- Guidelines for statistical education - Jul 31, 2014.
Data science has grown in importance to the point where statistics education should begin to integrate data science into the core statistics curriculum, as opposed to treating data science as a separate strand.
- Top KDnuggets tweets, Jul 16-17: An awesome list of Big Data frameworks - Jul 18, 2014.
An awesome GitHub list of #BigData frameworks, resources, and more; 15 interviews with 15 data scientists; 14 definitions of data scientist, from funny to serious; Revised standards for statistical evidence.
- Top KDnuggets tweets, Jul 7-10: IBM #Watson Swear Filter; How Birth Year Influences Political Views - Jul 13, 2014.
Appropriate after #BrazilvsGermany - IBM #Watson gets a Swear Filter; How Birth Year Influences Political Views; Analytics methods that "think" like Humans; Why lists of experts in Statistics, Data Science rarely intersect.
- How Xbox, Big Data & Statistical Analysis Can Measure Public Opinion - Jul 11, 2014.
Could the Xbox gaming platform and Big Data hold the key to generating accurate measures of public opinion, such as election polling? A team of statistical scientists think so.
- Top KDnuggets tweets, May 23-25: Data Science vs. Statistics: one big difference; A SQL query walks into a bar - May 26, 2014.
Data Science vs. Statistics: one big difference in Data Science focus; TGIF: A SQL query walks into a bar, approaches two girls at two tables ...; Amazing demo - IBM #Watson analyzes topic, presents a speech, can debate opponents; Microsoft #Kinect as Inexpensive #BigData Tool.
- Exclusive Interview: Michael O’Connell, Chief Data Scientist, TIBCO on How to Lead in Big Data - May 19, 2014.
We discuss Big Data vs. Fast Data, Data Visualization trends, Jaspersoft acquisition, factors differentiating future leaders of Big Data and more.
- Interview: Vasanth Kumar, Principal Data Scientist, Live Nation - May 2, 2014.
We discuss challenges in analyzing bursty data, real-time classification, relevance of statistics and advice for newcomers to Data Science.
- Learning and Teaching Machine Learning: A Personal Journey - Apr 5, 2014.
Joseph Barr examines history and origins of Machine Learning and Artificial Intelligence and recounts his personal journey from statistics to industry to teaching machine learning and running R on Unix clusters.
- DataScience Central competition: Automate jackknife regression - Apr 3, 2014.
Data Science Central holds a competition to get statisticians more involved in Data Science - create a black-box, automated, easy-to-interpret, sample-based, robust technique called jackknife regression.
- New Book: Practical Data Science with R - Mar 29, 2014.
This new book will help you learn and apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.
- Top KDnuggets tweets, Mar 26-27: Watch “Statistics with R for newbies”; Coursera free #DataScience courses - Mar 28, 2014.
Also free ebooks on Practical Machine Learning: Innovations in Recommendations, and Apache Hive - How to access big data on Hadoop with SQL/HiveQL.
- Scholarships for first-ever Women in Statistics conference, May 15-17, Cary, NC - Mar 13, 2014.
The JMP team and SAS Women’s Initiatives Network want to empower three statistics students by helping them attend the Women in Statistics conference. Apply by April 11.
- Dancing Statistics – who says statistics cannot be fun? - Mar 10, 2014.
Four little dance routines explain statistical concepts of frequency distributions, sampling, standard error, variance, correlation, and correlation != causation. Enjoy!
- Analytically Speaking Webcast with David J. Hand, Mar 5 - Feb 7, 2014.
Join "Analytically Speaking" webcast with David J. Hand, a 2-time president of the Royal Statistical Society, who will explain the commonplace nature of extraordinary events, laws behind chance moments in life, and the great importance of statistics.
- Statistical Modeling: The Two Cultures, by Leo Breiman - Nov 28, 2013.
There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown - read the full paper.