Let's start with the importance of data storytelling and the elements you need to tell stories with data. You'll learn best practices to influence how decisions are made before learning how to translate technical results into stories for non-technical stakeholders.


Fundamentals of storytelling

The story begins

Building a story

Translating technical results

A non-tech story

Be aware

Impacting the decision-making process

Is it a true story?

Structured to impact

A story to compare

Storytelling with Data

Deepen your storytelling knowledge. Learn how to avoid common mistakes when telling stories with data by tailoring your presentations to your audience. Then learn best practices for including visualizations and choosing between oral or written formats to make sure your presentations pack a punch!


Selecting the right data

The truth about salaries

Earning interests

Showing relevant statistics

Salary variation

On a payroll

It's not significant

Visualizations for different audiences

Salary development

Salary on demand

Choosing the appropriate format

A communication problem

Should we meet?

When in doubt

Preparing to Communicate the Data

Now that you understand how to prepare for communicating findings, it’s time to learn how to structure your reports. You'll also learn the importance of reproducibility (work smarter, not harder) and how to get to the point when describing your findings. You’ll then get to apply all you’ve learned to a real-world use case as you create a compelling report on credit risk.


Types of reports

Data Science Report

Something to report

Options

Final report

Summary report

In summary

Reproducibility and references

Replicate me

Bad practice

Best practice

Same results

Write precise and clear reports

Improvable

Intelligible

Half-empty glass

Strong words

Case study: report on credit risk

Credit me

Summary Report

Report my credit

Structuring Written Reports

You'll finish by learning simple techniques to structure a presentation, communicate insights, and inspire your audience to take action. Lastly, you'll learn how to improve your communication style and prepare to handle questions from your audience.


Planning an oral presentation

Is this the plan?

An effective plan!

Building presentation slides

A color building

Too much text

The right building

Delivering the presentation

Put it into practice

Avoiding common errors

The true mistake

Do's and don'ts

Congratulations!

Building Compelling Oral Presentations

You’ve analyzed your data, run your model, and made your predictions.  Now, it's time to bring your data to life! Presenting findings to stakeholders so they can make data-driven decisions is an essential skill for all data scientists. In this course, you’ll learn how to use storytelling to connect with your audience and help them understand the content of your presentation—so they can make the right decisions. Through hands-on exercises, you’ll learn the advantages and disadvantages of oral and written formats. You’ll also improve how you translate technical results into compelling stories, using the correct data, visualizations, and in-person presentation techniques. Start learning and improve your data storytelling today.


<h2>Learn the Basics of Data Communication</h2> 
<br>
You’ve analyzed your data, run your model, and made your predictions. Now, it's time to bring your data to life! Presenting findings to stakeholders so they can make data-driven decisions is an essential skill for all data scientists. In this course, you’ll learn how to use storytelling to connect with your audience and help them understand the content of your presentation—so they can make the right decisions. 
<br><br>
<h2>Explore Formats of Data Communication</h2> 
<br>
Through hands-on exercises, you’ll learn the advantages and disadvantages of oral and written formats. You’ll also improve how you translate technical results into compelling stories, using the correct data, visualizations, and in-person presentation techniques. Start learning and improve your data storytelling today.

Understanding Data Science

Understanding Data Visualization

No one enjoys looking at spreadsheets! Bring your data to life. Improve your presentation and learn how to translate technical data into actionable insights. 

Data Communication Concepts

Associate Data Analyst in SQL

Associate Data Scientist in R

Associate Data Scientist  in Python

The purpose of this report is to describe the results obtained from a model that predicts and identifies customers that will likely churn.

The data, gathered from the website, contains categorical data, such as gender, and internet service. It was converted to either 0 or 1 columns. 

The dataset was split into train (70%) and test (30%) set. A K-Nearest Neighbors model was trained and model performance was evaluated.

As you can see in the graph, this report analyzes the importance of different features such as monthly charges, contract type and phone service.

The model has an accuracy of 92% in predicting customer churn. The internet service type and discounts correlates with customer churning.

In summary, discounts on premium phone services should be implemented in order to retain customers.

Something to report

Data Communication Concepts

Hands-on interactive exercise