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 1: Fundamentals of Plotting

Matplotlib, a cornerstone visualization library in Python, provides a vast array of plotting capabilities that are both expressive and efficient. Some compelling advantages of using Matplotlib for your data visualization tasks are:

Versatility: Matplotlib supports a diverse range of plots, from basic line plots to more complex visualizations like contour plots.
Customization: Every aspect of a plot, from its colors to its labels, can be tailored, providing full control to the user.
Integration: It works seamlessly with other libraries, especially Pandas and NumPy, making it a fundamental tool in the data analysis workflow.

For budding data scientists, analysts, or anyone keen on visual representation, Matplotlib's plotting functions act as a bridge between raw data and insights.

Task

Plot three foundational graph types using Matplotlib:

Plot a simple line graph.
Create a scatter plot.
Generate a histogram.

Task

Plot three foundational graph types using Matplotlib:

Plot a simple line graph.
Create a scatter plot.
Generate a histogram.

Everything was clear?

Section 4. Chapter 1

Challenge 1: Fundamentals of Plotting

Versatility: Matplotlib supports a diverse range of plots, from basic line plots to more complex visualizations like contour plots.
Customization: Every aspect of a plot, from its colors to its labels, can be tailored, providing full control to the user.
Integration: It works seamlessly with other libraries, especially Pandas and NumPy, making it a fundamental tool in the data analysis workflow.

For budding data scientists, analysts, or anyone keen on visual representation, Matplotlib's plotting functions act as a bridge between raw data and insights.

Task

Plot three foundational graph types using Matplotlib:

Plot a simple line graph.
Create a scatter plot.
Generate a histogram.

Task

Plot three foundational graph types using Matplotlib:

Plot a simple line graph.
Create a scatter plot.
Generate a histogram.

Everything was clear?

Section 4. Chapter 1

Challenge 1: Fundamentals of Plotting

Versatility: Matplotlib supports a diverse range of plots, from basic line plots to more complex visualizations like contour plots.
Customization: Every aspect of a plot, from its colors to its labels, can be tailored, providing full control to the user.
Integration: It works seamlessly with other libraries, especially Pandas and NumPy, making it a fundamental tool in the data analysis workflow.

For budding data scientists, analysts, or anyone keen on visual representation, Matplotlib's plotting functions act as a bridge between raw data and insights.

Task

Plot three foundational graph types using Matplotlib:

Plot a simple line graph.
Create a scatter plot.
Generate a histogram.

Task

Plot three foundational graph types using Matplotlib:

Plot a simple line graph.
Create a scatter plot.
Generate a histogram.

Everything was clear?

Versatility: Matplotlib supports a diverse range of plots, from basic line plots to more complex visualizations like contour plots.
Customization: Every aspect of a plot, from its colors to its labels, can be tailored, providing full control to the user.
Integration: It works seamlessly with other libraries, especially Pandas and NumPy, making it a fundamental tool in the data analysis workflow.

For budding data scientists, analysts, or anyone keen on visual representation, Matplotlib's plotting functions act as a bridge between raw data and insights.

Task

Plot three foundational graph types using Matplotlib:

Plot a simple line graph.
Create a scatter plot.
Generate a histogram.

Section 4. Chapter 1

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