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.

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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

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When was the last time you told a story with data? Impactful stories can change the way we look at data, whether the story includes quantitative statistics and/or the trends we observe. 

Get an overview of the course, its intended audience, and prerequisites.

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

Before We Begin

What is the course about?

Who is this course for?

Prerequisites