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Data Analysis and Visualization

University at Buffalo and State University of New York via Coursera


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. It begins with common hurdles that obstruct adoption of a data-driven culture before introducing data analysis tools (R software, Minitab, MATLAB, and Python). Deeper examination is spent on statistical process control (SPC), which is a method for studying variation over time. The course also addresses do’s and don’ts of presenting data visually, visualization software (Tableau, Excel, Power BI), and creating a data story.

Material features online lectures, videos, demos, project work, readings and discussions. 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. It is helpful if learners have some familiarity with reading reports, gathering and using data, and interpreting visualizations. It is the second course in the Data-Driven Decision Making (DDDM) specialization. To learn more about the specialization, check out a video overview at


  • Data Analysis Software Tools
    • This module provides an overview of the tools needed for data analysis.
  • Statistical Process Control (SPC)
    • This module covers SPC, a way to analyze variation over time in your process using data. It is helpful in identifying current problems and can also be used to monitor the process for any deviations once the process is ‘in control'.
  • Data Visualization and Translation
    • This module provides tools for leveraging data through visualization and translation.
  • Project: Data Analysis and Visualization
    • This module provides an opportunity to bridge theory and practice. Learners apply knowledge from this course to solve a business problem.

Taught by

Peter Baumgartner and Brittany O'Dea

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