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.


Soorten rapporten

Data Science-rapport

Iets om over te rapporteren

Opties

Eindrapport

Samenvattend rapport

Kort samengevat

Reproduceerbaarheid en referenties

Repliceer mij

Slechte praktijk

Best practice

Dezelfde resultaten

Schrijf precieze en duidelijke rapporten

Voor verbetering vatbaar

Begrijpelijk

Halfvol glas

Verbeterbaar

Krachtige woorden

Casestudy: rapport over kredietrisico

Geef me krediet

Mijn krediet rapporteren

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

Je hebt je data geanalyseerd, je model gedraaid en je voorspellingen gemaakt. Nu is het tijd om je data tot leven te brengen! Je bevindingen presenteren aan stakeholders zodat zij datagedreven beslissingen kunnen nemen, is een essentiële vaardigheid voor elke data scientist. In deze cursus leer je hoe je storytelling inzet om contact te maken met je publiek en hen te helpen de inhoud van je presentatie te begrijpen—zodat ze de juiste beslissingen kunnen nemen. Met praktische oefeningen ontdek je de voor- en nadelen van mondelinge en schriftelijke formats. Je verbetert ook hoe je technische resultaten vertaalt naar overtuigende verhalen, met de juiste data, visualisaties en technieken voor live presenteren. Ga aan de slag en verbeter vandaag nog je data storytelling.

De video's bevatten live transcripts die je kunt weergeven door linksonder in de video's op "Show transcript" te klikken.									
De verklarende woordenlijst van de cursus vind je rechts in de sectie met bronnen.
									
Om CPE-credits te behalen, moet je de cursus afronden en een score van 70% halen op de gekwalificeerde toets. Je kunt naar de toets navigeren door rechts op de CPE-credits-callout te klikken.									

Understanding Data Science

Understanding Data Visualization

Niemand kijkt graag naar spreadsheets! Breng je data tot leven en vertaal technische data naar inzichten.

Concepten voor datacommunicatie

Medewerker data-analist in SQL

Associate Data Scientist in R

Associate Data Scientist  in Python

I evaluated the classification predictions using a confusion matrix. This indicated that the model accuracy was 85%.

The data exploration analysis introduced new features that should be incorporated into the model.

There is a positive correlation between the number of rooms and the price, people with children are willing to pay more.

A confusion matrix was used to evaluate the classification predictions. The matrix showed a model accuracy of 85%.

The data exploration analysis introduced features that should be incorporated into the model.

There is a positive correlation between the number of rooms and the price because people with children are willing to pay more.

Krachtige woorden

Concepten voor datacommunicatie

Praktische interactieve oefening