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 5: Iterating Over Data

Iterating over datasets in Pandas is a critical operation, especially when custom data processing steps need to be applied to each row or column. Pandas offers:

Flexibility: Whether you need to process data row-wise, column-wise, or cell-wise, Pandas has you covered with multiple methods.
Efficiency: While it's typically more efficient to use vectorized Pandas operations, sometimes iteration is the most straightforward approach.

Understanding how to iterate effectively over datasets in Pandas can greatly aid in the data cleaning and pre-processing phase.

Task

Discover different ways to iterate over datasets in Pandas:

Iterate over rows of a DataFrame.
Iterate over column names of a DataFrame.
Apply a custom function to each cell in a DataFrame column.
Use the map function to format the entire DataFrame.

Task

Discover different ways to iterate over datasets in Pandas:

Iterate over rows of a DataFrame.
Iterate over column names of a DataFrame.
Apply a custom function to each cell in a DataFrame column.
Use the map function to format the entire DataFrame.

Everything was clear?

Section 3. Chapter 5

Challenge 5: Iterating Over Data

Iterating over datasets in Pandas is a critical operation, especially when custom data processing steps need to be applied to each row or column. Pandas offers:

Flexibility: Whether you need to process data row-wise, column-wise, or cell-wise, Pandas has you covered with multiple methods.
Efficiency: While it's typically more efficient to use vectorized Pandas operations, sometimes iteration is the most straightforward approach.

Understanding how to iterate effectively over datasets in Pandas can greatly aid in the data cleaning and pre-processing phase.

Task

Discover different ways to iterate over datasets in Pandas:

Iterate over rows of a DataFrame.
Iterate over column names of a DataFrame.
Apply a custom function to each cell in a DataFrame column.
Use the map function to format the entire DataFrame.

Task

Discover different ways to iterate over datasets in Pandas:

Iterate over rows of a DataFrame.
Iterate over column names of a DataFrame.
Apply a custom function to each cell in a DataFrame column.
Use the map function to format the entire DataFrame.

Everything was clear?

Section 3. Chapter 5

Challenge 5: Iterating Over Data

Iterating over datasets in Pandas is a critical operation, especially when custom data processing steps need to be applied to each row or column. Pandas offers:

Flexibility: Whether you need to process data row-wise, column-wise, or cell-wise, Pandas has you covered with multiple methods.
Efficiency: While it's typically more efficient to use vectorized Pandas operations, sometimes iteration is the most straightforward approach.

Understanding how to iterate effectively over datasets in Pandas can greatly aid in the data cleaning and pre-processing phase.

Task

Discover different ways to iterate over datasets in Pandas:

Iterate over rows of a DataFrame.
Iterate over column names of a DataFrame.
Apply a custom function to each cell in a DataFrame column.
Use the map function to format the entire DataFrame.

Task

Discover different ways to iterate over datasets in Pandas:

Iterate over rows of a DataFrame.
Iterate over column names of a DataFrame.
Apply a custom function to each cell in a DataFrame column.
Use the map function to format the entire DataFrame.

Everything was clear?

Iterating over datasets in Pandas is a critical operation, especially when custom data processing steps need to be applied to each row or column. Pandas offers:

Flexibility: Whether you need to process data row-wise, column-wise, or cell-wise, Pandas has you covered with multiple methods.
Efficiency: While it's typically more efficient to use vectorized Pandas operations, sometimes iteration is the most straightforward approach.

Understanding how to iterate effectively over datasets in Pandas can greatly aid in the data cleaning and pre-processing phase.

Task

Discover different ways to iterate over datasets in Pandas:

Iterate over rows of a DataFrame.
Iterate over column names of a DataFrame.
Apply a custom function to each cell in a DataFrame column.
Use the map function to format the entire DataFrame.

Section 3. Chapter 5

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