I've recorded two courses for LinkedIn Learning https://www.linkedin.com/learning/me

Let me emphasize that their production values take a lot of work. While I think I'm a pretty good live presenter, a few days in the recording booth with a producer, reveals all my weaknesses. so. um …

I work with data scientists. I am not a scientist.

This kind of thing on scikit learn is helpful for understanding what they're trying to do and how I can help.

From someone in the process of becoming a data scientist. They had a question on regular expressions, which made almost no sense. It appears that the core concepts of ETL -- Extracting source data, Transforming it into a useful form and the Loading into some persistent storage for long-term analysis -- had …

Here's a snippet of an email

In big data / data science, the curse of dimensionality keeps showing up over and over. A good place to start is the wiki article “curse of dimensionality.” The issue seems to be that a lot of these big data / data science people have not …

Here are some tricks to working with log file extracts. We're looking at some Enterprise Splunk extracts. We can fiddle around with Splunk, trying to explore the data. Or we can get a simple extract and fiddle around with the data in Python.

Running different experiments in Python seems to …

The context is an ongoing question about optimization -- not my strong suit -- and the SciPy algorithms for this. See Scipy.optimization.anneal Problems for some additional confusion over simple things.

The new quote is this:

However, firing up Python, NumPy, SciPy and figuring out which solver to use is not …

A deep dive into probability and scipy: https://www.packtpub.com/building-probabilistic-graphical-models-with-python/book I have to admit up front that this book is out of my league. The Python is sensible to me. The subject matter -- graph models, learning and inference -- is above my pay grade.

Read this: http://www.forbes.com/sites/emc/2014/06/26/the-hottest-jobs-in-it-training-tomorrows-data-scientists/ Interesting subject areas: Statistics, Machine Learning, Algorithms. I've had questions about data science from folks who (somehow) felt that calculus and differential equations were important parts of data science. I couldn't figure out how they decided that diffeq's …

This: Python Displacing R As The Programming Language For Data Science.

Recently, I've had a former colleague asking questions about Data Science. See Obstinate Idiocy They -- weirdly -- insisted that the only language that made sense to them was Excel.

My response was a blunt "What?"

The Python vs. R post …