by TDS Team
In October 2016, we created a data science publication using Medium. Our goal was simply to gather good posts and distribute them to a broader audience. Just a few months later, we were pleased to see that we had a very fast growing audience and many contributors.
How Docker Can Help You Become A More Effective Data Scientist
by Hamel Husain – 14 min read
For the past 5 years, I have heard lots of buzz about docker containers. It seemed like all my software engineering friends are using them for developing applications.
Stranger Things: Five lessons for analyzing and communicating data
by Jordan Dworkin – 8 min read
As a graduate student in a statistical field, I quickly realized that people who don’t work with data often have one of two responses to the word statistics: "Oh, I hated that class!" and "You must really like math!".
What is the most effective way to structure a data science team?
By Chuong Do – 7 min read.
From 2012 to 2017, I had the privilege to build the Data and Analytics organization at Coursera from scratch. Over that period of time, we experimented with a variety of different team structures as the company grew in size and the business evolved.
Top Algorithms and Data Structures You Really Need To Know
by Jason Roell – 11 min read
If you want to become a software engineer, but don’t know where to start, let’s save you the suspense: it’s algorithms and data structures. Once you get the gist of these pillars of programming, you’ll start seeing them everywhere.
How To Ace Data Science Interviews: Statistics
by Carson Forter – 9 min read
For someone working or trying to work in data science, statistics is probably the biggest and most intimidating area of knowledge you need to develop. The goal of this post is to reduce what you need to know to a finite number of concrete ideas, techniques, and equations.
Gradient descent vs. neuroevolution
by Lars Hulstaert – 10 min read
In March 2017, OpenAI released a blog post on evolution strategies, an optimisation technique that has been around for several decades. The novelty of their paper was that they managed to apply the technique to deep neural networks in the context of reinforcement learning (RL) problems.
How to Make Technical Data Simple and Beautiful
by Payman Taei – 10 min read
Data is the supportive brick for any presentation or report out there. It offers bits of reality that together form an accurate image. Without data, marketers would create their campaigns on assumptions and suppositions, instead of knowing exactly what their clients want.
Interactive Data Science with Jupyter Notebooks
by Yufeng G – 6 min read
In my videos, you’ve seen me running Python code live on screen and showing the results. Today, I want to share with you how I’ve been doing this, and show how you can take advantage of it too!
Machine Learning for Diabetes
by Susan Li – 9 min read
About one in seven U.S. adults has diabetes now, according to the Centers for Disease Control and Prevention. But by 2050, that rate could skyrocket to as many as one in three. With this in mind, this is what we are going to do today: Learning how to use Machine Learning to help us predict Diabetes. Let’s get started!
Velocious Vehicles and How to Track Them
by Béthy – 7 min read
Since post graduation I’ve made it a goal to explore topics that I haven’t had the chance to explore during class. I’ve made it a goal to read two articles a day about the latest in AI technologies (i.e. machine learning, deep learning, AI uses in gaming, the list goes on).
How I used machine learning to classify emails and turn them into insights (part 2).
by Anthony Dm. – 5 min read
It’s been a while since I wrote part 1, during lots of projects past year I couldn’t find the time and effort to continue where I left of. While I’m writing part 2, Christmas holidays are getting closer, giving me some spare time to continue my research.
Building a Similar Images Finder without any training!
by Anson Wong – 4 min read
In this article, we will build a similar images finder by dissecting the trained weights of the image object-classifier VGG and using it to extract feature vectors from an image database to see which images are "similar" to each other. This technique is called transfer learning and requires no training on our end – the hard work was done back in the day when VGG was actually being trained, we are just re-using the trained weights to build a new model.
Why your analytics project will fail to deliver exponential value
by Jesse Paquette – 5 min read
So you’ve finished building an analytics dashboard on top of your data warehouse – congrats! You’re now moving ahead on some exciting predictive/machine learning initiatives, and that’s great, but before you close the book on analytics there are some things you should know.
Separating the Art of Medicine from Artificial Intelligence
by Hugh Harvey – 8 min read
Artificial intelligence requires data. Ideally that data should be clean, trustworthy and above all, accurate. Unfortunately, medical data is far from it. In fact medical data is sometimes so far removed from being clean, it’s positively dirty.
We thanks our Patreon supporters for making our work possible ☺️
Chuan-Heng(Henry) Lin, Chris Mangum, Piyush Routray, Choong Hong Cheng, Jayaram Nair, Paavo Pohndorff, Shannon Horn, Carson Forter, Harel Weinstein, Heasun Choung, Michael D Schrage, Jerica Copeny, Denny Rommel, Anna Anisin, Raman Gupta, Stephen Yip, Wagner Rodeski , Aiko Yamashita, Vineet Agarwal, Dominic Monn, Jorge Cunha, Ashwini Sarode, Shufang LIU, Wasif Huda, Yuval Blau, Wendy Wong.
