Course: Python Statistical Plots

$89.00
$107.69 incl. vat

duration: 5 hours |

Language: English (US) |

access duration: 90 days |

Details

The wealth of Python data visualization libraries makes it hard to decide the best choice for each use case. However, if you're looking for statistical plots that are easy to build and visually appealing, Seaborn is the obvious choice. You'll begin this course by using Seaborn to construct simple univariate histograms and use kernel density estimation, or KDE, to visualize the probability distribution of your data. In addition, you’ll learn to use strip plots and swarm plots and recognise how they work together using low-intensity noise. Finally, you'll work with time series data through various techniques, like resampling data at different time frequencies and plotting with confidence intervals and other types of error bars

Result

After completing this course, you’ll be ready to use Seaborn to visualize data, analyze data, work with time series data and make regression analyses.

Prerequisites

No formal prerequisites. However, some prior knowledge about the topic is highly recommended.

Target audience

Software Developer, Data analist

Content

Python Statistical Plots

5 hours

Python Statistical Plots: Visualizing & Analyzing Data Using Seaborn

The wealth of Python data visualization libraries makes it hard to decide the best choice for each use case. However, if you're looking for statistical plots that are easy to build and visually appealing, Seaborn is the obvious choice. You'll begin this course by using Seaborn to construct simple univariate histograms and use kernel density estimation, or KDE, to visualize the probability distribution of your data. You'll then work with bivariate histograms and KDE curves. Next, you'll use box plots to concisely represent the median and the inter-quartile range (IQR) and define outliers in data. You'll work with boxen plots, which are conceptually similar to box plots but employ percentile markers rather than whiskers. Finally, you'll use Violin plots to represent the entire probability density function, obtained via a KDE estimation, for your data.

Python Statistical Plots: Time Series Data & Regression Analysis in Seaborn

Seaborn's smartly designed interface lets you illuminate data through aesthetically pleasing statistical graphics that are incredibly easy to build. In this course, you'll discover Seaborn's capabilities. You'll begin using strip plots and swarm plots and recognizing how they work together using low-intensity noise. You'll then work with time series data through various techniques, like resampling data at different time frequencies and plotting with confidence intervals and other types of error bars. Next, you'll visualize both logistic and linear regression curves. Moving on, you'll use the pairplot function to visualize the relationships between columns in your data, taken two at a time, in a grid format. You'll change the chart type being visualized and create pair plots with multiple chart types in each plot. Lastly, you'll create and format a heatmap of a correlation matrix to identify relationships between dataset columns.

Course options

We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.

Optional practice exam (trial exam)

To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam. 

Optional practice lab

To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediatelyThe lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience. 

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