We have long talked of a future in which everything is reduced to data — and in many ways, that future has arrived. So many aspects of our lives can now be described by data, in no small part because so much is tracked by computers, which collect tremendous amounts of data. The problem is no longer what we will collect, but rather how to make sense of what we have collected, turning mountains of data into actual insights. "Data science" is the term used to describe the field that is developing methods to make sense of this data — reading, cleaning, and querying the data, and turning them into useful information that can drive our businesses.

Python is one of the leading technologies in the world of data science, thanks to a set of libraries that provide unusually powerful tools and methods. NumPy, SciPy, and Pandas, along with graphing tools such as matplotlib, make it possible to extract and analyze data in a number of different ways. Beyond that, big-data tools such as Hadoop and Spark have interfaces that allow Python programmers to access large-scale analytic systems.

In this free, hour-long Webinar, I'll introduce some of the basic tools in Python's data-science toolkit. What do they provide, and what can we do with them? What questions can we ask, and what answers can we expect to get? This is likely the first in a series of Webinars I'll be doing on the subject of Python and data science.

I invite you to join me for what should be a fun and interesting presentation and discussion; I'm hoping that you'll have lots of questions, which will make it even better!