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

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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

**(Select all that apply.)** Which plots can be used to effectively visualize outliers?

(Select all that apply.) Which plots can be used to effectively visualize outliers?

Fixing accurate data samples so they produce correct visualizations

Substituting data samples with estimated values

**(Select all that apply.)** Which elements are important to include in a forecasting plot?

(Select all that apply.) Which elements are important to include in a forecasting plot?

Which feature in this plot has the highest feature importance for the model?

<img src="/udata/nPPQYYme85D/feature importance.png" loading="lazy" />

Which feature in this plot has the highest feature importance for the model?
<img src="/udata/nPPQYYme85D/feature importance.png" loading="lazy" />

Use arrows to point to specific data trends

Use a different color for each data point in the plot

Use a different color for individual data samples

**(Select all that apply.)** What are some ways to add callouts to visualizations?

(Select all that apply.) What are some ways to add callouts to visualizations?

Tackling Real-World Data

Tackling Real-World Data

Test your understanding of tackling real-world data.

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: Tackling Real-World Data