Statistics for data science teams and leadership
Data science, Sigma Six, analytics, and business intelligence are all different sides of a multi-sided polygon. Each has different tools, vocabularies, projects, and certifications.
However, they all serve the business to reduce costs and increase revenues. These are practical tools that help businesses be more effective at what they do!
And each comes with their own new styles of management practices and necessities for leadership to understand the actual value that can be gained from properly utilizing these tools.
We wanted to help create a quick guide to help management and refresh data scientists memories on some of the concepts that data science utilizes.
If you are a data scientist, you should have at least a basic understanding of statistics. You just need to be able to describe a few basic algorithms at a dinner party.
We want to arm you with concepts, equations, and theorems that will make it sound like you aced your advanced statistical computing course in college.
For instance, what is the probability density function? What about a joint distribution function and the role they play in modern data science?
The key to knowing any subject well is understanding it’s base parts.
Libraries like scki-learn and tensorflow abstract almost all the complex math away from the user. This is great, in the sense that you don’t have to worry about accidentally forgetting to carry the one or remember how each rule in calculus operates.
It is still great to have a general understanding of some of the equations you can utilize, distributions you can model and general statistics rules that can help clean up your data!