About this Course
2,987

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 25 hours to complete

Suggested: 2-5 hours per week...

English

Subtitles: English

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 25 hours to complete

Suggested: 2-5 hours per week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
2 hours to complete

Getting Started

...
1 video (Total 14 min), 6 readings, 1 quiz
6 readings
About the Course10m
Best Practices for Online Learning10m
Technology Tools10m
Learning Journey Syllabus10m
Introduction to Multivariate Analysis10m
Multivariate Analysis10m
1 practice exercise
Knowledge Check: Introduction to Multivariate Analysis30m
Week
2
4 hours to complete

Multivariate Analysis

...
6 videos (Total 79 min), 6 readings, 4 quizzes
6 videos
Mosaic Plots8m
Pixel Based Displays6m
Parallel Coordinate Plots20m
Text Visualization7m
Jupyter Notebook Demonstration: Multivariate Analysis17m
6 readings
Introduction to Scatterplots10m
Introduction to Mosaic Plots and Pixel Based Displays10m
Introduction to Pixel Based Displays10m
Introduction to Parallel Coordinate Plots10m
Introduction to Text Visualization10m
Jupyter Notebook Demonstration: Advanced Graphics in Python10m
4 practice exercises
Knowledge Check: Introduction to Scatterplots30m
Knowledge Check: Mosaic Plots and Pixel Based Displays30m
Knowledge Check: Parallel Coordinate Plots30m
Knowledge Check: Text Visualization30m
Week
3
2 hours to complete

Supervised Learning

...
4 videos (Total 55 min), 4 readings, 1 quiz
4 videos
Supervised Learning: Nearest Neighbor16m
Supervised Learning: Regression8m
Supervised Learning: Evaluation11m
4 readings
Data Visualization and Machine Learning Connection10m
Introduction to Nearest Neighbor Classifier10m
Supervised Learning: Regression10m
Supervised Learning: Evaluation10m
1 practice exercise
Knowledge Check: Supervised Learning30m
Week
4
6 hours to complete

Unsupervised Learning

...
3 videos (Total 50 min), 3 readings, 2 quizzes
3 videos
Unsupervised Learning: Evaluation18m
Jupyter Notebook Demonstration: Machine Learning11m
3 readings
Introduction to Unsupervised Learning10m
Unsupervised Learning: Evaluation10m
Jupyter Notebook Demonstration: Machine Learning in Python10m
1 practice exercise
Knowledge Check: Unsupervised Learning30m
Week
5
4 hours to complete

Part 1: Geographical Analysis

...
8 videos (Total 73 min), 9 readings, 3 quizzes
8 videos
Thematic Maps7m
Coordinate System6m
Map Projections8m
Map Elements and Typography5m
Introduction to Choropleth Maps and Color Schemes8m
Data Classifications21m
Spatial Statistics12m
9 readings
Unit Introduction10m
Introduction to Geographic Analysis and Visualization10m
Thematic Maps10m
Coordinate System10m
Map Projections10m
Map Elements and Typography10m
Choropleth Maps10m
Data Classifications10m
Spatial Statistics10m
3 practice exercises
Knowledge Check: Introduction to Geographic Analysis30m
Knowledge Check: Choropleth Maps30m
Knowledge Check: Spatial Statistics30m
Week
6
7 hours to complete

Part 2: Geographical Analysis

...
4 videos (Total 46 min), 4 readings, 4 quizzes
4 videos
Spatial Scan Statistics8m
Geovisual Analytics Systems15m
Jupyter Notebook Demonstration: Geographic Data10m
4 readings
Spatial Autocorrelation10m
Spatial Scan Statistics10m
Geovisual Analytics Systems10m
Jupyter Notebook Demonstration: Geographic Visualization and Data Analysis in Python10m
3 practice exercises
Knowledge Check: Spatial Autocorrelation30m
Knowledge Check: Spatial Scan Statistics30m
Knowledge Check: Geovisual Analytics Systems30m

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

Huan Liu

Professor: Computer Science and Engineering
School of Computing, Informatics, and Decision Systems Engineering (CASCADE)

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.