Gain insights into data storytelling in Python using Matplotlib, Seaborn, and Plotly. Explore trends, handle data challenges, and create impactful visual narratives aligning with business goals.

Data Storytelling- Efficient Data Visualization and Narratives  (1).png

plotly_bokeh.tar.gz

plotly and bokeh

Mining the insights from data is the next critical step after parsing data and generating visualizations. This activity is called data storytelling, where you form a cohesive story explaining the strengths, weaknesses, and trends of your dataset with the help of predictions through machine learning models. 

In this course, you will learn how to identify and evaluate your data for trends, handle common real-world challenges of messy data such as large datasets and missing values, and present the right visualizations for different kinds of data. We will use Python, Matplotlib, Seaborn, and Plotly as the data science libraries for this course.

This course will help you develop the key skills to translate the technical indicators in line with business objectives. It also aids in building your technical skills and processes to create effective data visualizations and narratives. Data storytelling can help you unlock actionable insights from your data.

Data Storytelling through Visualizations in Python

So we can guide the audience's attention to focus on the right parts of the narrative at a given time during the presentation

So the audience does not reject the presented claims

So the audience only looks at parts of the narrative that are correct

Why is holding an audience's attention important?

Why is holding an audience’s attention important?

gradually revealing elements of the story

overlaying visualizations on top of each other

Designing a visualization to be a layer in a stack

**(Fill in the blank.)** Layering is a data storytelling concept that involves ________.

(Fill in the blank.) Layering is a data storytelling concept that involves ________.

Bar plot: Highlight one bar in a different color than the others for emphasis.

Line Plot: Draw one line in blue and the others in grey for emphasis.

Bar plot: Have each bar in a different color so the viewer can easily identify which is which.

**(Select all that apply.)** Which ways are good ways to prepare a visualization for a presentation?

(Select all that apply.) Which ways are good ways to prepare a visualization for a presentation?

**(Select all that apply.)** Data dashboards can include a mix of which of the following?

(Select all that apply.) Data dashboards can include a mix of which of the following?

It gives a data scientist tips on how to use bokeh's tools.

It specifies what kind of interactivity the plot will support.

It specifies the data/metadata that will be specified when the user interacts with the plot.

What does the `tooltips` argument do with the bokeh library?

What does the <code>tooltips</code> argument do with the bokeh library?

Test your understanding of data presentation.

Introduction

Effective Data Analysis and Visualization

Extracting Narratives from Data

Tackling Real-World Data

Data Presentation

Data Types and Sources

Data Storytelling Best Practices

Wrapping Up

Test Your Data Storytelling Skills

Quiz: Data Presentation