In this monthly addition, we wanted to look back at the year’s most influential articles (which you can find listed below). You can find the titles of these articles visualized as a word cloud above. As one might expect, words such as machine, learning, and data are very common. Python also had a very strong showing this year and seemed to be the language of choice for our writers.
Another trend centered around introductory articles. With "beginner’s guides", "simple hacks", and "first year" posts, some of our most influential articles were targeted at those new to the field or looking for easy to understand introductions.
Our community also enjoyed content which dove deep into applied use cases of data science. From stock market prediction to rare event classification, articles which showed practical application were among some of the best.
As we move into 2020, we are excited to read all the new amazing content from our community and look forward to seeing what new trends and topics will emerge.
Tyler Folkman, TDS Editorial Associate.
Turn Python Scripts into Beautiful ML Tools
By Adrien Treuille – 7 min read
Introducing Streamlit, an app framework built for ML engineers
Everything a Data Scientist Should Know About Data Management*
By Phoebe Wong – 19 min read
(*But Was Afraid to Ask)
Introduction to Decision Intelligence
By Cassie Kozyrkov – 13 min read
A new discipline for leadership in the AI era
Becoming a Level 3.0 Data Scientist
By Jan Zawadzki – 9 min read
Want to be a Junior, Senior, or Principal Data Scientists? Find out what you need to do to navigate the Data Science Career Game.
Extreme Rare Event Classification using Autoencoders in Keras
By Chitta Ranjan – 10 min read
In this post, we will learn how to implement an autoencoder for building a rare-event classifier. We will use a real-world rare event dataset
The Next Level of Data Visualization in Python
By Will Koehrsen – 8 min read
How to make great-looking, fully-interactive plots with a single line of Python
Beginner’s Guide to Machine Learning with Python
By Oleksii Kharkovyna – 10 min read
Machine Learning, a prominent topic in Artificial Intelligence domain, has been in the spotlight for quite some time now.
12 Things I Learned During My First Year as a Machine Learning Engineer
By Daniel Bourke – 11 min read
Being your own biggest sceptic, the value in trying things which might not work and why communication problems are harder than technical problems.
Using the latest advancements in deep learning to predict stock price movements
By Boris B – 34 min read
In this notebook I will create a complete process for predicting stock price movements.
Understanding Random Forest
By Tony Yiu – 9 min read
How the Algorithm Works and Why it Is So Effective
Data Science is Boring
By Ian Xiao – 9 min read
How I cope with the boring days of deploying Machine Learning
10 Simple hacks to speed up your Data Analysis in Python
By Parul Pandey – 8 min read
Tips and Tricks, especially in the programming world, can be very useful.
The Actual Difference Between Statistics and Machine Learning
By Matthew Stewart, PhD Researcher – 15 min read
No, they are not the same. If machine learning is just glorified statistics, then architecture is just glorified sand-castle construction.
A beginner’s guide to Linear Regression in Python with Scikit-Learn
By Nagesh Singh Chauhan – 11 min read
There are two types of supervised machine learning algorithms: Regression and classification.
We also thank all the great new writers who joined us recently, Anton Muehlemann, Chayan Kathuria, Alex Kim, Xinran Waibel, Brandon Walker, Brenda Hali, Timothy Tan, James Briggs, Rebecca Mqamelo, Rıza Özçelik, Warwick Rommelrath, Conor Lazarou, Lauren Kemp, Serena Peruzzo, Catherine Wang, Jenny Lee, Inna Tokarev Sela, Michael Poli, Nivedhitha Mathan Kumar, Wallyson De Oliveira, Jiahui Wang, Adrian Yijie Xu, Jason Boog, and many others. We invite you to take a look at their profiles and check out their work.
