Description
In this course, you will learn how to convert large volumes of data into visual elements to clearly and efficiently communicate their meaning. We will use diagrams, maps, and statistical graphs to intuitively communicate the most relevant information.
To do this, you will learn how to create the most relevant graphs with the Matplotlib library and edit their content and visual appearance.
Then, we will get to know the Seaborn library, which will allow us to create complex graphs in a few lines of code in an efficient way.
Next, we will see how to represent information on maps using the Folium library. We will learn how to control the properties of these maps and how to add markers to the locations we want to highlight, as well as how to create choropleth and density maps.
Finally, through the Plotly and Dash libraries, we will learn how to create dashboards that allow us to monitor all the relevant properties of a system through graphs.
The tools that we'll use
Matplotlib
Matplotlib is the main plotting library in Python.
Seaborn
Seaborn is a Python data visualization library based on Matplotlib.
Plotly
Plotly's Python graphing library makes interactive, publication-quality graphs.
Prerequisites
The only prerequisite of this course is having a basic fluency with the Python programming language.
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Example Curriculum
- A brief introduction to matplotlib (2:13)
- Line Plot (1:29)
- Our first graph (3:35)
- Anatomy of a figure in matplotlib (3:27)
- The Figure and Axes classes (2:26)
- Pyplot (6:39)
- Object-oriented interface (7:09)
- Add annotations to the graph (7:06)
- Draw shapes on the graph (5:43)
- Draw lines on the graph (2:12)
- Manipulate the axes of the graph (7:17)
- Dataset presentation (3:34)
- Loading the dataset (5:18)
- Pie chart (1:34)
- How many records of each class do we have? (4:55)
- Modifying the style of the chart (3:19)
- Scatter Plot (1:45)
- Can we differentiate the species by their petals? (6:21)
- 3D Plots (1:37)
- Does it help to know the length of the sepal? (6:15)
- Box Plot (3:29)
- What is the range of values for each feature? (2:29)
- Violin Plot (2:19)
- Feature value distribution (9:05)
- Bar chart (1:11)
- Multiple graphs (1:51)
- Do different species have different features? (11:28)
- Global styles (2:58)
- Dataset presentation (1:49)
- Loading the dataset (2:40)
- Density plots (1:13)
- Who was on the Titanic? - Part 1 (6:14)
- Who was on the Titanic? - Part 2 (8:30)
- How much did the guests pay? (2:28)
- Does paying more improve the odds of surviving? (4:13)
- Where did the guests stay? (3:27)
- What are the odds of surviving? (5:12)
- Dataset loading and presentation (1:02)
- How many diamonds do we have (by color, cut and clarity)? (3:54)
- What are the most common features of diamonds? (1:38)
- How does cut, color and clarity affect the price? (1:56)
- Which continuous variables affect the price? (4:06)
- Explore the relationship between carats, cut, clarity and price (3:10)
- Creating the dash application (5:27)
- Dataset loading and presentation (1:40)
- Creating the application title (2:00)
- Creating the scatterplot (7:15)
- Adding a selector for the scatterplot (3:25)
- Connecting the scatterplot selector with a callback (5:14)
- Adding a selector for the map (2:42)
- Creating the map (3:10)
- Connecting the map selector with a callback (3:27)
- Creating the trend chart (4:20)
- Adding a selector to the trend chart (2:04)
- Connecting the selector to the trend chart (3:18)
- Creating the distribution chart (3:15)
- Adding a selector to the distribution chart (2:19)
- Connecting the selector to the distribution chart (4:07)
- Exploring the resulting dashboard (4:00)
- Dashboard overview (2:35)
- Creating the dashboard (2:42)
- Dataset loading and presentation (1:06)
- Dashboard styling (3:30)
- Creating the dashboard heading (2:25)
- First chart: evolution of website visits (5:53)
- Second chart: sales funnel (13:43)
- Third chart: proportion of visits by category (5:56)
- Fourth chart: visit distribution by day and hour (10:46)
- Fifth chart: visits per country (2:51)
- Dashboard analysis (2:01)
