A Data Science Central Community
This is our selection of featured articles for today. There was a lot of activity (new contributions) over the weekend, and more coming soon.
What Types of Questions Can Data Science Answer? - Guest blog post, authored by Brandon Rohrer , Senior Data Scientist at Microsoft. Machine learning (ML) is the motor that drives data science. Each ML method (also called an algorithm) takes in data, turns it over, and spits out an answer. ML algorithms do the part of data science that is the trickiest to explain and the most fun to work with. That’s where the mathematical magic happens. ML algorithms can be grouped into families based on the type of question they answer. These can help guide your thinking as you are formulating your razor sharp question. Read More.
Implemetation of 17 classification algorithms in R - This long article with a lot of source code was posted by Suraj V Vidyadaran. Suraj is pursuing a Master in Computer Science at Temple university primarily focused in Data Science specialization. His areas of interests are in sentiment analysis, data visualization, big data and machine learning. This data is obtained from the UCI machine learning repository. Read More.
Performance From Various Predictive Models - Guest blog post by Dalila Benachenchou. Dalila is Professor at George Washington University. In this article, benchmarks were computed on a specific data set, for Geico Calls Prediction, comparing Random Forests, Neural Networks, SVM, FDA, K Nearest Neighbors, C5.0 (Decision Trees), Logistic Regression, and Cart. Read More.
How to Use Cohort Data to Analyze User Behavior - In the world of data analysis, one tool is often left unused. While being a very powerful analytics tool, cohorts are often pushed aside due to their seemingly complex nature. With a lot to offer in the way of data analysis, let’s take a deeper (yet simplified) look into cohorts. Read More.