Data Science News Highlights:

The data science ecosystem, part 3: Data applications

Computerworld
Remember that quote I started part two with? About data scientists wanting better tools for wrangling so they could work on the “sexy stuff”? Well, after …

Cybersecurity, data science and machine learning: Is all data equal?

Computerworld
Positive data, i.e. malicious network traffic data from malware and cyberattacks, have much more value than some other data science problems.

7 guerrilla tactics for retaining data scientists

TechRepublic
The ABCs of retention won’t cut it for data scientists, which is why you must use creative tactics. Try these out-of-the-box retention strategies.

JHU Researchers to Launch New Coursera Specialization Focused on Genomic Data Science

GenomeWeb
The newly-minted Genomic Data Science specialization features six non-credit courses that provide a coherent introduction to some common tools of …

Top Online Engineering School Responds to Industry Appetite for Data Science, Energy, and …

PR Newswire (press release)
The new Data Science certificate includes three courses in big data analysis, machine learning, and principles of database systems, with substitutions …

State Street, Berkeley and Stanford form data science consortium..

Finextra (press release)
“We are excited to be working with leading data scientists to tackle the immensely complex data challenges that face our clients and the institutional …

Genomic Data Science Course Invades MOOC Platform

International Business Times AU
A genomic data science course designed by John Hopkins University would soon hit massive online open course platform Coursera.org, a Forbes …

How Disruptive Are MOOCs? Hopkins Genomics MOOC Launches In June

Forbes
Of course the content today is different, particularly in the sciences–no one even knew what DNA was 200 years ago–but the way we teach has barely …

TOM STILL: Data science is learning as it grows

Kenosha News
Data science is a fancy term for statistics. It’s the extraction of knowledge from data, which can be derived from multiple digital sources and turned into …

NCDS Takes Action on Big Data

insideHPC
Big data is such a hot topic it has finally outgrown the descriptor ‘big’. From scientific journals to the popular press, so much has been said about big …

BLOGS
NASA’s Space Apps Challenge Tries to Coax More Women to Data Science

Xconomy
NASA officials visited New York in the fall to pick up some ideas on how women in the data science and tech community were doing and how to make …

WEB

the Dutch Data Science Summit

Technische Universiteit Eindhoven
the Dutch Data Science Summit. On December 1st 2015, another Dutch Data Science Summit will take place at TU/e. First confirmed speakers are …

Tackling big data: How Europe is trying to bridge the data science skills gap

Guest contributor Jonathan Keane takes a look at this space and how the European Data Science Academy wants to bridge the big data skills gap.

Three Reasons Data Scientists Might Prevent The Next Market Collapse

Attunity
Data science might be our best hope for better predicting and averting market panics like we saw in late 2008.

Here are some highlights from the 2013 Data Miner Survey:
SURVEY & PARTICIPANTS: 68-item survey conducted online in 2013. Participants: 1,259 analytic professionals from 75 countries. This is the 6th Data Miner Survey.
FOCUS ON CRM: In the past few years, there has been an increase among data miners in the already substantial area of customer-focused analytics. Respondents are looking for a better understanding of customers and seeking to improve the customer experience. This can be seen in their goals, analyses, big data endeavors, and in the focus of their text mining.
BIG DATA: Many in the field are talking about the phenomena of Big Data. There are clearly some areas in which the volume and sources of data have grown. However it is unclear how much Big Data has impacted the typical data miner. While data miners believe that the size of their datasets have increased over the past year, data from previous surveys indicate that the size of datasets have been fairly consistent over time.
THE ASCENDANCE OF R: The proportion of data miners using R is rapidly growing, and since 2010, R has been the most-used data mining tool. While R is frequently used along with other tools, an increasing number of data miners also select R as their primary tool.
CHALLENGES IN THE USE OF ANALYTICS: Data miners continue to report challenges at each level of the analytic process. Companies often are not using analytics to their fullest and have continuing issues in the areas of deployment and performance measurement.
ENGAGEMENT & JOB SATISFACTION: The Data Miners in our survey are highly engaged with the analytic community: consuming and producing content, entering competitions and searching for education and growth within their jobs. All of these activities lead to high job satisfaction, which has been increasing over time.
ANALYTIC SOFTWARE: Data miners are a diverse group who are looking for different things from their data mining tools. Ease-of-use and cost are two distinguishing dimensions. Software packages vary in their strengths and features. STATISTICA, KNIME, SAS JMP and IBM SPSS Modeler all receive high satisfaction ratings.
OTHER FINDINGS include the labels analytic professionals use to describe themselves (Data Scientist is #1), the algorithms being used (regression, decision trees, and cluster analysis continue to be the triad of core algorithms), and computing environments (cloud computing is increasing).

Some highlights from the Rexer Analytics’ 5th Annual Data Miner Survey (2011):
SURVEY & PARTICIPANTS: 52-item survey of data miners, conducted on-line in 2011. Participants: 1,319 data miners from over 60 countries.

FIELDS & GOALS: Data miners work in a diverse set of fields. CRM/Marketing has been the #1 field for the past five years. Fittingly, 
“improving the understanding of customers”, “retaining customers” and other 
CRM goals continue to be the goals identified by the most data miners.

ALGORITHMS: Decision trees, regression, and cluster analysis continue to form a triad of core algorithms for most data miners. However, a wide variety of algorithms are being used. A third of data miners currently use text mining and another third plan to do so in the future.

TOOLS: R continued its rise this year and is now being used by close to half of all data miners (47%). R users report preferring it for being free, open source, and having a wide variety of algorithms. Many people also cited R’s flexibility and the strength of the user community. STATISTICA is selected as the primary data mining tool by the most respondents (17%). Data miners report using an average of 4 software tools. STATISTICA, KNIME, Rapid Miner and Salford Systems received the strongest satisfaction ratings in 2011.

ANALYTIC CAPABILITY AND SUCCESS MEASUREMENT: Only 12% of corporate respondents rate their company as having very high analytic sophistication. However, companies with better analytic capabilities are outperforming their peers. Respondents report analyzing analytic success via Return on Investment (ROI) and analyzing the predictive validity or accuracy of their models. Challenges to measuring success include client or user cooperation and data availability/quality.