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.
第 1 门课程
Introduction to Data Exploration and Visualization
This course answers the questions, What is data visualization and What is the power of visualization? It also introduces core concepts such as dataset elements, data warehouses and exploratory querying, and combinations of visual variables for graphic usefulness, as well as the types of statistical graphs, ?tools that are essential to exploratory data analysis.
第 2 门课程
Multivariate and Geographical Data Analysis
Covering the tools and techniques of both multivariate and geographical analysis, this course provides hands-on experience visualizing data that represents multiple variables. This course will use statistical techniques and software to develop and analyze geographical knowledge.
第 3 门课程
Temporal and Hierarchical Data Analysis
Data repositories in which cases are related to subcases are identified as hierarchical. This course covers the representation schemes of hierarchies and algorithms that enable analysis of hierarchical data, as well as provides opportunities to apply several methods of analysis.
第 4 门课程
Additional Tools Used for Data Visualization
This course will expose learners to additional tools that can be used to perform Data Visualization. In particular, the courses focuses on Tableau, a state-of-the-art visualization package. In this course, the visualization concepts from previous courses are reinforced and the Tableau software is introduced through replication of the visualizations built in previous courses.
Designed to help you practice and apply the skills you learn.
Project 1: Analyzing Theme Park Patronage
Project 2: Analyzing Wait Times and Dynamics of Theme Park Patronage
Project 3: Exploring and Clustering Trajectories in a Theme Park
Project 4: Finding Commonalities Between Theme Park Patrons
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How long does it take to complete the Specialization?
Time to completion can vary based on your schedule and experience level, most individual courses, in which this Specialization has 4, will take about a month to complete if you devote 2-5 hours per week.
What background knowledge is necessary?
Basic statistics and computer science knowledge including computer organization and architecture, discrete mathematics, data structures, and algorithms
Knowledge of high-level programming languages (e.g., C++, Java) and scripting language (e.g., Python), Jupyter Notebooks
Do I need to take the courses in a specific order?
No, courses may be taken in any order.
Will I earn university credit for completing the Specialization?
All courses in this Specialization form the lecture and skill practice component of a corresponding course in ASU’s online Master of Computer Science Degree. You can apply to the degree program either before or after you begin the Specialization.
What will I be able to do upon completing the Specialization?
Learners completing this specialization will be able to:
Develop exploratory data analysis and visualization tools using Python and Jupyter notebooks
Apply design principles for a variety of statistical graphics and visualizations including scatterplots, line charts, histograms, and choropleth maps
Combine exploratory queries, graphics, and interaction to develop functional tools for exploratory data analysis and visualization