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"A picture is worth a thousand words". We are all familiar with this expression. It is especially applicable when trying to explain the information obtained from the analysis of ever larger data sets. Data visualization plays an essential role in representing data on both a small and large scale. One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an accessible and thought-provoking way. Learn how to take advantage of a software tool to view
"A picture is worth a thousand words".
We are all familiar with this expression.
It is especially applicable when trying to explain the information obtained from the analysis of ever larger data sets.
Data visualization plays an essential role in representing data on both a small and large scale.
One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an accessible and thought-provoking way.
Learning to take advantage of a software tool to visualize data will also allow you to extract insights, better understand the data, and make more effective decisions.
The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present it in a way that makes sense to people.
Several techniques have been developed to present data visually, but in this course we will use several Python data visualization libraries, namely Matplotlib, Seaborn, and Folium.
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In this module, you will learn about data visualization and some of the best practices to keep in mind when creating charts and visuals. You will also learn about the history and architecture of Matplotlib and learn about basic plotting with Matplotlib. In addition, you will learn about the Immigration to Canada dataset, which will be used extensively throughout the course. Finally, you will briefly learn how to read csv files into a pandas dataframe and process and manipulate the data in the dataframe, and how to generate line plots using Matplotlib.
In this module, you will learn about area charts and how to create them with Matplotlib, histograms and how to create them with Matplotlib, bar charts and how to create them with Matplotlib, pie charts and how to create them with Matplotlib, box plots. and how to create them with Matplotlib, and scatter plots and bubble plots and how to create them with Matplotlib.
In this module, you will learn about advanced visualization tools like waffle charts and word clouds and how to create them. You will also learn about seaborn, which is another visualization library, and how to use it to generate attractive regression plots. Also, you will learn about Folium, which is another visualization library, specially designed for visualizing geospatial data. Finally, you'll learn how to use Folium to create maps of different regions of the world, how to overlay markers on top of a map, and how to create choropleth maps.
In this module, you'll start creating dashboards using the Plotly library. You'll create a dashboard with the topic "US Domestic Airline Flight Performance." To do this, you will use a set of Point, Plotly, and Dash performance data from a US airline learned throughout the course. Hands-on labs will follow each concept to get you comfortable using the library. The reading lists will reference additional resources to learn more about the concepts covered.
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