Data Science News 17 April 2015

Data Science News Highlights:

The data science ecosystem, part 3: Data applications

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?

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

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

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, a Forbes …

How Disruptive Are MOOCs? Hopkins Genomics MOOC Launches In June

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

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 …

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

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 …


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

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

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Data Scientist Resources 2013

Big Data is still a hot talk for business intelligence and data mining people in 2013. In my previous post, i have researched about big data search trends in 2012. FYI, data scientist is a practitioner of data science. Below are top 10 resources to kick start becoming a data scientist by exploring big data online resources:

Data Science Central – the industry’s online resource for big data practitioners, including information about the latest in technology, tools and trends.
How to be a Data Scientist – article in Smart Data Collective describing set of skills you should have if you want to do data science.
Free Big Data Education – article in Big Data Republic that listed free online courses (MOOC) which you can take toward obtaining the requisite background for becoming a data scientist.
Data Science Tutorials – list of tutorials by Kaggle to perform data analysis using data scientist’s toolkit.
Data Science News in Social Media – compilation of latest news about data science.
Data Science Wikibooks – open book with a very basic introduction to data science.
CODATA – the International Council for Science (ICSU), which works to improve the quality, reliability, management and accessibility of data. Also resource for Data Science Journal.
GigaOM Big Data – latest big data tech stories.
5 Big Data Predictions for 2013 – some of the key big data themes to dominate 2013.
Top 5 Data Science Bloggers – article that listed top 5 data science blogs.

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Data Miner Survey 2011

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.

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