Five new courses from Statistics.com, fully online and asynchronous - interact with leading experts in private forums:

Oct 21 - Nov 18: Anomaly Detection (Python & Theano)

Oct 21 - Nov 18: Meta Analysis in R

Nov 4 - Dec 16: Internet of Things (IoT)

Nov 18 - Dec 15: Deep Learning

Feb 24 - Mar 24: Spatial Analytics in QGIS

ANOMALY DETECTION (starts Oct. 28): Detecting anomalous cases in large datasets is critical in conducting surveillance, countering credit-card fraud, protecting against network hacking, combating insurance fraud, and many more applications in government, business and healthcare. Sometimes, the analyst has a set of known anomalies, and identifying similar anomalies in the future can be handled as a supervised learning task (a classification model). More often, though, little or no such “training” data are available. In such cases, the goal is to identify cases that are very different from the norm. Some techniques (clustering, nearest neighbors) may be familiar to you, others less so (e.g. based on information theory or spectral techniques).

META ANALYSIS IN R (starts Oct. 28): Meta analysis is the technique of bringing together multiple studies on the same subject for a consolidated (and statistically valid) conclusion - often a phenomenon that remains unnoticed or unproved in smaller individual studies can be demonstrated in a meta analysis.

INTERNET OF THINGS (IoT) - PROGRAMMING FOR ANALYTICS (starts Nov. 4): IoT analytics is really about time-series data. In this course you will review the flow of IoT data and survey various vertical applications of IoT analytics (automotive, healthcare, smart cities, etc.). You will then get into hands-on analytics with Python to work with various IoT analytics models like anomaly detection. Using a case-study approach, you will review pandas, numpy, matplotlib and sklearn.

DEEP LEARNING (starts Nov. 18): The surge in deployed applications based on deep learning (a form of neural nets on steroids) is an indication of its potential to help fully realize the promise of Artificial Intelligence. At the end of this course you will understand the basic concepts underlying the representations and methods in deep learning and see some applications where deep learning is most effective. You will also gain an appreciation of what kind of problems are most suited for this field and current research trends.

SPATIAL ANALYTICS IN QGIS (starts Feb 24): QGIS, open source spatial analytics software, is beginning to challenge the dominance of ESRI’s ArcGIS. Learn the basics of spatial data handling in this course, which assumes no prior experience with QGIS.

Use promo code “kdn2016” for $50 off any course.