In our everyday life, we are constantly surrounded by data. The text you are reading now is data. The list of phone numbers of your friends in your smartphone is data, as well as the current time displayed on your watch. As human beings, we naturally operate with data by counting the money we have or by writing letters to our friends.
| Visualizing Quantities - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
In this lesson you will explore how to use one of the many available Python libraries to learn how to create interesting visualizations all around the concept of quantity. Using a cleaned dataset about the birds of Minnesota, you can learn many interesting facts about local wildlife.
In the previous lesson, you learned some interesting facts about a dataset about the birds of Minnesota. You found some erroneous data by visualizing outliers and looked at the differences between bird categories by their maximum length.
Another way to dig into data is by looking at its distribution, or how the data is organized along an axis. Perhaps, for example, you'd like to learn about the general distribution, for this dataset, of the maximum wingspan or maximum body mass for the birds of Minnesota.
@ -10,7 +10,7 @@ This dataset of about 600 items displays honey production in many U.S. states. S
It will be interesting to visualize the relationship between a given state's production per year and, for example, the price of honey in that state. Alternately, you could visualize the relationship between states' honey yield per colony. This year span covers the devastating 'CCD' or 'Colony Collapse Disorder' first seen in 2006 (http://npic.orst.edu/envir/ccd.html), so it is a poignant dataset to study. 🐝
In this lesson, you can use Seaborn, which you have used before, as a good library to visualize relationships between variables. Particularly interesting is the use of Seaborn's `relplot` function that allows scatter plots and line plots to quickly visualize '[statistical relationships](https://seaborn.pydata.org/tutorial/relational.html?highlight=relationships)', which allow the data scientist to better understand how variables relate to each other.
Analyzing in the data lifecycle confirms that the data can answer the questions that are proposed or solving a particular problem. This step can also focus on confirming a model is correctly addressing these questions and problems. This lesson is focused on Exploratory Data Analysis or EDA, which are techniques for defining features and relationships within the data and can be used to prepare the data for modeling.
In this lesson, you will learn the fundamental principles of the Cloud, then you will see why it can be interesting for you to use Cloud services to run your data science projects and we'll look at some examples of data science projects run in the Cloud.
Lee Stott
1 day ago
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GitHub