What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Fundamentals of Data Visualization

Économisez sur les compétences qui vous font briller avec 40 % de réduction sur 3 mois de Coursera Plus. Économisez maintenant

Fundamentals of Data Visualization

Ce cours fait partie de Spécialisation "Vital Skills for Data Science"

Enseigné en Français (doublage IA)

Instructeur : Danielle Szafir

8 592 déjà inscrits

Inclus avec

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

43 avis

niveau Intermédiaire

Expérience recommandée

Planning flexible

2 semaines à 10 heures une semaine

Apprenez à votre propre rythme

Préparer un diplôme

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

43 avis

niveau Intermédiaire

Expérience recommandée

Planning flexible

2 semaines à 10 heures une semaine

Apprenez à votre propre rythme

Préparer un diplôme

Ce que vous apprendrez

Develop a toolkit for exploring and communicating complex data using visualization
Produce basic data visualizations using a chosen dataset
Compare methods for visualizing data and understand how these methods may guide users towards different conclusions
Evaluate how effectively a visualization conveys target data

Compétences que vous acquerrez

Catégorie : Usability
Catégorie : User Research
Catégorie : User Centered Design
Catégorie : Human Centered Design
Catégorie : Data Presentation
Catégorie : Visualization (Computer Graphics)
Catégorie : Interactive Data Visualization
Catégorie : Design Research
Catégorie : Data Visualization Software
Catégorie : Data Visualization
Catégorie : Quantitative Research
Catégorie : Design Elements And Principles
Catégorie : Usability Testing
Catégorie : Graphic and Visual Design

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Évaluations

4 devoirs

Enseigné en Français (doublage IA)

Découvrez comment les employés des entreprises prestigieuses maîtrisent des compétences recherchées

En savoir plus sur Coursera pour les affaires

logos de Petrobras, TATA, Danone, Capgemini, P&G et L'Oreal

Élaborez votre expertise du sujet

Ce cours fait partie de la Spécialisation "Vital Skills for Data Science"

Lorsque vous vous inscrivez à ce cours, vous êtes également inscrit(e) à cette Spécialisation.

Apprenez de nouveaux concepts auprès d'experts du secteur
Acquérez une compréhension de base d'un sujet ou d'un outil
Développez des compétences professionnelles avec des projets pratiques
Obtenez un certificat professionnel partageable

Il y a 3 modules dans ce cours

Data is everywhere. Charts, graphs, and other types of information visualizations help people to make sense of this data. This course explores the design, development, and evaluation of such information visualizations. By combining aspects of design, computer graphics, HCI, and data science, you will gain hands-on experience with creating visualizations, using exploratory tools, and architecting data narratives. Topics include user-centered design, web-based visualization, data cognition and perception, and design evaluation.

This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

In this module, you will learn the foundations of visualization design. You will walk through the key components of a visualization, how we effectively represent data using channels like color, size, and position, and some ground rules for honest and effective visualization. You will also gain preliminary exposure to Altair, a Python library for rapidly generating interactive visualizations. Each week will also include either two readings or one reading and one notebook activity.

Inclus

13 vidéos17 lectures2 devoirs2 sujets de discussion2 laboratoires non notés

13 vidéosTotal 157 minutes

Course Overview3 minutes
Why Visualization?14 minutes
A Grammar of Graphics11 minutes
Introduction to Altair7 minutes
Hands on with Altair - Part 113 minutes
Hands on with Altair - Part 212 minutes
Mapping Basics11 minutes
Mapping Tips12 minutes
Color18 minutes
Rules of Thumb17 minutes
Introduction to Interaction13 minutes
Interactions Part 114 minutes
Interactions Part 212 minutes

