Start Date: 07/05/2020
Course Type: Common Course
Course Link: https://www.coursera.org/learn/analytics-tableauExplore 1600+ online courses from top universities. Join Coursera today to learn data science, programming, business strategy, and more.
One of the skills that characterizes great business data analysts is the ability to communicate practical implications of quantitative analyses to any kind of audience member. Even the most sophisticated statistical analyses are not useful to a business if they do not lead to actionable advice, or if the answers to those business questions are not conveyed in a way that non-technical people can understand. In this course you will learn how to become a master at communicating business-relevant implications of data analyses. By the end, you will know how to structure your data analysis projects to ensure the fruits of your hard labor yield results for your stakeholders. You will also know how to streamline your analyses and highlight their implications efficiently using visualizations in Tableau, the most popular visualization program in the business world. Using other Tableau features, you will be able to make effective visualizations that harness the human brain’s innate perceptual and cognitive tendencies to convey conclusions directly and clearly. Finally, you will be practiced in designing and persuasively presenting business “data stories” that use these visualizations, capitalizing on business-tested methods and design principles.
The Coursera Specialization: Excel to MySQL: Analytic Techniques for Business, is about how 'Big Data' interacts with business, and how to use data analytics to create value for businesses. This specialization consists of four courses and a final Capstone Project, where you will apply your skills to a real-world business process. You will learn to perform sophisticated data-analysis functions using powerful software tools such as Microsoft Excel, Tableau, and MySQL. To learn more, watch the video and review the specialization overview document we provided.
In the third course of the specialization: Data Visualization and Communication with Tableau, you will learn how to communicate business-relevant implications of data analyses.
Specifically, you will:
By the end of this course, you will know how to structure your data analysis projects to ensure the fruits of your hard labor yield results for your stakeholders. You will also know how to streamline your analyses and highlight their implications efficiently using visualizations in Tableau, the most popular visualization program in the business world. Using other Tableau features, you will be able to make effective visualizations that harness the human brain’s innate perceptual and cognitive tendencies to convey conclusions directly and clearly. Finally, you will be practiced in designing and persuasively presenting business “data stories” that use these visualizations, capitalizing on business-tested methods and design principles by completing a final peer assessed project recommending a business process change.
To get started, please begin with the video 'About This Specialization.'
I hope you enjoy this week's materials!
Data Visualization and Communication with Tableau Project In this project-based course, you will design and complete a completely realistic visualization project. You will work with a design-savvy group of students and a professional graphic artist to complete this project. You will have the opportunity to work with a fully functional visualization system and design the visualization design to suit the needs of the clients and data. You will learn the ins and outs of data visualization systems and how to design a design for each system. You will also learn the ins and outs of structuring a project plan and how to make it flexible. Note: The project will require some basic knowledge of programming and data visualization.You will need to download and install the free application BlockTree from https://www.dropbox.com/s/dmt3jr/BlockTree-v2.1.zip?dl=0&appid=1000&st=preview&tabid=p&referrer_id=https://www.dropbox.com/s/dmt3jr/BlockTree-v2.1.zip&dl=0&appid=1000&st=preview&tabid=p&referrer_id=https://www.dropbox.com/s/dmt3jr/BlockTree-v2.1.zip&dl=0&appid=1000&st=preview&tabid=p&referrer_id=https://
|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.|
|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||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:|
|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||Data visualization or data visualisation is viewed by many disciplines as a modern equivalent of visual communication. It involves the creation and study of the visual representation of data, meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information".|
|Data visualization||Data visualization involves specific terminology, some of which is derived from statistics. For example, author Stephen Few defines two types of data, which are used in combination to support a meaningful analysis or visualization:|
|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||Historically, the term "data presentation architecture" is attributed to Kelly Lautt: "Data Presentation Architecture (DPA) is a rarely applied skill set critical for the success and value of Business Intelligence. Data presentation architecture weds the science of numbers, data and statistics in discovering valuable information from data and making it usable, relevant and actionable with the arts of data visualization, communications, organizational psychology and change management in order to provide business intelligence solutions with the data scope, delivery timing, format and visualizations that will most effectively support and drive operational, tactical and strategic behaviour toward understood business (or organizational) goals. DPA is neither an IT nor a business skill set but exists as a separate field of expertise. Often confused with data visualization, data presentation architecture is a much broader skill set that includes determining what data on what schedule and in what exact format is to be presented, not just the best way to present data that has already been chosen. Data visualization skills are one element of DPA."|
|Data visualization||There is a history of data visualization: beginning in the 2nd century C.E. with data arrangement into columns and rows and evolving to the initial quantitative representations in the 17th century. According to the Interaction Design Foundation, French philosopher and mathematician René Descartes laid the ground work for Scotsman William Playfair. Descartes developed a two-dimensional coordinate system for displaying values, which in the late 18th century Playfair saw potential for graphical communication of quantitative data. In the second half of the 20th century, Jacques Bertin used quantitative graphs to represent information "intuitively, clearly, accurately, and efficiently".|
|Data visualization||Data visualization is both an art and a science. It is viewed as a branch of descriptive statistics by some, but also as a grounded theory development tool by others. Increased amounts of data created by Internet activity and an expanding number of sensors in the environment are referred to as "big data" or Internet of things. Processing, analyzing and communicating this data present ethical and analytical challenges for data visualization. The field of data science and practitioners called data scientists help address this challenge.|
|Data visualization||Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines or bars) contained in graphics. The goal is to communicate information clearly and efficiently to users. It is one of the steps in data analysis or data science. According to Friedman (2008) the "main goal of data visualization is to communicate information clearly and effectively through graphical means. It doesn't mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key-aspects in a more intuitive way. Yet designers often fail to achieve a balance between form and function, creating gorgeous data visualizations which fail to serve their main purpose — to communicate information".|
|Dundas Data Visualization||Dundas Data Visualization, Inc. is a company specializing in data visualization and dashboard solutions. In addition to developing enterprise-level dashboard software, Dundas offers a professional services group that provides consulting and training.|
|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".|
|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.|
|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.|
|Interactive data visualization||In statistics, interactive data visualization enables direct actions on a plot to change elements and link between multiple plots.|
|Data visualization||A primary goal of data visualization is to communicate information clearly and efficiently via statistical graphics, plots and information graphics. Numerical data may be encoded using dots, lines, or bars, to visually communicate a quantitative message. Effective visualization helps users analyze and reason about data and evidence. It makes complex data more accessible, understandable and usable. Users may have particular analytical tasks, such as making comparisons or understanding causality, and the design principle of the graphic (i.e., showing comparisons or showing causality) follows the task. Tables are generally used where users will look up a specific measurement, while charts of various types are used to show patterns or relationships in the data for one or more variables.|
|Data visualization||Almost all data visualizations are created for human consumption. Knowledge of human perception and cognition is necessary when designing intuitive visualizations. Cognition refers to processes in human beings like perception, attention, learning, memory, thought, concept formation, reading, and problem solving. Human visual processing is efficient in detecting changes and making comparisons between quantities, sizes, shapes and variations in lightness. When properties of symbolic data are mapped to visual few properties, humans can browse through large amounts of data efficiently. It is estimated that 2/3 of the brain's neurons can be involved in visual processing. Proper visualization provides a different approach to show potential connections, relationships, etc. which are not as obvious in non-visualized quantitative data. Visualization can become a means of data exploration.|
|Biological data visualization||Finally, as datasets are increasing in size, complexity, and interconnectness, biological visualization systems are improving in usability, data integration and standardization.|