Content touches on leadership’s role in instituting an internet of things (IoT) strategy in manufacturing and service environments. Course topics include analyzing process maps for driving improvement, software for maximizing data analysis, statistical process control, creating metrics dashboards and translating data stories, and the connection between operations technology metrics and organizational performance. The three-course series explores how technology enables the collection and organization of unprecedented amounts of data, and how to dissect that data to gain powerful insights. This specialization explains why it is important to leverage data when contemplating organizational choices, and supplies the tools at the heart of data-driven decision making (DDDM). 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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