Goodbye, data science

This gives plenty of space for opportunists and tricksters to hide.

You don’t ever have to fear being beheaded by the people whose life savings you stole and you don’t have to face consequences if you have a good lawyer.

To do well in todays world learn all the rules and where the loop holes lie. Violating the spirit of the law is fine as long as you can lawyer around the letter of it.

Maybe a little harsh...

Contact hours at most universities are around 2-4 hours per week per 15-credit module. To gain a degree, you have to take 120 credits a year, typically two terms of 4 x 15 credit modules, or 8-16 hours of contact per week maximum with the entire summer off.

You therefore have at least 24 hours a week to study on your own to bring your working week up to 40 hours. Maybe you're working, fair enough. But if you don't have time to study subjects in depth then you need to reduce your working hours. If you can't, then by definition you are not a full-time student.

This is not a personal attack on you. Perhaps you were genuinely studious and spent all your time poring over the coursework. It is a commentary on the whole academic sector where we repeatedly see students do nothing for most of the time and spend the last 2 weeks cramming and putting in substandard assessments, then blame the course material/their lecturers/their anxiety etc. for their poor results. And of course the leadership teams lap it up and tell us to make our courses easier.

> Shitty code & shitty data science

In my opinion the bar should be higher for code quality but also general engineering know-how in data science. You'd be surprised how many are uncomfortable with git, using the command line, interacting with APIs, managing environments, etc. Being able to only work within a jupyter notebook is not good enough, at all. Otherwise, you end up with people who's entire job it is to productionize and deploy the code which is a waste of time and effort.

> Poor mentorship

There is either a lack of quality leadership and mentorship or an inability for upper management to see the value in hiring for it. What ends up happening is you have a team who doesn't know how to grow, scale, or work together. They instead focus on building models and learning statistics when they should be focusing on building systems and process for helping the business scale analytics and building models when appropriate.

I enjoyed data science but found it to also not matter in the implementation that everyone thinks it should be. Data science isn't building nothing but ML models. In most companies, in my opinion, it is actually about scaling analytics. Being able to reach further up into data engineering, get raw data, explore it, shape it, give it back to DE to automate, and then automate the delivery of data to upper management and guide them through using it. If the team thinks their job is to just build models, everyone is going to have a miserable, miserable time.