Data Analysis and Visualization

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.

• Identify stakeholders and key components imperative to an analytics project plan
• Name strengths and weaknesses of different analysis and visualization tools
• Visually identify, monitor, and remove process variation
• Explain how to create a compelling data story