Fundamentals of Visualization with Tableau

Start Date: 07/05/2020

Course Type: Common Course

Course Link:

About Course

In this first course of the specialization, you will discover just what data visualization is, and how we can use it to better see and understand data. Using Tableau, we’ll examine the fundamental concepts of data visualization and explore the Tableau interface, identifying and applying the various tools Tableau has to offer. By the end of the course you will be able to prepare and import data into Tableau and explain the relationship between data analytics and data visualization. This course is designed for the learner who has never used Tableau before, or who may need a refresher or want to explore Tableau in more depth. No prior technical or analytical background is required. The course will guide you through the steps necessary to create your first visualization story from the beginning based on data context, setting the stage for you to advance to the next course in the Specialization.

Course Syllabus

Welcome to this first module, where you will begin to discover the power of data visualization. You will define the meaning and purpose of data visualization and explore the various types of data visualization tools, beyond Tableau. You will install Tableau on your own device and create your first visualization.

Coursera Plus banner featuring three learners and university partner logos

Course Introduction

Fundamentals of Visualization with Tableau Project In this course you will be able to make infographics interactive. You will learn about the basic concepts of using figures, maps, and charts to make your images more appealing and effective. You will also learn how to manipulate data to make infographics more effective. You will also learn about the tableau file format and how to use the advanced functions within the tableau format. We use high quality images and video recording tools to make the videos and pictures of our students. This is the second course in the Visualization with Tableau Specialization. The course focuses more on the practical skills needed to use data visualization in the real world than on the theoretical background on visualization. You will learn how to use data to tell a story and how to use charts to do that. We will touch on advanced topics in data visualization such as line graphs and tree-ring regression and how to use these tools to make your images more effective. Learning Goals: After completing this course, you will be able to: - Design and use figures to make infographics more effective - Manipulate data to make infographics more effective - Use line graphs and tree-ring regression to make your images more effective - Use high-quality images and video recording tools to make the videos and pictures of your students more effective You will need to buy the tools and materials needed to complete the assignments. You can find detailed product information and pricing information at www.tableau.

Course Tag

Tableau Software Data Virtualization Data Visualization (DataViz) Visualization (Computer Graphics)

