Introduction to Altair - A Declarative Visualization Library in Python

@machinelearnbot

Visualization is one of the most exciting parts of data science. Plotting huge amounts of data to unveil underlying relationships has its own fun.


Fast and slow visualization

@machinelearnbot

This breakneck pace is a real data visualization constraint. It's not a myth that charts are often deployed in rooms full of people who only have a short time to comprehend them (or not) and make a decision. Automatic views into datasources are a critical aspect of exploratory data analysis and health checks. The fast mode of data visualization is real and important, but when we let it become our only view into what data visualization is, we limit ourselves in planning for how to build, support and design data visualization. We limit not only data visualization creators but also data visualization readers.


Thinking Outside of the Visualization Box

@machinelearnbot

There's still time to secure a place at the Data Visualization Summit, which returns to the Westin Copley Place in Boston in just 4 weeks time, on September 25 & 26. This event will bring together the leading data visualization experts for an exploration of the tools, trends and technologies that are shaping the future of this diverse discipline. In addition to keynote presentations, workshops, panels and countless networking opportunities, we've also launched our first Data Visualization Poster Competition. For a chance to showcase your skills, ideas and understanding to an executive led audience of data visualization gurus (and win great prizes), submit a visualization that presents data in a digestible way by Tuesday, September 9. For a sneak-peak of what to expect at the summit, check out a presentation from the Senior Data Scientist at Jawbone, 'Dream a Little Bigger'.


From Data Analysis to Machine Learning

@machinelearnbot

"In my last article, I stated that for practitioners (as opposed to theorists), the real prerequisite for machine learning is data analysis, not math. One of the main reasons for making this statement, is that data scientists spend an inordinate amount of time on data analysis. The traditional statement is that data scientists "spend 80% of their time on data preparation." While I think that this statement is essentially correct, a more precise statement is that you'll spend 80% of your time on getting data, cleaning data, aggregating data, reshaping data, and exploring data using exploratory data analysis and data visualization. And ultimately, the importance of data analysis applies not only to data science generally, but machine learning specifically.