Did you ever have the best data science experience? Everything went smoothly during data pull. There were no missing or merging errors. Prior to analysis, hypotheses were clearly identified. Randomization was used to treat interest. Before analysis, the analytic plan was laid out and then followed exactly. The results were clear and made it easy to take action. Have you ever experienced something like that? No. Data analysis in real-life is messy. How can one manage a team that is facing real data analysis? This course will compare the ideal and what actually happens in real life. You will be able to apply key concepts to real-life analyses by contrasting the ideal.
This course is focused on getting you up to speed quickly in data science. We wanted to make it as easy as possible without sacrificing any important content. So that you can concentrate on the management of your team and driving it forward, we've left out technical details. This course will teach you how to: 1. Describe the ideal data science experience 2. Identify the strengths and weaknesses of experimental designs. Learn how to manage data pulls and identify potential pitfalls in pulling/assembling data. 4. Refute statistical modeling assumptions and provide feedback to data analysts Identify common pitfalls when communicating data analyses. A glimpse into the day-to-day life of a data analyst manager. This course is designed for data scientists and statisticians who are active managers. The following are key concepts that will be discussed: Experimental design, randomization, A/B testing 2. Counterfactuals, causal inference and counterfactuals. Strategies for managing data quality. 4. Bias and confounding 5. Contrasting machine learning versus classical statistical inference Course promo: https://www.youtube.com/watch?v=9BIYmw5wnBI Course cover image by Jonathan Gross. Creative Commons BY-ND https://flic.kr/p/q1vudb
