GrokkingDataScientist.tar.gz

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

No need to waste hours and hours on browsing from one article to the next and piecing together the info you need to grasp important topics. No need to get overwhelmed by the information overload. Find easy to follow, hands-on, and fun explanations of all the essential topics in one place so you can quickly and efficiently learn what you need to thrive as a data scientist.

"Is this course right for me?" Continue to read to decide for yourself!

- "I want to understand this data science concept. Let me Google it". Then after hours of surfing, reading random articles, and invoking the heavens, you are more confused than before.
- "Data science is the sexiest and highest paying job of the 21st century. I want to become a data scientist too".
- "I have a basic knowledge of Python, willingness to learn, and commitment to become a great data scientist."

Is that you? If yes, you are at the right place.

Grokking Data Science

## Visualization Techniques

### 4. Bar Plots
Bar plots are an effective visualization technique when we have categorical data that consists of various categories or groups. 

For example, we can use bar plots to view test scores categorized by gender. These plots allow us to easily see the difference between categories (gender) because the size of the bar varies with the magnitude of the represented variable (score), and categories are clearly divided and color-coded. 

However, there is a catch. Bar plots work well when we are dealing with a few categories. If there are too many categories though, the bars can get very cluttered and the plots can quickly become hard to understand. 

We can have several variations of bar plots. The image below shows three different types of bar plots: regular, grouped, and stacked: 



# Visualization Techniques

## 4. Bar Plots
Bar plots are an effective visualization technique when we have categorical data that consists of various categories or groups. 

For example, we can use bar plots to view test scores categorized by gender. These plots allow us to easily see the difference between categories (gender) because the size of the bar varies with the magnitude of the represented variable (score), and categories are clearly divided and color-coded. 

However, there is a catch. Bar plots work well when we are dealing with a few categories. If there are too many categories though, the bars can get very cluttered and the plots can quickly become hard to understand. 

We can have several variations of bar plots. The image below shows three different types of bar plots: regular, grouped, and stacked: 



Python Fundamentals for Data Science

The Fundamentals of Statistics

Machine Learning 101

End-to-End Machine Learning Project

The Real Talk

Data Visualization Techniques - Bar and Box Plot

Visualization Techniques

4. Bar Plots