Mathforbusiness Course Site

We will introduce you to the different growth stages companies have, and the qualitative and quantitative information they need in said stages. We will walk you through Sean Ellis, founder of dropbox’s product-market fit roadmap, and teach you exactly what metrics and qualitative information is needed to advance in the business growth pyramid , from product market fit, to transition of growth and viral growth. You will understand exactly what information needs to be gathered and how to obtain it. This knowledge will proove vital for being able to adapt yourself in the future to any business and industry you might work in, and being able to properly audit knowledge architectures and fill any knowledge gaps that your company might have. It will also proove to be essential to maximize your feature engineering capabilities.

We will show you a framework to structure any possible data science project you might have to work, to maximize it’s chances of success. This framework will also allow you to make sure that “success” is aligned with actually providing value to the company, hopefully ROI. We will also show you the most comprehensive list of possible data science and math applications used across multiple different industries, so you have an exhaustive knowledge of all the actual possibilities math holds for businesses. This will allow you to diagnose effectively what possible projects your company can tackle with hopes of success, and how to start working said projects.

Any respectable one-man army data scientist should also have the engineering knowledge to make its solutions ready for productization in a scalable, reproducible, and robust manner. Here we will teach you everything you need in the ML-Ops methodology so you can start making your projects production-ready and reproducible, as any true business-driven data scientist should aspire to.

A very self-explainable title. For me, feature engineering is probably one of the two most critical skills a data scientist could have, and the one that actually names the profession. This chapter we will tackle how to take your feature engineering skills to the next level to be elite level, specially when working dynamic and chaotic systems where humans play a key role in it’s outcomes (meaning: any system where business stakeholders are involved).

The other critical skill a data scientist should have. There is no data-driven strategy if you can’t actually demonstrate hypothesis and create tests that proove and reduce risk in the decision making process. In this chapter we will make you a true statistician capable of making your company truly data-driven.

How to lead a data science project, how to manage it’s risks, potential outcomes, business expectations and stakeholders, we will cover in this chapter all the essential soft skills a data scientist needs, and we will teach you how to effectively integrate with the company culture. No matter how good a data scientist you are, you are not transforming the company on your own, you will have to win departments and coworkers over and sell them on your vision and the benefits of being data-driven. This chapter will guide you.