About this Course
3.3
8 ratings
4 reviews
Specialization
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Hours to complete

Approx. 21 hours to complete

Suggested: Approximately 2-5 hours per week. ...
Available languages

English

Subtitles: English...
Specialization
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Hours to complete

Approx. 21 hours to complete

Suggested: Approximately 2-5 hours per week. ...
Available languages

English

Subtitles: English...

Syllabus - What you will learn from this course

Week
1
Hours to complete
2 hours to complete

Getting Started

...
Reading
3 videos (Total 26 min), 8 readings, 1 quiz
Video3 videos
Introduction to Data Exploration7m
Data Challenges12m
Reading8 readings
About the Course10m
Best Practices for Online Learning10m
What will I be able to do when I complete this course?10m
Technology Tools10m
Learning Journey Syllabus10m
Lesson Introduction: What is Visualization?5m
Lesson Introduction: Data Exploration5m
Data Challenges5m
Quiz1 practice exercise
Knowledge Check: Data Visualization and Exploration30m
Week
2
Hours to complete
4 hours to complete

Introduction to Data Exploration Components

...
Reading
7 videos (Total 106 min), 8 readings, 2 quizzes
Video7 videos
Vector Data12m
Basis of a Vector Space11m
Vector Features8m
Vector Distance Measures26m
Vector Norms8m
Strings and Sequences24m
Reading8 readings
Lesson Introduction: Common Data Representations3m
Introduction to Data Models and Data Organization5m
Vector Data10m
Basis of Vector Data10m
Vector Features10m
Vector Distance Measures10m
Vector Norms10m
Strings and Spaces10m
Quiz2 practice exercises
Knowledge Check: Vector Data8m
Knowledge Check: Common Data Representations30m
Week
3
Hours to complete
7 hours to complete

Exploratory Querying and Visual Variables Used in Data Exploration and Visualization

...
Reading
4 videos (Total 83 min), 5 readings, 2 quizzes
Video4 videos
Visual Variables18m
Color Schemes and Design13m
Jupyter Notebooks Demonstration: Cereal Data9m
Reading5 readings
Exploratory Querying10m
Lesson Introduction: Visual Variables10m
Lesson Introduction: Color Schemes and Design10m
Next Steps: Jupyter Notebook Demonstrations10m
Jupyter Notebook Demonstration: Loading Data in Python10m
Quiz1 practice exercise
Knowledge Check: Visual Elements Used in Data Visualization30m
Week
4
Hours to complete
3 hours to complete

Statistical Graphics: Design Principles for the Most Widely Used Data Visualization Charts

...
Reading
5 videos (Total 45 min), 5 readings, 4 quizzes
Video5 videos
Introduction to Pie Charts5m
Bar and Line Charts10m
Design Considerations for Non-Data Components of Graphs9m
Creating Histograms14m
Reading5 readings
Exploratory Data Analysis10m
Lesson Introduction: Design Principles for Pie and Donut Charts10m
Lesson Introduction: Design Principles for Bar Charts and Line Charts10m
Design Considerations for Non-Data Components of Graphs10m
Lesson Introduction: Design Principles for Histograms10m
Quiz4 practice exercises
Knowledge Check: Exploratory Data Analysis30m
Knowledge Check: Pie and Donut Charts4m
Knowledge Check: Bar and Line Charts30m
Knowledge Check: Histograms30m
3.3

Top Reviews

By AFAug 15th 2018

The concepts were clearly explained in a practical manner. I am already able to upon it.

Instructors

Avatar

Ross Maciejewsk

Associate Professor at Arizona State University in the School of Computing, Informatics & Decision Systems Engineering and Director of the Center for Accelerating Operational Efficiency
School of Computing, Informatics & Decision Systems Engineering
Avatar

K. Selcuk Candan

Professor of Computer Science and Engineering
Director of ASU’s Center for Assured and Scalable Data Engineering (CASCADE)
Graduation Cap

Start working towards your Master's degree

This course is part of the 100% online Master of Computer Science from Arizona State University. If you are admitted to the full program, your courses count towards your degree learning.

About Arizona State University

Arizona State University has developed a new model for the American Research University, creating an institution that is committed to excellence, access and impact. ASU measures itself by those it includes, not by those it excludes. ASU pursues research that contributes to the public good, and ASU assumes major responsibility for the economic, social and cultural vitality of the communities that surround it. ...

About the Data Visualization Specialization

Visual representations generated by statistical models help us to make sense of large, complex datasets through interactive exploration, thereby enabling big data to realize its potential for informing decisions. This specialization covers techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science to enhance the understanding of complex data....
Data Visualization

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.