Towards Data Science

When I think of the trends I’ve seen in data science over the last few years, perhaps the most significant and hardest to ignore has been the increased focus on deployment and productionization of models. Not all companies need models deployed to production, of course but at those that do, there’s increasing pressure on data science teams to deliver software engineering along with machine learning solutions.

That’s why I wanted to sit down with Adam Waksman, Head of Core Technology at Foursquare. Foursquare is a company built on data and machine learning: they were one of the first fully scaled social media-powered recommendation services that gained real traction, and now help over 50 million people find restaurants and services in countries around the world.

Our conversation covered a lot of ground, from the interaction between software engineering and data science, to what he looks for in new hires, to the future of the field as a whole.

In this podcast interview, YK (CS Dojo) interviews Chanchal Chatterjee, who’s an AI leader at Google. 

Podcast interview with one of our top data science writers, Will Koehrsen.

Let’s go!  Here’s Will’s article about what he learned from writing a data science article every week for a year: https://towardsdatascience.com/what-i-learned-from-writing-a-data-science-article-every-week-for-a-year-201c0357e0ce

This episode was hosted by YK from CS Dojo: https://www.instagram.com/ykdojo/

Getting hired as a data scientist, machine learning engineer or data analyst is hard. And if there’s one person who’s spent a *lot* of time thinking about why that is, and what you can do about it if you’re trying to break into the field, it’s Edouard Harris.

Ed is the co-founder of SharpestMinds, a data science mentorship program that’s free until you get a job. He also happens to be my brother, which makes this our most nepostistic episode yet.

If there’s one trend that not nearly enough data scientists seem to be paying attention to heading into 2020, it’s this: data scientists are becoming product people.

Five years ago, that wasn’t the case at all: data science and machine learning were all the rage, and managers were impressed by fancy analytics and build over-engineered predictive models. Today, a healthy dose of reality has set in, and most companies see data science as a means to an end: it’s way of improving the experience of real users and real, paying customers, and not a magical tool whose coolness is self-justifying.

At the same time, as more and more tools continue to make it easier and easier for people who aren’t data scientists to build and use predictive models, data scientists are going to have to get good at new things. And that means two things: product instinct, and data storytelling.

That’s why we wanted to chat with Nate Nichols, a data scientist turned VP of Product Architecture at Narrative Science — a company that’s focused on addressing data communication. Nate is also the co-author of Let Your People Be People, a (free) book on data storytelling.

In this podcast episode, Helen Ngo and YK (aka CS Dojo) discuss deep fake, NLP, and women in data science.

In this podcast interview, YK (aka CS Dojo) asks Ian Xiao about why he thinks machine learning is more boring than you may think. 

Original article: https://towardsdatascience.com/data-science-is-boring-1d43473e353e 

The other day, I interviewed Jeremie Harris, a SharpestMinds cofounder, for the Towards Data Science podcast and YouTube channel. SharpestMinds is a startup that helps people who are looking for data science jobs by finding mentors for them.

In my opinion, their system is interesting in a way that a mentor only gets paid when their mentee lands a data science job. I wanted to interview Jeremie because I had previously spoken to him on a different occasion, and I wanted to personally learn more about his story, as well as his thoughts on today’s data science job market.

Hi! It's YK here from CS Dojo. In this episode, I interviewed Jessica Li from Kaggle about how she worked with NASA to predict snowmelt patterns using deep learning. Hope you enjoy!

One question I’ve been getting a lot lately is whether graduate degrees — especially PhDs — are necessary in order to land a job in data science. Of course, education requirements vary widely from company to company, which is why I think the most informative answers to this question tend to come not from recruiters or hiring managers, but from data scientists with those fancy degrees, who can speak to whether they were actually useful.

That’s far from the only reason I wanted to sit down with Rachael Tatman for this episode of the podcast though. In addition to holding a PhD in computational sociolinguistics, Rachael is a data scientist at Kaggle, and a popular livestreaming coder (check out her Twitch stream here). She’s has a lot of great insights about breaking into data science, how to get the most out of Kaggle, the future of NLP, and yes, the value of graduate degrees for data science roles.