February Meetups:
Machine Learning Working Group: Saturday, 06 February
3:00 pm at 757 Creative Space, 259 Granby St. Suite 250, downtown Norfolk.
This is a hands-on working group for those interested in working with the underlying algorithms and code that will be discussed at the Tuesday night Tidewater Analytics meeting.
Tidewater Analytics: Tuesday, 09 February
7:00 pm at 757 Creative Space, 259 Granby St. Suite 250, downtown Norfolk.
This is the second month of our machine learning track. The topic this month is classification trees. This will be more of an educational presentation of the topic. Those interested in hands-on work with the algorithms and code should attend the working group meeting on Saturday, 06 February.
757 R User’s Group: Tuesday, 16 February
6:30 pm at 757 Creative Space, 259 Granby, Suite 250, downtown Norfolk.
“Open Mic Night”: an opportunity for everyone to share their favorite tips and tricks…ask questions about specific issues in R that have been vexing them…and discuss future topics.
Try.Py – Learn Python: Wednesday, 17 February
7:00 pm at The Hatch, 111 Granby, downtown Norfolk
Great news! Two new Python User Groups are starting up. Jay Gendron has started this one — Try Py — for beginners to learn the very basics of programming in the Python environment. He’ll be using Google’s Python learning modules, and trying to get people up to the point where they are ready to move on to more intermediate stuff at the regular 757 Python User Group.
Speaking of which, the 757 PUG has been saved from oblivion by Jesse Wright, a computer science grad student at ODU. The first meeting will be in March, and it will be listed appropriately in next month’s blog post.
Tidewater Big Data Enthusiasts:Â Tuesday, 23 February
7:00 pm at 757 Creative Space, 259 Granby St. Suite 250, downtown Norfolk.
Dr. Ilyas Ustun will talk about his research into using smartphone technology to visualize your own trips, to detect vehicle starts and stops without GPS, and how bits of data can benefit traffic engineers, transportation planners, and optimize traffic signals to decrease delays.
Office Hours: Saturday, 27 February
3:30 pm at 757 Creative Space, 259 Granby, Suite 250, downtown Norfolk.
This is a monthly hands-on working group for those new to R programming. It focuses on getting started and the basics of R.
MOOCs and other educational venues:
Coursera
Coursera continues to dominate the MOOC space when it comes to data science and analytics. They are developing more and more specialization tracks as they go, so that business model must be working (or at least showing promise).
Here is a listing of all their current data analysis courses. There are 11 specialization tracks listed, and 89 individual courses:
Coursera Data Analysis Courses
Miscellaneous:
Machine Learning is Fun
This is a wonderful two-part article that I wish I had run across when I was first starting to get interested in machine learning. Yes, there is some math, and yes, some Python code, but all in all it’s pretty accessible without too much technical stuff.
Machine Learning is Fun – Part I
Machine Learning is Fun – Part II
Top 10 Data Mining Algorithms in Plain English
This is another article I wish that I had run across when I first started this stuff. Very
Top 10 Data Mining Algorithms…
Top 100 R-Bloggers Posts of 2015
The title says it all. This is a great collection of the top 100 R-Bloggers posts from 2015, with an added 28 thrown in for good measure. Well worth slowly perusing.
Top 100 R-Bloggers Posts of 2015
R Package Round Up
Also from R-Bloggers, an interesting collection of R packages. This post lists the top 20 packages downloaded from CRAN in 2015, along with the author’s personal top five packages.
This is a 34-minute YouTube video by Owen Zhang, who went from being a software developer in a corporate IT environment to being a data scientist in a startup. This was presented at the 2015 Open Data Science Conference, and the focus is on the practical lessons he learned along with some interesting points he observed during his transition.
Analytics Blog of the Month: No Free Hunch
This is Kaggle’s blog, and as one might expect, it provides a wealth of interesting and practical information related to data science and predictive analytics. Of particular note this month was the announcement of the release of Kaggle Datasets.
Tidewater Analytics 