With loads of content and hype about Data Science, Analytics and Big Data coming up every day, I felt compelled to share my journey and experiences in exploring this space. Here, I intend to begin a series of posts on the subject stripped it down to it's core (as I understand it) and than building upon it.
While there are a whole bunch of directions these subjects could be approached from, I present here the path that I found most convenient to tread. I would like to call it the path…Continue
Added by Amogh Borkar on May 31, 2014 at 3:46am — No Comments
Data Science Central launched this week the Data Science Research Center, a public online resource for practitioners to read, download or publish high quality papers. If your article is accepted, you are entitled to a signed, free copy of Dr Granville's book, may have your profile featured on DSC on the…Continue
Added by Vincent Granville on May 30, 2014 at 12:00pm — No Comments
You’ve gathered up customer data which is more valuable than gold, but now what? Whether you paid for big data, got it for free or conducted your own surveys to drum it up, that’s only the…Continue
Added by Larry Alton on May 30, 2014 at 8:52am — No Comments
It is the most difficult to digest and comprehend book to date out of all I have recently read. At the same time it was pure fun.
Why so hard, I guess I need to blame myself because this book unexpectedly turned out to be more from the Academia world where my skills in Algebra and Statistics faded out over time than from the practical world where I spend my productive time. At the same time it was pleasant to have a feeling of being a student again.
Nevertheless, the book…Continue
Added by Arthur on May 29, 2014 at 2:40pm — No Comments
There isn’t any specific standard to model data warehouse. It can be built either using the “dimensional” model or the “normalised” model methodologies. Normalised model normalises the data into third normal form (3NF) whereas dimensional model collects the transactional data in the form of facts and dimensions. Normalised model is easy to use as we can add related topics without affecting the existing data. But one must have good knowledge of how data is associated before performing…Continue
Added by Avesh Dhakal on May 29, 2014 at 4:12am — No Comments
Data scientists are the lion kings of data pros while salaries for business intelligence and data warehousing pros are stagnating.
Actual data scientist salaries are much higher considering many garden…Continue
Interesting article posted on NoSQL-database.org, listing 150 databases. Here are some highlights.
Databases are categorized in the following categories:
Added by Mirko Krivanek on May 27, 2014 at 4:30pm — No Comments
The descriptions below are from Wikipedia.
In the drive towards the semantic web, mailing lists are ripe, low hanging fruit. They are full of wisdom totally inaccessible to the casual user. To unlock this wealth of knowledge for our apps, we need it in a format like the Stack Exchange data dump.
Added by Peter Higdon on May 27, 2014 at 3:30am — No Comments
Big Data is the new oil for Banking Industry. It is here to stay. McKinsey calls Big Data “the next frontier for innovation, competition and productivity.” Banks are moving to use Big Data to make more effective decisions. They are…Continue
Added by Mousumi Ghosh on May 26, 2014 at 6:29am — No Comments
Here I am talking about small countries with 10 million or less inhabitants, with good universities and high standard of living: places such as Switzerland, Singapore, Ireland, Belgium, Greece, Netherlands etc.
For data scientists, the challenges are as follows:
Interested article published by…Continue
Added by Mirko Krivanek on May 25, 2014 at 8:00am — No Comments
Above is a distribution of price differentials for the Dow Jones Industrial Average from the 1930s. The image was generated by one of my programs called Storm. I posted a few images from the same application in other blogs. If I recall correctly, the more volatile differentials (closer to the action) are at top; the more stable differentials (further from the…Continue
Added by Don Philip Faithful on May 24, 2014 at 6:51am — No Comments
This list is a bit old (I think 2011), but it features a bunch of very interesting people, true data scientists who can't afford wasting their time to post on Twitter - unlike other similar lists published by journalists.Continue
Added by Mirko Krivanek on May 23, 2014 at 2:00pm — No Comments
We return this week for Part II of our blog with astrophysicist and data scientist, Kirk Borne, Ph.D. Formerly a NASA scientist, he’s one of the foremost experts in big data and its applications in business, government and science − from exploration of space to economic growth. Here again he speaks with Anametrix CEO Pelin Thorogood, this time identifying the areas where he thinks business will benefit most from big data.…
Added by Ryan Montano on May 23, 2014 at 6:30am — No Comments
You like working with data. You’ve completed a few data science courses and enjoyed them. Now what?
I second Don VanDemark’s enthusiasm for course sequences, specialization tracks, and certification offerings. Whether through traditional brick-and-mortar schools, on-line offerings, or bootcamps, the carefully-planned curricula and (depending on the program) personal…Continue
Added by L George, Ph.D. on May 22, 2014 at 4:24pm — No Comments
Over four months ago I, with two partners, began crafting a handbook to inform students and young professionals about the data science industry. We interviewed over 30 data scientists, data analysts, CEOs, and academic professionals from the Chief Economist at Google to the founder of Cloudera. …Continue
Added by Brian Liou on May 22, 2014 at 8:26am — No Comments
AQL - Annotation Query Language
AOSD - Aspect-Oriented Software Development
ACID - Atomicity, Consistency, Isolation and Durability
BDA - Big Data Analytics
CQL - Cypher Query Language
CQL - Cassandra Query Language
CQL - Contextual/Common Query Language
COTS - Commodity off-the-shelf
CART - Classification and Regression Trees
CCA - Canonical Correlational Analysis
CEP - Complex Event Processing
DAD - Discover, Access, Distill