17 lecturesTotal 161 minutes

Course Updates and Accessibility Support1 minute
Earn Academic Credit for your Work! 10 minutes
Course Support10 minutes
Assessment Expectations10 minutes
AI Citation and Acknowledgement10 minutes
Assessment Expectations10 minutes
A Tour Through the Visualization Zoo10 minutes
Data Humanism: The Revolutionary Future of Data Visualization10 minutes
Vega-Lite: A Grammar of Interactive Graphics10 minutes
Workshop Overview of Altair Basics10 minutes
Exploratory Data Visualization with Altair10 minutes
The Structure of the Information Visualization Design Space10 minutes
Extra Resources: Data Types, Graphical Marks, and Visual Encoding Channels10 minutes
The Good, the Bad, and the Biased10 minutes
Extra Resources: WTF Visualizations10 minutes
Extra Resources: Ethical Dimensions of Visualization Research10 minutes
Toward a Deeper Understanding of the Role of Interaction in Information Visualization10 minutes

2 devoirsTotal 35 minutes

AI Policy Quiz5 minutes
Week 1 Quiz30 minutes

2 sujets de discussionTotal 20 minutes

Introduce Yourself10 minutes
Visualization Project Part 1: Finding your Data10 minutes

2 laboratoires non notésTotal 120 minutes

Introduction to Altair60 minutes
Implementing Interaction in Altair 60 minutes

In this module, you will learn how to choose the right visualization for a given scenario. You will learn how to reason about the different kinds of questions people ask with visualization and, how to align your design with that task. The module will cover basics of task analysis, methods for task elicitation, and foundational knowledge of visual perception for design. Each week will also include two external readings or one reading and one notebook activity.

Inclus

9 vidéos7 lectures1 devoir1 sujet de discussion

9 vidéosTotal 110 minutes

Visualization Tasks13 minutes
Task Elicitation16 minutes
Basic Design Methods12 minutes
Design Studies17 minutes
Perception Overview6 minutes
Preattention14 minutes
Attention & Search14 minutes
Uncertainty11 minutes
Ethical Questions8 minutes

7 lecturesTotal 70 minutes

A Design Space of Visualization Tasks10 minutes
Design Study Methodology: Reflections from the Trenches and the Stacks10 minutes
Overview: The Design, Adoption, and Analysis of a Visual Document Mining Tool for Investigative Journalists10 minutes
Criteria for Rigor in Visualization Design Study10 minutes
On the Prospects for a Science of Visualization10 minutes
Attention and Visual Memory in Visualization and Computer Graphics10 minutes
Why Authors Don't Visualize Uncertainty10 minutes

1 devoirTotal 30 minutes

Week 2 Quiz30 minutes

1 sujet de discussionTotal 10 minutes

Visualization Project Part 2: Sketching your Data10 minutes

In this module, you will learn how to assess the effectiveness of your visualization. You will learn both qualitative and quantitative approaches for evaluating visualizations as well as how to isolate key elements for assessment and iteration. The module will cover basics of insight-based evaluation, interview studies, and experimental design and analysis. Each week will also include two external readings or one reading and one notebook activity.

Inclus

7 vidéos7 lectures1 devoir1 sujet de discussion

7 vidéosTotal 80 minutes

Module Overview2 minutes
Evaluation Overview8 minutes
Defining Insight12 minutes
Qualitative Evaluation16 minutes
Experimental Evaluation Part 114 minutes
Experimental Evaluation Part 217 minutes
Evaluation Trade-Offs11 minutes

7 lecturesTotal 70 minutes

Empirical Studies in Information Visualization: Seven Scenarios10 minutes
Toward Measuring Visualization Insight10 minutes
Criteria for Rigor in Visualization Design Study10 minutes
Experimental Research in HCI10 minutes
A Design Space of Vision Science Methods for Visualization Research10 minutes
Empirical Studies in Information Visualization: Seven Scenarios10 minutes
Evaluating Information Visualizations10 minutes

1 devoirTotal 30 minutes

Week 3 Quiz30 minutes

1 sujet de discussionTotal 10 minutes

Visualization Project Part 3: A Plan for Evaluation10 minutes

Obtenez un certificat professionnel

Ajoutez ce titre à votre profil LinkedIn, à votre curriculum vitae ou à votre CV. Partagez-le sur les médias sociaux et dans votre évaluation des performances.

