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

Data Science Interview Challenge

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.

If you want to learn more about this topic or review your knowledge, we recommend taking the Visualization in Python with matplotlib and the Ultimate Visualization with Python courses.

Task

Plot three foundational graph types using Matplotlib:

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

Code Description

plt.plot(x, y)

The plt.plot() function plots y against x as lines. It is commonly used for displaying a series or trajectory in a two-dimensional space.

plt.scatter(x_scatter, y_scatter)

The plt.scatter() function creates a scatter plot which displays values for two variables for a set of data. The dots in a scatter plot not only report what values occur together, but also show how often they do.

plt.hist(data, bins=30)

The plt.hist() function computes and draws the histogram of the input data, offering insights into the distribution. The parameter `bins` denotes the number of bins (intervals) to be used for the histogram.

Everything was clear?

Section 4. Chapter 1