Retraining the Librarian for the Future
March 28, 2016
The Internet is often described as the world’s biggest library containing all the world’s knowledge that someone dumped on the floor. The Internet is the world’s biggest information database as well as the world’s biggest data mess. In the olden days, librarians used to be the gateway to knowledge management but they need to vamp up their skills beyond the Dewey Decimal System and database searching. Librarians need to do more and Christian Lauersen’s personal blog explains how in, “Data Scientist Training For Librarians-Re-Skilling Libraries For The Future.”
DST4L is a boot camp for librarians and other information professionals to learn new skills to maintain relevancy. Last year DST4L was held as:
“DST4L has been held three times in The States and was to be set for the first time in Europe at Library of Technical University of Denmark just outside of Copenhagen. 40 participants from all across Europe were ready to get there hands dirty over three days marathon of relevant tools within data archiving, handling, sharing and analyzing. See the full program here and check the #DST4L hashtag at Twitter.”
Over the course of three days, the participants learned about OpenRefine, a spreadsheet-like application that cane be used for data cleanup and transformation. They also learned about the benefits of GitHub and how to program using Python. These skills are well beyond the classed they teach in library graduate programs, but it is a good sign that the profession is evolving even if the academia aspects lag behind.
Whitney Grace, March 28, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Useful Probability Lesson in Monte Carlo Simulations
April 6, 2015
It is no surprise that probability blogger Count Bayesie, also known as data scientist Will Kurt, likes to play with random data samples like those generated in Monte Carlo simulations. He lets us in on the fun in this useful summary, “6 Neat Tricks with Monte Carlo Simulations.” He begins:
“If there is one trick you should know about probability, it’s how to write a Monte Carlo simulation. If you can program, even just a little, you can write a Monte Carlo simulation. Most of my work is in either R or Python, these examples will all be in R since out-of-the-box R has more tools to run simulations. The basics of a Monte Carlo simulation are simply to model your problem, and then randomly simulate it until you get an answer. The best way to explain is to just run through a bunch of examples, so let’s go!”
And run through his six examples he does, starting with the ever-popular basic integration. Other tricks include approximating binomial distribution, approximating Pi, finding p-values, creating games of chance, and, of course, predicting the stock market. The examples include code snippets and graphs. Kurt encourages readers to go further:
“By now it should be clear that a few lines of R can create extremely good estimates to a whole host of problems in probability and statistics. There comes a point in problems involving probability where we are often left no other choice than to use a Monte Carlo simulation. This is just the beginning of the incredible things that can be done with some extraordinarily simple tools. It also turns out that Monte Carlo simulations are at the heart of many forms of Bayesian inference.”
See the write-up for the juicy details of the six examples. This fun and informative lesson is worth checking out.
Cynthia Murrell, April 6, 2015
Stephen E Arnold, Publisher of CyberOSINT at www.xenky.com
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