Sunday, December 23, 2012

Data Mining and Predictive Analytics: 6 Reasons You Hired the Wrong Data Miner

Thursday, December 13, 2012

A Compilation on Time Series and Stochastic Processes geared towards Quantitative Finance

Beware - this is NOT an original piece of work!

I just prepared a compilation from Wikipedia articles and repackaged it into a pdf file. I will later try to repackage it into Kindle format using Caliber. I couldn´t get to upload it to my own site, so I am sending it to Slideshare so you can download it.

It amounts to a LARGE file, almost 200 pages long, so think before you print...

The idea was to put everything together (or almost everything...) in a halfway cohesive manner, to serve as an off-line and free reference for those interested in Time Series, Stochastic Processes and Quant Finance.

Once again, I am NOT responsible for the content, I just compiled it from Wikipedia in English.

The Economist Charts - Economic Opportunity for women (by country)

Economic opportunity for women: Where to be female | The Economist

The results are not surprising at all. I am, however, quite disappointed with Brazil's ranking. It seems that having a female president had a non significant effect on the opportunities for women so far. But, let's be realistic - cultural changes don't happen easily or quickly most of the times.

The Brave New World of BI

One of many interesting articles on the
subject. My guess is BI will be THE subject for the years to come and reshape business and academia.
The Brave New World of Business Intelligence

Wednesday, December 12, 2012

Free online courses in stat and data mining - Coursera

I discovered by chance a website that I thought was absolutely wonderful, with sensational and free courses taught by professors from major universities (so far I only noticed universities in the USA).

It looks so good that I signed up for three courses. Now I have no excuses not to learn R.

The link to the homepage is:

I was looking for courses related to Statistics and Data Mining, and found things that seemed wonderful. If you want to start where I began , see :

Monday, December 10, 2012

Presentation - International Symposium on Forecasting 2012

This is my presentation at ISF 2012. The purpose is to forecast the electricity spot price in Brazil through a hybrid neuro-fuzzy/neural network model.

The presentation can also be downloaded from my site at:

Noteworthy blog

I came across this blog today and it gave me ideas to write most of today's posts. It is really worth reading if you're interested in statistics. The link is:

Case study - Daily load forecasting

Some preliminary analysis I did a long time ago and I believe it still is relevant.

You can download it in my main site (under "Case Studies").

MOOC - an acronym for the future in teaching?

I just came across a blog post about a free statistics course (more about it later) and MOOC, which, of course, I had no idea of what it was.

Well, if you go to:

You´ll find out MOOC stands for "Massively Open Online Courses". They are a relatively new idea, and the blog author narrows MOOCs to those that "that allow registration to anyone (for free or for a small price) and also allow some degree of two way communication". Traditional Online courses are not necessarily MOOCs, as MOOCs require and promote an incredible amount of interaction among participants, and information is not concentrated, or stored, in a single place.

The next video explains the concept pretty well and is fun to watch.

If we go back to the beginning of this post and recall the online stat course I was talking about, the link is:

And the course (Statistics 110: Probability ) is free and available at Itunes. It is a Harvard University course taught by Prof. Joe Blitzstein.

R introductory course - free

I haven't checked out the entire course, but it seems very nice and the registration process is quick and without hassles (just use your FB account).

I believe it is worth a look.

Big Data in practice - from NYTimes

A very interesting article from the NY Times with a direct application of big data.

Your car insurance soon will be a direct function of very measurable and personal parameters. Basically, the insurance policy works in a "pay per mile" basis, and charges less to people who drive shorter distances.

Naturally, this can be easily extended (and will, of course) to other areas, and even other types of insurance policies, as the NY Times article points out.

Read more at: