Philippe Barbe

If you are a data scientist with some experience, you lived through this: the work if not going well. One bug fixed reveals another one. What was seemingly well designed encounters unexpected data points which question the assumptions made.

Most Data Scientists I interview do not want to talk about data, or science, but only about business.

It all started 1 year ago, with the startup where I was leading the Data Science team shutting down, necessitating a job search for me.

“Why?” is a question about making sense of things: Why are we doing this project? Why are you offering me that compensation? Why are you asking me these questions?

The title of this article, pick a technical topic that you know really well, and tell me about it, is my request to every Data Science candidate I interview. Why I will make that request is explained in my previous article.

I am sometimes asked what questions I ask in technical interviews. Over the years I have seen many interesting questions that reveal skills and knowledge.

I’ve interviewed a fair number of candidates over the years and have been interviewed several times.

The old grinder is still in the barn, ready to sharpen whatever farming tool was well used. A sharpening wheel, a couple gears, a handle, a base, these are all the parts.

The wall is 900 years old.

I own a third of an oven.

The buildings have been standing since the 12th century, moss decorating the stones, adding a green touch to the gray granite. The village is quiet.

There is a pleasure in doing. The work is getting done. Something tangible is appearing. We are accomplishing something. Our progress can be measured, hence managed!

Over the past 30 years, I’ve worked with a lot of people.

I’ve met many “leaders” and have found that descriptor is over used. In many companies, anyone who has reports, a “manager”, is deemed to be a leader.

What makes you a leader?

The term Data Science is often attributed to computer scientist Peter Naur who used it in 1974, and statistician Jeff Wu who popularized it around 1997. `

Search for best practices for documenting complex Data Science projects and you although you find many articles and books about how to document Data Science projects, few will address documentation of large, complex Data Science projects.

If you drop a penny from the height of your waist, it will take slightly less than half a second to hit the floor.

Several press outlets are reporting on what’s being called “The Great Resignation” based on the numbers of resignations happening in corporate America right now. Similar to what happened during the Great Recession of 2008, the Covid-19 pandemic saw many people displaced from their roles and those who weren’t clung tightly to the job they had.

The work of a Data Scientist is to build and apply knowledge and systems that produce insights to support decision making. The primary tool used by Data ScieThe work of a Data Scientist is to build and apply knowledge and systems that produce insights to support decision making. The primary tool used by Data Scientists to do this work is computer code.

In 1999, the Bureau of Labor Statistics predicted that 5% of children in grade school at that time would end up in jobs that hadn’t yet been created. And, the Institute for the Future predicted in 2017 that around 85% of the jobs that today’s learners will be doing in 2030 haven’t been invented yet. Other predictions see 30% of the workforce changing careers every 12 months.

If all you have is hammer, everything looks like a nail.

If you are going to hire a professional mathematician for all the non-technical skills that were described in parts 2 and 3 of this series, for the reason explained in part 4, how will you go about it?

As described in part 1 on this series of articles, the work of a professional mathematician is very different than what most people think. It relies on skills that go far beyond the obvious one of being good at math. As indicated in parts 2 and 3, these skills are not only highly transferable, but also highly valuable.

Business is complex!

No business I know of is in the business of proving theorems! But, a lot a businesses do benefit from hiring Mathematicians or I know a lot more who could.

What do Mathematicians actually do and why, as a business person, should you care?

Few months ago, I needed to learn about the Trucking industry in order to understand how Data Science could be applied to improve decision making and transactional systems in that industry.

Have you ever wondered how others think?

Programmatic television advertising (“programmatic”) is a catchy term for the automation of the buying and selling of ads on linear television. A concept that has been around for several years, the idea is to give advertisers in the traditional media space the analogue of tools available for digital advertising.

Cookies are little files that are stored on your computer when you browse the internet. When you log onto a website, a cookie is stored on your computer that allows you to navigate on that website without retyping your password every time you click something. Different types of internet cookies exist with different purposes.

Part 20 of 20 in a series examining the interplay of Data Science, AI, the media and advertising.

Part 19 of 20 in a series examining the interplay of Data Science, AI, the media and advertising.

Part 18 of 20 in a series examining the interplay of Data Science, AI, the media and advertising.

Part 17 of 20 in a series examining the interplay of Data Science, AI, the media and advertising.

Part 16 of 20 in a series examining the interplay of Data Science, AI, the media and advertising.

Part 15 of 20 in a series examining the interplay of Data Science, AI, the media and advertising.

Part 14 of 20 in a series examining the interplay of Data Science, AI, the media and advertising.

Part 13 of 20 in a series examining the interplay of Data Science, AI, the media and advertising.

Part 12 of 20 in a series examining the interplay of Data Science, AI, the media and advertising.

Part 11 of 20 in a series examining the interplay of Data Science, AI, the media and advertising.

Part 10 of 20 in a series examining the interplay of Data Science, AI, the media and advertising.

Part 9 of 20 in a series examining the interplay of Data Science, AI, the media and advertising.

Part 8 of 20 in a series examining the interplay of Data Science, AI, the media and advertising.

Part 7 of 20 in a series examining the interplay of Data Science, AI, the media and advertising.

Part 6 of 20 in a series examining the interplay of Data Science, AI, the media and advertising.

Part 5 of 20 in a series examining the interplay of Data Science, AI, the media and advertising. Thus far in this series we’ve examined how:

Part 4 of 20 in a series examining the interplay of Data Science, AI, the media and advertising.

Part 3 of 20 in a series examining the interplay of Data Science, AI, the media and advertising.

Part 2 of 20 in a series examining the interplay of Data Science, AI, the media and advertising.

This is the first in a 20-part series of articles delving into the role of Data Science and AI in Media and Advertising that expands on presentations I made in the US, France and China. Future articles will address a range of related topics.

I’m not exactly sure why (perhaps because I successfully moved from academia to business?) but I am often asked by colleagues, former employees, and sometimes recent graduates for career advice and what to look for in their next professional move.

Seeking my feedback, a CEO stated the strategy for his very large corporation as: “We will continue to nurture our cash generating businesses and invest in adjacent spaces.”

It is said that Data Science is a combination of statistics, computing, and business… three disciplines with underlying core principles.