Data Science for ManagersData Science Management - Organizing teams for Predictive Analytics Development & Production Operations
Data Science for Managers is How You Stop Micro-Managing
There are not enough hours in the day for you to do your own job, much less the jobs of everyone below you. You need to feel confident that if you stop looking over their shoulders, your employees will continue working in a productive, expert fashion. This is where the power of Data Science for Managers comes into play. It’s not just about trust, it’s about trust in the process and your own ability to adequately measure it.
Key Performance Indicators and Metrics are no Longer Enough
Just knowing that John Doe didn’t meet his quota on widgets means absolutely nothing if you don’t know why he didn’t, whether he is improving or not, and at what rate. When you take Data Science for Managers, you not only learn how to answer these questions accurately, you gain an insight into the usefulness (or lack thereof) of the established KPIs and metrics, as well as developing new ones.
Performance Reviews Are No Longer a Pain
Assuming your company even uses performance reviews, they are probably your least favorite task. Ordinarily, no matter who “meets and exceeds” and who “needs improvement” in reality, someone is going to contest their rating, and often with good cause. However, when you have solid analytical skills to take a data-driven approach to the problem of ranking employees, it becomes far easier, and more justifiable. Divergence Academy’s Data Science for Managers is a one-day course that gives you the power and protection you need, in order to make your team and company meet and exceed its goals, before Performance Reviews even become due.
The Data Science for Managers is an offering uniquely focused on the needs of professionals in managerial positions in the data-driven world. Drawing on Divergence Academy’s practical expertise and real-world case studies, the class is designed to give professionals an understanding regarding where data relevant to decision-making can be found, how it can be harnessed, what wrangling is required to make it usable and how predictive or prescriptive models can be generated from it. The class will furthermore address how to use data in decision-making.