Préparer un diplôme

Ce site cours fait partie du (des) programme(s) diplômant(s) suivant(s) proposé(s) par University of Colorado Boulder. Si vous êtes admis et que vous vous inscrivez, les cours que vous avez suivis peuvent compter pour l'apprentissage de votre diplôme et vos progrès peuvent être transférés avec vous.¹

Instructeur

Évaluations de l’enseignant

(13 évaluations)

Danielle Szafir

University of Colorado Boulder

1 Cours8 592 apprenants

Offert par

University of Colorado Boulder

En savoir plus sur Data Analysis

Statut : Essai gratuit
University of Colorado Boulder
Data Science as a Field
Cours
Statut : Essai gratuit
Microsoft
Data Visualization Fundamentals
Cours
Statut : Essai gratuit
University of Colorado Boulder
Graphic Design
Cours
Statut : Essai gratuit
Coursera
Charts: Visualize Data Like a Pro
Cours

Pour quelles raisons les étudiants sur Coursera nous choisissent-ils pour leur carrière ?

Felipe M.

Étudiant(e) depuis 2018

’Pouvoir suivre des cours à mon rythme à été une expérience extraordinaire. Je peux apprendre chaque fois que mon emploi du temps me le permet et en fonction de mon humeur.’

Jennifer J.

Étudiant(e) depuis 2020

’J'ai directement appliqué les concepts et les compétences que j'ai appris de mes cours à un nouveau projet passionnant au travail.’

Larry W.

Étudiant(e) depuis 2021

’Lorsque j'ai besoin de cours sur des sujets que mon université ne propose pas, Coursera est l'un des meilleurs endroits où se rendre.’

Chaitanya A.

’Apprendre, ce n'est pas seulement s'améliorer dans son travail : c'est bien plus que cela. Coursera me permet d'apprendre sans limites.’

Avis des étudiants

5 stars
79,06 %
4 stars
13,95 %
3 stars
4,65 %
2 stars
0 %
1 star
2,32 %

Affichage de 3 sur 43

Révisé le 16 déc. 2023

Very Lively interaction from the mentor. Simplified explanations

Révisé le 23 janv. 2026

Not your basic data visualization course - very in depth and interesting with concepts that are fresh and new. Professor is very thorough and understandable.

Voir plus d’avis

Foire Aux Questions

A cross-listed course is offered under two or more CU Boulder degree programs on Coursera. For example, Dynamic Programming, Greedy Algorithms is offered as both CSCA 5414 for the MS-CS and DTSA 5503 for the MS-DS.

· You may not earn credit for more than one version of a cross-listed course.

· You can identify cross-listed courses by checking your program’s student handbook.

· Your transcript will be affected. Cross-listed courses are considered equivalent when evaluating graduation requirements. However, we encourage you to take your program's versions of cross-listed courses (when available) to ensure your CU transcript reflects the substantial amount of coursework you are completing directly in your home department. Any courses you complete from another program will appear on your CU transcript with that program’s course prefix (e.g., DTSA vs. CSCA).

· Programs may have different minimum grade requirements for admission and graduation. For example, the MS-DS requires a C or better on all courses for graduation (and a 3.0 pathway GPA for admission), whereas the MS-CS requires a B or better on all breadth courses and a C or better on all elective courses for graduation (and a B or better on each pathway course for admission). All programs require students to maintain a 3.0 cumulative GPA for admission and graduation.

Yes. Cross-listed courses are considered equivalent when evaluating graduation requirements. You can identify cross-listed courses by checking your program’s student handbook.

You may upgrade and pay tuition during any open enrollment period to earn graduate-level CU Boulder credit for << this course/ courses in this specialization>>. Because << this course is / these courses are >> cross listed in both the MS in Computer Science and the MS in Data Science programs, you will need to determine which program you would like to earn the credit from before you upgrade.

MS in Data Science (MS-DS) Credit: To upgrade to the for-credit data science (DTSA) version of << this course / these courses >>, use the MS-DS enrollment form. See How It Works.

MS in Computer Science (MS-CS) Credit: To upgrade to the for-credit computer science (CSCA) version of << this course / these courses >>, use the MS-CS enrollment form. See How It Works.

If you are unsure of which program is the best fit for you, review the MS-CS and MS-DS program websites, and then contact datascience@colorado.edu or mscscoursera-info@colorado.edu if you still have questions.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.