Data Science in Real Life

Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real

• Identify strengths and weaknesses in experimental designs
• Apply innovative solutions for managing data
• Describe common pitfalls in communicating data analysis
• Understand roles of a data analysis manager