During the EARL (Enterprise Applications of the R Language) conference in London last week, the organizers asked me how I thought the conference had changed over the years. (This is the conference’s fifth year, and I’d been to each one.) My response was that it reflected the increasing maturity of R in the enterprise. The early years featured many presentations that were about using R in research and the challenges (both technical and procedural) for integrating that research into the day-to-day processes of the business. This year though, just about every presentation was about R in production, as a mainstream part of the operational infrastructure for analytics.
AI-Based Virtual Tutors – The Future of Education?
This post is co-authored by Chun Ming Chin, Technical Program Manager, and Max Kaznady, Senior Data Scientist, of Microsoft, with Luyi Huang, Nicholas Kao and James Tayali, students at University of California at Berkeley.
Distilled News
Let’s Think in Graphs: Introduction to Graph Theory and its Applications using Python
Whats new on arXiv
GritNet 2: Real-Time Student Performance Prediction with Domain Adaptation
✚ Chart Components and Working On Your Graphics Piece-wise
Before you can form a set of steps to visualize data, you need to know the components of a chart that you can separate. Like making an outline for an essay, you look for sections that make sense rather than define how many periods and question marks you need to show.
The Best Programming Languages for Data Science and Machine Learning in 2018
In 2018, the field of data science continues to expand at a pace previously unseen in all but the Full Stack Web Development cycle. As a result of faster processing chips and more readable programming languages, including package updates, today data science is becoming available for both consumers and professionals.
AI, Machine Learning and Data Science Roundup: September 2018
A monthly roundup of news about Artificial Intelligence, Machine Learning and Data Science. This is an eclectic collection of interesting blog posts, software announcements and data applications from Microsoft and elsewhere that I’ve noted over the past month or so.
What is P-value?
Every Data Scientist must have come across a question, What is P-value and how do we use it in our statistical analysis?
Data Notes: How Do Autoencoders Work?
Autoencoders, convolutions, and predicting Google’s stock price: Enjoy these new, intriguing, and overlooked datasets and kernels
Post-publication peer review: who’s qualified?
Gabriel Power writes: