National Geographic Channel: Brain Games - Fun Facts About Perception Gestalt Priinciples: Proximity, Similarity, Enclosure, Closure, Continuity, Connection Week 3: Design Best practices and Exploratory Analysis Week 2: Visual Perception and Cognitive load Good Data Visualization should be boring: To optimize or satisfice in Data visualization: Types of Visualizations: Text/Number Visualization, 2x2 Table, Heatmap, Scatter plots (continuos variables), Line Graphs(time), Bar Graph(Horiontal, vertical, stacked, waterfall graphs), and maps for spatial analysis.ĭata Visualization Best Practices - Part 1: ĭata Visualization Best Practices- Part 2: Week 1: Effective and Ineffective visuals To optimize or to satisfice in data visualization? ĥ things to know about Web data Connectors: Why data visualization isn't optional:Anscombe's Quartet: Visualizations: Comparing Tableau, SPSS, R, Excel, Matlab, JS, Python, SAS: What data visualization tools do /r/DataIsBeautiful OC creators use? Roles associated with data analytics in larger organizations: Week 1: Getting started and introduction to data visualization Notes from Coursera's Tableau specialization To learn more about the specialization, check out a video overview at. It is the second course in the Data-Driven Decision Making (DDDM) specialization. It is helpful if learners have some familiarity with reading reports, gathering and using data, and interpreting visualizations. This course is ideal for individuals keen on developing a data-driven mindset that derives powerful insights useful for improving a company’s bottom line. Material features online lectures, videos, demos, project work, readings and discussions.

The course also addresses do’s and don’ts of presenting data visually, visualization software (Tableau, Excel, Power BI), and creating a data story. Deeper examination is spent on statistical process control (SPC), which is a method for studying variation over time. It begins with common hurdles that obstruct adoption of a data-driven culture before introducing data analysis tools (R software, Minitab, MATLAB, and Python). By the end of this course, learners are provided a high-level overview of data analysis and visualization tools, and are prepared to discuss best practices and develop an ensuing action plan that addresses key discoveries.

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