Unlock the power of Julia in this chapter and learn how to create stunning, professional-quality data visualizations using the Plots.jl package.

Visualizing data

Options

True

False

Why should I plot?

Visualization

Table

Should I plot?

Interpreting a plot

Getting started with Plots.jl

Installing Plots.jl

Price goes up

Price changes

Trading volume

Plotting multiple variables

Multiple line plots

Correlated ETF funds

Multiple line plots!

First steps with Plots.jl

Expand your Julia data visualization skills as you delve into advanced plot types, including histograms, bar charts, time series plots, and box and violin plots.

Visualizing distributions

Potato prices

Rajasthan normalized

Instant and powdered coffee

Stacking states

Dropping Bars

Bar chart or histogram

Price deviations

Saree prices

Visualizing time series

Them apples

(Super)fine rice

Boxes and violins

Boxing states

Price distributions

Tomato seasons

Histograms, time series and more

Master the art of data visualization customization! Discover how to wield themes, manipulate plot attributes, utilize color palettes, arrange multiple plots in grids with layouts, and create your series recipes. With these skills, you'll have the power to craft stunning and highly tailored visualizations to convey impactful insights.

Customizing with themes

Previewing themes

Saving to file

Theme showdown

Plot Attributes and Color Palettes

Pump it up

Music effects on depression

Self-reported conditions

Efficient visualizations with layouts

Composers mental health

Streaming while working

Favorite genres

Series Recipes

Series recipe notation

Spotify as therapy

Log scatter recipe

Customizing Julia Plots

Learn to visualize data in DataFrames, using data manipulation tools for these data structures. Delve into multi-dimensional views and grid-based layouts for more effective data representation. Elevate your data storytelling and unlock new insights in your datasets!

Plotting data in DataFrames

Upcharging older smokers

Upcharging heavier smokers

BMI versus age

Plotting in More Dimensions

Slicing by region

Aging and having kids

Multiple plots from DataFrames

Correlation matrix plot

BMI per region

Smokers by age

Region premiums

Wrap-up

Plotting Data in DataFrames

funds

food

commodities

streaming

insurance

Master data visualization using Julia. From fundamentals to advanced methods, gain the ability to craft impactful visuals and effectively communicate insights. Learn to wield Julia's diverse visualizations like line plots, scatter plots, histograms, box plots, or violin plots, and understand when and how to use them. Elevate your skills by grasping techniques like combining multiple plots, customizing every aspect of your visualizations, crafting plotting recipes, and visualizing DataFrames.

Learn to create stunning data visualizations with Julia, the fast and powerful programming language for data science. This course starts with the basics of data visualization and gradually introduces advanced techniques. By the end, you'll have the skills to create impactful visualizations and confidently share your insights with others.<br>

<h2>Tell stories with data</h2>

Begin your exploration by grasping the core concepts of data visualization, focusing on learning when and how to construct Julia's different visualizations, such as line plots, scatter plots, histograms, box plots, and violin plots.<br>

<h2>Bring your plotting skills to the next level</h2>

As you delve deeper into the course, you'll uncover advanced techniques for enhancing your visualizations, such as combining multiple plots, customizing every aspect of your visualizations, creating plotting recipes, and plotting data in DataFrames. By the end of this course, you'll be equipped with a versatile skill set in data visualization with Julia, enabling you to create stunning visualizations.

Data Manipulation in Julia

Master data visualization in Julia. Learn how to make stunning plots while understanding when and how to use them.

Introduction to Data Visualization with Julia

Julia-Grundlagen

Data visualization facilitates the detection of trends, patterns, and uncertainties in your data.

Graphs provide an effective means of summarizing and communicating data.

Plotting is necessary to identify outliers in data.

Graphs are more effective than statistical tests in identifying relationships between variables.

Why should I plot?

Introduction to Data Visualization with Julia

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