Welcome to Paul Boyer

We're pleased to announce that Paul Boyer will be joining the SSCC as a Desktop Support Specialist on June 17th. Paul just graduated from UW-Madison with a Bachelor of Business Administration degree with majors in Information Systems, Operations & Technology Management, and Marketing. Paul is taking the place of Oliver Giramma, but his role will be slightly different in that he'll be focused on desktop support along with Cody Gerhartz. This will allow Zach Heise and Mitchell Karam to focus on system administration tasks. (As always, send questions to the Help Desk and we'll get them to the right person.)

New Training Curriculum

This August we're excited to introduce a new data science-focused curriculum for SSCC's core training, including our first training in Python. Like the old Stata for Researchers and R for Researchers, the new curriculum will give you the skills you need to use statistical software for research. However, it is organized around and focused on the tasks involved in using data, not programming. It will also introduce you to the concepts and workflows of data science, which will better prepare you to understand and work with your data. The heart of the curriculum is a set of classes on "data wrangling," or the process of preparing data for analysis, which includes importing, cleaning, recoding, restructuring, combining, and anything else data needs before it can be analyzed.

Introduction to Stata will teach you the fundamentals of how Stata works. It is a prerequisite for the rest of our Stata training (or comparable experience), but it is also designed to help you excel in classes like Sociology 361 or Economics 410 that use Stata.

Data Wrangling in Stata will prepare you to do research in Stata. Graduate students may choose to take it at the beginning of their graduate student career or wait until they're ready to start doing research. Over time, our topical "Stata Workshops" will be integrated into the new curriculum as modular sections so you can spread out the process of learning to work with data over multiple semesters. If you've taken Stata for Researchers in the past you probably don't need to take Data Wrangling in Stata, but once it's in the SSCC Knowledge Base later this summer you might want to look it over to see what's new.

In R, the family of functions known as the "tidyverse" is rapidly becoming the tool of choice for data wrangling. It makes working with data much easier and cleaner. It also has more in common with Stata than base R does, making it easier for Stata users to learn R. The new R curriculum is primarily focused on using the tidyverse.

Data Wrangling in R is designed to prepare you to do research using R and the tidyverse. Graduate students may choose to take it at the beginning of their graduate student career or wait until they're ready to start doing research. While it is designed for people who have no experience with R, veteran R users who would like to learn the tidyverse will also benefit from the class. We will schedule an accelerated version of Data Wrangling in R in September.

R/RStudio for Independent Learners and Tidyverse for Independent Learners are brief introductions designed for those who want to learn the tidyverse but only need help getting started. The first part will cover basic usage of R and RStudio, just enough to be able to use the tidyverse functions taught in the second half. Veteran R users will only need the second half. It's assumed that students will work on their own after the class to learn the rest of what they need for their work. The online materials for Data Wrangling in R (once they're published) will be an excellent resource for doing so.

Data Wrangling in Python is designed to prepare you to do research using Python and the pandas package, a collection of functions for working with data similar to R's tidyverse. It will cover the exact same data skills as Data Wrangling in R, just implementing them in Python using pandas. Graduate students may choose to take it at any point in their graduate student career if they're interested in using Python in their work.

Summer Training

Meanwhile, our training in June and July gives you a last chance to take the old classes (as the new ones aren't ready just yet). This includes Stata for Researchers and R Programming and Concepts, which uses base R. We'll also have Stata workshops on programming, working with dates, and making presentable bar graphs.

Time to Upgrade Windows 7 PCs to Windows 10

As we've mentioned previously, Microsoft will end support for Windows 7 in January 2020. We expect a flurry of attacks on Windows 7 computers at that point, as hackers exploit vulnerabilities they'll save until they know Microsoft won't patch them. We plan to block Windows 7 computers from our network in January 2020, including those connecting from home via VPN, as a security threat.

We've been in touch with our member departments and agencies and their plans to update or replace their Windows 7 computers are well underway. If you use a University-owned computer that is still running Windows 7, please contact the Help Desk and arrange for our PC Support staff to take a look at it. We'll tell you if it can successfully run Windows 10 and if so make a plan to install it. Our goal is to update all the computers we support by the end of summer.

Time to Renew Your SSCC Account

If you haven't already renewed your SSCC account, please take a moment right now to do so. All you need to do is fill out the very brief form. The account renewal process helps us identify accounts that are no longer needed and the information we collect is used in SSCC's budget process.

Tip: Use the Power of SSCC's Servers

The average laptop of today has more computing power than the average server of not long ago, and for many people their laptop has all the computing power they need for their work. But for those with greater computing needs, servers have gotten a lot more powerful too...

  • Winstat and Linstat can load a one gigabyte data set in less than ten seconds, while a regular PC on our network takes over a minute
  • On Winstat you can use up to 40GB of memory. Linstat has 386GB. A typical laptop has 8GB.
  • On Winstat you can use up to 8 cores, making jobs that can do parallel processing run up to 8 times faster. On Linstat you can use up to 36 cores. A typical laptop has 4 cores.
  • On Linstat you can start a job and log off, and the job will continue to run for days or weeks if necessary. On a laptop that job is likely to be interrupted before running that long.
  • Winstat and Linstat have direct access to SSCC's network data storage, which is backed up multiple times a day to a remote location. We hope you're backing up your laptop.

Instructions for using Winstat and Linstat can be found in our Knowledge Base. Learning just enough Linux to run jobs is very easy, even if you've never used it before. Stop by the Help Desk and we'll be happy to get you started.