I am teaching part of a short-course on Data Science for Managers from 10–12 October in Melbourne.
The impact of Data Science on modern business is second only to the introduction of computers. And yet, for many businesses the barrier of entry remains too high due to lack of knowhow, organisational inertia, difficulties in hiring the right manpower, an apparent need for upfront commitment, and more.
This course is designed to address these barriers, giving the necessary knowledge and skills to flesh out and manage Data Science functions within your organisation, taking the anxiety-factor out of the Big Data revolution and demonstrating how data-driven decision-making can be integrated into one’s organisation to harness existing advantages and to create new opportunities.
Assuming minimal prior knowledge, this course provides complete coverage of the key aspects, including data wrangling, modelling and analysis, predictive-, descriptive– and prescriptive-analytics, data management and curation, standards for data storage and analysis, the use of structured, semi-structured and unstructured data as well as of open public data, and the data-analytic value chain, all covered at a fundamental level.
More details available at it.monash.edu/data-science.
Early-bird bookings close in a few days.
This week, I am teaching my Business Analytics class about the bias-variance trade-off. For some reason, the proof is not contained in either ESL or ISL, even though it is quite simple. I also discovered that the proof currently provided on Wikipedia makes little sense in places.
So I wrote my own for the class. It is longer than necessary to ensure there are no jumps that might confuse students.
Continue reading →
At the recent International Symposium on Forecasting, held in Riverside, California, Tillman Gneiting gave a great talk on “Evaluating forecasts: why proper scoring rules and consistent scoring functions matter”. It will be the subject of an IJF invited paper in due course.
One of the things he talked about was the “Murphy diagram” for comparing forecasts, as proposed in Ehm et al (2015). Here’s how it works for comparing mean forecasts. Continue reading →
There are some tools that I use regularly, and I would like my research students and post-docs to learn them too. Here are some great online tutorials that might help.
Last week I gave a talk in the Yahoo! Big Thinkers series. The video of the talk is now online and embedded below.
Many people ask me to let them know when I write a new research paper. I can’t do that as there are too many people involved, and it is not scalable.
The solution is simple. Take your pick from the following options. Each is automatic and will let you know whenever I produce a new paper.
- Subscribe to the rss feed on my website using feedly or some other rss reader.
- Subscribe to new papers via email from feedburner.
- Go to my Google scholar page and click “Follow” at the top of the page.
The latter method will work for anyone with a Google scholar page. The Google scholar option only includes research papers. The first two methods also include any new seminars I give or new software packages I write.
Today at the International Symposium on Forecasting, I announced the awards for the best paper published in the International Journal of Forecasting in the period 2012–2013.
We make an award every two years to the best paper(s) published in the journal. There is always about 18 months delay after the publication period to allow time for reflection, citations, etc. The selected papers are selected by vote of the editorial board. The best paper wins an engraved bronze plaque and US$1000. Any other awards are in the form of certificates. Continue reading →
For the next few weeks I am travelling in North America and will be giving the following talks.
The Yahoo talk will be streamed live.
I’ll post slides on my main site after each talk.
Every now and then a commercial software vendor makes claims on social media about how their software is so much better than the forecast package for R, but no details are provided.
There are lots of reasons why you might select a particular software solution, and R isn’t for everyone. But anyone claiming superiority should at least provide some evidence rather than make unsubstantiated claims. Continue reading →
The anomalous package provides some tools to detect unusual time series in a large collection of time series. This is joint work with Earo Wang (an honours student at Monash) and Nikolay Laptev (from Yahoo Labs). Yahoo is interested in detecting unusual patterns in server metrics. Continue reading →