Mathematicians becoming data scientists

Second, programmers at companies almost never work in isolation from other programmers, and in most open office environments are close enough they could touch another one from their desk. On the other hand, a startup with five to ten engineers may be hiring you as their first data scientist, and bigger companies may be putting you on an embedded team, with the nearest data scientist a hallway or floor away. And this isn't a big deal in terms of teamwork, but most data scientists don't have a PhD in mathematics, so if you find higher level mathematical ideas and notation to be the most efficient way for you to think about a problem, your colleagues may not. That's also not a math specific thing though -- an economist, statistician, mathematician, computer scientist, physicist, etc. are all going to have slightly different ways they think about things internally.

When you interface with non-specialists, you can't rely on the shared specialized vocabulary. This means you have to do a lot of work to distill which insights are key and figure out how to communicate them (along with supporting ideas) to an audience whose highest insight resolution is going to be varying and who may each bring their own language to the table.

So on top of building your problem domain model, you're going to have to build a model of how the people you're working with can understand it. And you will have to do this over and over.

You can be snobby about that, and say that it's soooo hard being so much smarter in your specialized field than non-specialists are, but you don't have to be snobby to realize that this is a dimension of the work that you might not enjoy or may even not be cut out for.

And of course, you don't have to be a mathematician to have experienced this. It's certainly sufficient, but not necessary. If you've been a developer in a company that has non-dev coworkers and you've never hit this turbulent boundary, you've either been remarkably well insulated, or you're so remarkably natural at that kind of job that you definitely should be doing it. :)

Register as a Twitter dev (free) and get the tokens, etc. needed to access the public API. Pick a topic of interest -- hockey, for example, or whatever floats your boat -- and write scripts to harvest all tweets coming off of the public API related to the topic. Design a relational schema for the tweets and push them into a SQLite database. I suggest SQLite because it's ubiquitous and has a low barrier to entry. Something like MongoDB also works well for dumping everything straight off of the API. You can then have another script pull out of MongoDB and push into SQLite, for example. Once you're collecting tweets, storing them, etc... try some unsupervised clustering; k-means, for example, or a decision tree. If you feel up to it, go through 1000 or so of the tweets manually and label them according to some target variable of interest to the project. Then, use that labeled data set to run supervised models. Maybe start with a binary target variable and run a simple logistic regression. Then, visualize the data. There are a lot of ways to go about this, but I suggest trying to use something JavaScript based, such as D3 or p5.js, since it allows you to create interactive web-based visuals. Create a public GitHub repo and push work to it as you progress through the project. When done, use GitHub pages to put a summary of the project online.

Twitter data is great because there are tons of variables. It's horrifying because it's like reading the refuse of language, littered with abbreviations, emoji, and other weird characters. However, it's the terrible part of it that makes it great for learning.

There are other similar public APIs that would accommodate similar projects. Having a few self-initiated projects similar to this under your belt will really help you when it comes time to apply to graduate school or to jobs. If nothing else, it will give you something to talk about in interviews.

The reason for the MBA was so she had a sense of the business value of the analyses she was performing, and, was more 'promotable' to executive positions.

As mentioned separately, I suggested a data science boot camp after getting the Stats BS, work a couple of years as a data scientist, then go back for the MBA. My thinking RE the MBA was to give a sense of the business value of the analyses she's performing, and, to make it easier to promote her to executive positions.

Maybe that's old school thinking, I know that the MBA in general gets a mixed reception these days, but we can look more closely at it after she's out of school.