Related Wiki Topic

Article Example
Visualization (graphics) Scientific visualization is the transformation, selection, or representation of data from simulations or experiments, with an implicit or explicit geometric structure, to allow the exploration, analysis, and understanding of the data. Scientific visualization focuses and emphasizes the representation of higher order data using primarily graphics and animation techniques. It is a very important part of visualization and maybe the first one, as the visualization of experiments and phenomena is as old as science itself. Traditional areas of scientific visualization are flow visualization, medical visualization, astrophysical visualization, and chemical visualization. There are several different techniques to visualize scientific data, with isosurface reconstruction and direct volume rendering being the more common.
Data visualization John Tukey and Edward Tufte pushed the bounds of data visualization; Tukey with his new statistical approach of exploratory data analysis and Tufte with his book "The Visual Display of Quantitative Information" paved the way for refining data visualization techniques for more than statisticians. With the progression of technology came the progression of data visualization; starting with hand drawn visualizations and evolving into more technical applications – including interactive designs leading to software visualization. Programs like SAS, SOFA, R, Minitab, and more allow for data visualization in the field of statistics. Other data visualization applications, more focused and unique to individuals, programming languages such as D3, Python and JavaScript help to make the visualization of quantitative data a possibility.
Music visualization With the increasing popularity of virtual reality, several start ups have begun working on music visualization although reception has been mixed with one informal poll finding that only 33% of respondents were interested in music visualization for VR.
Purdue Spatial Visualization Test: Visualization of Rotations The Purdue Spatial Visualization Test-Visualization of Rotations (PSVT:R) is a test of spatial visualization ability published by Roland B. Guay in 1977. Many modifications of the test exist.
Information visualization The modern study of visualization started with computer graphics, which "has from its beginning been used to study scientific problems. However, in its early days the lack of graphics power often limited its usefulness. The recent emphasis on visualization started in 1987 with the special issue of Computer Graphics on Visualization in "Scientific Computing". Since then there have been several conferences and workshops, co-sponsored by the IEEE Computer Society and ACM SIGGRAPH". They have been devoted to the general topics of data visualisation, information visualization and scientific visualisation, and more specific areas such as volume visualization.
Interactive visualization Another type of interactive visualization is collaborative visualization, in which multiple people interact with the same computer visualization to communicate their ideas to each other or to explore information cooperatively. Frequently, collaborative visualization is used when people are physically separated. Using several networked computers, the same visualization can be presented to each person simultaneously. The people then make annotations to the visualization as well as communicate via audio (i.e., telephone), video (i.e., a video-conference), or text (i.e., IRC) messages.
Strategy visualization Strategy visualization is any kind of (semi-artistic) infographics for visualization of a business strategy.
Visualization (graphics) Visualization today has ever-expanding applications in science, education, engineering (e.g., product visualization), interactive multimedia, medicine, etc. Typical of a visualization application is the field of computer graphics. The invention of computer graphics may be the most important development in visualization since the invention of central perspective in the Renaissance period. The development of animation also helped advance visualization.
Visualization (graphics) As a subject in computer science, scientific visualization is the use of interactive, sensory representations, typically visual, of abstract data to reinforce cognition, hypothesis building, and reasoning. Data visualization is a related subcategory of visualization dealing with statistical graphics and geographic or spatial data (as in thematic cartography) that is abstracted in schematic form.
Social Visualization One of the common misperception of social visualization is that the relationship between Network Analysis or Social Network Visualization and Social Visualization; they are loosely related. Social network visualization is a traditional form of social visualization. It is more appropriate to consider in the context of visualization in social sciences. i.e. John Snow's maps of the 1854 cholera outbreak in Soho and Charles Booth's maps of poverty in London 1889
Interactive visualization Interactive visualization or interactive visualisation is a branch of graphic visualization in computer science that involves studying how humans interact with computers to create graphic illustrations of information and how this process can be made more efficient.
Tableau Software Tableau Software has won awards including "Best Overall in Data Visualization" by "DM Review", "Best of 2005 for Data Analysis" by "PC Magazine", and "2008 Best Business Intelligence Solution (CODiE award)" by the Software & Information Industry Association.
Information visualization Information visualization or information visualisation is the study of (interactive) visual representations of abstract data to reinforce human cognition. The abstract data include both numerical and non-numerical data, such as text and geographic information. However, information visualization differs from scientific visualization: "it’s infovis [information visualization] when the spatial representation is chosen, and it’s scivis [scientific visualization] when the spatial representation is given".
Biological data visualization Biology data visualization is a branch of bioinformatics concerned with the application of computer graphics, scientific visualization, and information visualization to different areas of the life sciences. This includes visualization of sequences, genomes, alignments, phylogenies, macromolecular structures, systems biology, microscopy, and magnetic resonance imaging data. Software tools used for visualizing biological data range from simple, standalone programs to complex, integrated systems.
Data visualization There are different approaches on the scope of data visualization. One common focus is on information presentation, such as Friedman (2008) presented it. In this way Friendly (2008) presumes two main parts of data visualization: statistical graphics, and thematic cartography. In this line the "Data Visualization: Modern Approaches" (2007) article gives an overview of seven subjects of data visualization:
Social Visualization There has been a long history of visualization in a social science perspective, which enables us to witness the power of social visualizations and their implications. However, changes in visualization methodology and tools in the last few decades are fundamentally affecting the way in which the social sciences and computational social science are researched, and in which studies are communicated (Olson 1997). These changes have been largely initiated by the rapid development of computing power and visualization technology since the 1980s, resulting in the availability of affordable computing and visualization. Many researchers had contributed to define and understand the potential power of this field with emerging media and information. In this regard, McCormick et al. indicates a visualization as offering "a method for seeing the unseen. (McCormick et al. 1989)" After that, many computer scientists dedicated their academic careers for nurturing the field of social visualization with strong emphasis on applying computational methods.
Flow visualization In scientific visualization flows are visualized with two main methods:
Data visualization KPI Library has developed the "Periodic Table of Visualization Methods," an interactive chart displaying various data visualization methods. It includes six types of data visualization methods: data, information, concept, strategy, metaphor and compound.
Data visualization Data visualization is closely related to information graphics, information visualization, scientific visualization, exploratory data analysis and statistical graphics. In the new millennium, data visualization has become an active area of research, teaching and development. According to Post et al. (2002), it has united scientific and information visualization.
The Fundamentals of Caring On December 7, it was announced that the original title, "The Revised Fundamentals of Caregiving", had been changed to "The Fundamentals of Caring". It was later revealed Bobby Cannavale and Frederick Weller had been cast in the film.