Gain insights into data science with easy-to-follow, hands-on explanations. Explore essential concepts quickly and efficiently, even without prior statistics knowledge, for a career boost.

GrokkingDataScientist.tar.gz

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Master the skills that can get you a $100K+ salary even if you bunked your statistics classes. 

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- "Data science is the sexiest and highest paying job of the 21st century. I want to become a data scientist too".
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Grokking Data Science

# Visualization Techniques
## 1. Scatter Plots

Scatter plots are deceptively simple and commonly used, but simple doesn't mean that they aren't useful! 

In a scatter plot, data points are represented individually with a dot, circle, or some other shape. These plots are great for showing the relationship between two variables as we can directly see the raw distribution of the data. 

To create a scatter plot in Matplotlib we can simply use the `scatter` method. Let’s see how by creating a scatter plot with randomly generated data points of many colors and sizes.

First, let's generate some random data points, *x* and *y*, and set random values for colors and sizes because we want a pretty plot:

***A complete "runnable" example is at the end.***

# Generating Random Data
x = np.random.randn(100)
y = np.random.randn(100)
colors = np.random.rand(100)
sizes = 1000 * np.random.rand(100)

Now that we have our two variables *(x, y)* and the colors and sizes of the points, we can call the scatter method like this:

````Python
plt.scatter(x, y, c=colors, s=sizes, alpha=0.2, cmap='viridis') 
````
`alpha = 0.2`: The optional alpha input parameter (optional) of the scatter method allows us to adjust the transparency level so that we can view overlapping data points. You will understand this concept better, visually, in the final output.

`cmap='viridis'`: Matplotlib provides a wide range of colormaps, an optional parameter, for creating visually appealing plots. For example, you can set `cmap=plt.cm.Blues`, `cmap=plt.cm.autumn`,  or `cmap=plt.cm.gist_earth`.

The color argument, `c`, is automatically mapped to a color scale, and the size argument, `s`, is in pixels. To view the color scale next to the plot on the right-hand side, we can use the `colobar()` command:

````Python
plt.colorbar()
````

Now let’s put it all together and run the full code to see the output. Note that we will save the output to a file to display the plot with Educative’s code widget. 

As an exercise, try changing the parameter values to understand their effect on the output.

Python Fundamentals for Data Science

The Fundamentals of Statistics

Machine Learning 101

End-to-End Machine Learning Project

The Real Talk

Data Visualization Techniques - Scatter, Line, and Histogram

Visualization Techniques

1. Scatter Plots