Friday Funny: The Fastest Way Ever to Becoming a Data Scientist

Data scientist and data analyst are among the most wanted positions. It also sounds very cool to be a scientist, despite Sheldon Cooper. There is one little thing that makes it hard to get on the board and it is the amount of work one need to put in developing the required skills. I have a good news to you! Recent research has proved that this an absolute misconception and data-science-related skills could be developed much faster than previously though. There is a recipe to open the door to this lucrative field and it is based on numerous observations on the evolution of experts in the field, so it could be trusted.

Can You Do Better? Some Great Examples of Excel Dashboards

 
Every year the awesome site of Chandoo organizes a Excel dashboards contest. The site has just released the best entries of the latest competition and there is some great stuff. The participants took time to produce fine examples of dashboards. There are a concise comments for what is good and what is bad. Also, every dashboard is available for download if you would like to get in the details of how it is made. Go at the post  and get some ideas, learn and enjoy.

Analytics for The Small Business: Mission Possible

Small business usually has to be very smart  to compete on the market. "Smart" includes not only the personal quality of people running it but also, with growing importance, the ability to analyze business data. The sad truth is that these businesses are left behind by the majority of analytics vendors and the owners struggle to find solutions to meet their needs. The question is what are these solutions that could meet the demand.

My Favourite Job-related Joke

Once there was a company and it had a computer system for its intensive operations. One day, out of he blue sky, the system broke down. The company froze - it could not do anything with it - no sales, no orders processed, no purchases. All the gurus from the IT department sweat over restoring the system, but nothing worked, even Google could not give an advice.

Great Online Course for Data Mining!

Data mining appeal for companies and analytic practitioners is growing by the day. So where should you start with it? Recently I have been evaluating data mining software and courses and I came across a very good one that I can recommend without any hold-backs. This is the MOOC organized by University of Waikato. MOOC stands for "massive open online course" but do not be fooled by the name - its a serious course that delivers right on the target.

The Raise of Data Scientist - Have We Seen That Before?

In a recent conversation somebody was very excited about the marketability of skills in R and similar tools as well as with the growing demand for people having them. The story went about the bright career perspectives - money, good position in the management hierarchy, fame and Aston Martins with Victoria Secret models in them. This person is not alone and his opinion is obviously backed by the growing number of job ads requiring R or similar skills. However, I beg to disagree because we all have seen something very similar and things developed differently.

Age of Miss America Correlated to Murders by Steam?

Age of Miss America And Murders by steam, hot vapors and hot objects

One of the dangers of too much data and too many "scientist" are the spurious correlations. These are correlations that happen purely by chance. If you have time series to explain and massive amount of data sets to test, sooner or later you wold find a meaningful correlation. I have posted about that some time ago but today a co-worker have sent a link with some excellent illustrations of the point and is too good not to share. Go to Spurious Correlations to see them all - some are very funny others are puzzling. I did know that somebody could die by becoming tangled in their bedsheets! Scary bedsheets!

Big Data Deja Vu?

I was running series of machine learning algorithms on few huge files the other day in search of some meaningful information. I was enjoying all the fun that comes with large volumes of data - painfully long processing times, slow response to any data operation and loud laptop cooler to mention a few. As I was optimizing memory usage and calculation time I had a deja vu about long time ago in a lab far, far way. Back then I was calculating big set of parameters from hundreds of physics experiments. The PCs had less computing and storage power than an entry-level smart phone and all the data operations and calculations had to be performed in a clever way in order to get something meaningful in your lifetime. Back at these times nobody talked about Big Data. It was probably because quite often the data was big. Of course, the ability for collecting large volumes of data was galaxies away from the powers we have today but still, there were many domains that amounted large volumes of data. It got me thinking. Going even further back made me realize that large data sets have been with us since the beginning of the computer era. Big data is defined in many ways (see Defining Big Data) but if we adopt the simplest definition we see that it. It seems our abilities to generate data always are one step ahead of our abilities to process all of it.

The p Value is Not One Number to Rule Them All

I have just seen an article that is too good not to be shared. It is published by Scientific American and discusses issues with the statistical significance and its effect on scientific results. It also lays out the alternative statistical methods that need to be taken into account. It is written in an a readable manner and has lots of links for the curious ones. I recommend it to everyone in data and analytics-related fields as well as everyone else interested in application of scientific methods. Find the article here: Statistical significance and its part in science downfalls. Enjoy!

Where Are We on the Big Data/Analytics Hype Curve and What Does It Mean?

We are all familiar with the new technologies hype-cycle curve as described by GartnerGroup. The question now is where are we on this curve and what would be a good strategy for development in both personal career plan and business-wise.