Doctor and Artist.
Rename columns in a DataFrame. Change the Name column into Full Name and the Age column into Age (years).
Drop a column City from a DataFrame.
Sort a DataFrame based on the Age column (descending).

Everything was clear?

Section 3. Chapter 4

Challenge 4: Altering DataFrame

Pandas provides a plethora of tools that allow for easy modification of both data and structure of DataFrames. These capabilities are essential because:

Data Cleaning: Real-world datasets are often messy. The ability to transform and clean data ensures its readiness for analysis.
Versatility: Frequently, the structure of a dataset may not align with the requirements of a given task. Being able to reshape data can be a lifesaver.
Efficiency: Direct modifications to DataFrames, as opposed to creating new ones, can save memory and improve performance.

Getting familiar with the techniques to alter data and the structure of DataFrames is a key step in becoming proficient with Pandas.

Task

Harness the power of Pandas to alter data and the structure of DataFrames:

Add a new column to a DataFrame with values Engineer, Doctor and Artist.
Rename columns in a DataFrame. Change the Name column into Full Name and the Age column into Age (years).
Drop a column City from a DataFrame.
Sort a DataFrame based on the Age column (descending).

Task

Harness the power of Pandas to alter data and the structure of DataFrames:

Add a new column to a DataFrame with values Engineer, Doctor and Artist.
Rename columns in a DataFrame. Change the Name column into Full Name and the Age column into Age (years).
Drop a column City from a DataFrame.
Sort a DataFrame based on the Age column (descending).

Everything was clear?

Pandas provides a plethora of tools that allow for easy modification of both data and structure of DataFrames. These capabilities are essential because:

Data Cleaning: Real-world datasets are often messy. The ability to transform and clean data ensures its readiness for analysis.
Versatility: Frequently, the structure of a dataset may not align with the requirements of a given task. Being able to reshape data can be a lifesaver.
Efficiency: Direct modifications to DataFrames, as opposed to creating new ones, can save memory and improve performance.

Getting familiar with the techniques to alter data and the structure of DataFrames is a key step in becoming proficient with Pandas.

Task

Harness the power of Pandas to alter data and the structure of DataFrames:

Add a new column to a DataFrame with values Engineer, Doctor and Artist.
Rename columns in a DataFrame. Change the Name column into Full Name and the Age column into Age (years).
Drop a column City from a DataFrame.
Sort a DataFrame based on the Age column (descending).

Section 3. Chapter 4

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