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數據可視化專項課程

Data Visualization

Launch Your Career in Data Visulization。Master strategies for creating effective visualizations to enhance understanding of complex data.

亞利桑那州立大學

Coursera

計算機

普通(中級)

3 個月

課程概況

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.

課程大綱

第 1 門課程
Introduction to Data Exploration and Visualization

課程概述
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 門課程
Multivariate and Geographical Data Analysis

課程概述
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 門課程
Temporal and Hierarchical Data Analysis

課程概述
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 門課程
Additional Tools Used for Data Visualization

課程概述
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.

課程項目

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

常見問題

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請閱讀完整的退款政策。

我可以只注冊一門課程嗎?我對整個專項課程不感興趣。

要注冊單門課程,請在目錄中搜索相應的課程標題。

當當您訂閱屬于專項課程的課程時,您將自動訂閱整個專項課程。如果您僅對單門課程感興趣,您將需要在完成本課程后取消您的訂閱,以停止定期繳納每月費用。

可以申請助學金嗎?

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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

Self-Driving Cars. Become an autonomous vehicle engineer.
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