My Little Poster: Improving Metrics for Corporate Emissions Initiatives

I presented a poster on Thursday night for the BERC Innovation Expo, which is an event at the annual Energy Conference at UC Berkeley!

Here it is!  I had many stimulating conversations with people about it, which was pretty exciting.

Big Data and Data Science

Big data and data science have been generating a lot of excitement lately.  Excitement is great and all, but more importantly, more substantive articles about its limitations and uses have also been cropping up.  Here are some moderately substantive ones.

http://www.nytimes.com/2014/04/07/opinion/eight-no-nine-problems-with-big-data.html?_r=0

Highlights the general problems with 'big data' but actually it's more about data science as it's practiced in tech firms these days.  The problems aren't explained that comprehensively.  What's nice is that there is an example for each one, though.  

http://www.wired.com/2014/04/your-big-data-is-worthless-if-you-dont-bring-it-into-the-real-world/

This is a bit more substantive.  It makes the argument that big data needs to go from 'thin data' to 'thick data,' where 'thin data' are just traces of activities that are getting collected inadvertently.  'Thick data' is more information about the context of actions.  'Thick data' is probably more useful for making decisions but takes more effort to college, probably requiring one to get out and talk to people.  Then again, it's written by someone who sounds like an advocate of 'the humanities,' who perhaps is trying to justify all the 'qualitative analysis' skills she learned instead of big data analysis.  In fact, its main argument is interesting but largely unsubstantiated.

http://www.technologyreview.com/news/523646/the-power-to-decide/

http://www.technologyreview.com/news/523651/startups-embrace-a-way-to-fail-fast/

Good old MIT Technology Review.  The March/April issue had a Business Report on Data and Decision-Making.  It had several articles about trends in how businesses are using A/B testing.

aside : Yes, committing to blogging about an article (that takes more than 20 seconds to understand) is the only way I will ever actually read it much less remember what it said.

It Puts the Statistics in the Data Science

Data science is a trendy buzzword.

That doesn't mean I am not interested in it, though.

I'm reviewing my statistics by walking through this tutorial, which will soon be a book 'Statistics Done Wrong.'

I am finding it really helpful.  It highlights several common pitfalls of interpreting statistics.  The language is approachable and clear.

Yaaay!!!

Climate Change Data Management

"To achieve a leadership position in supply chain sustainability, companies should have strong capabilities in data, process and governance" - CDP (Carbon Disclosure Project) - the 2013 Supply Chain Report

Yay! I'm into all those things!!  

Information Organization

I'm taking a class in the Information School this semester called Information Organization and Retrieval.  It's about the intersection of computer science, library science, business information management, and database management.  It provides a framework that unifies all those seemingly disparate disciplines.  I find it to be extremely relevant right now, especially as I'm implementing a web app myself.  Oroeco has to handle customer data as well as science data.  We also then "create" new data that is tailored for the customer.  Many decisions for how to organize the information are more about what makes sense conceptually than technically.  Professor Robert Glushko argues that computer scientists could learn a lot from library science and vice versa.


This is a link to the syllabus.
http://blogs.ischool.berkeley.edu/i202f13/schedule/

Professor Glushko wrote a book The Discipline of Organizing