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Courses/Training on Data Mining, Analytics,
and Data Science

          

Past | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec |      On-demand


April 2014

WhenEvent
Apr 3-4 TRANSFORMING DATA TO DECISIONS: A Roadmap to Revenue for Data Analytics [NEW], by The Modeling Agency
Los Angeles, CA, USA.
Apr 4 Hands-On Data Mining Training, by Salford Systems
Washington, DC, USA.
Apr 7-11 Predictive Analytics & Data Mining: Model Development (MD) and Strategic Implementation (SI), by The Modeling Agency
Los Angeles, CA, USA.
Apr 25 Hands-On Data Mining Training, by Salford Systems
San Diego, CA, USA.
Apr 25 - May 23 Interactive Data Visualization. (4 weeks, online) The focus is not on presentation, but on exploration and analysis. Time series, scatterplots, trellis plots, parallel coordinate plots, treemaps. Choose different variables, different plots, and different filters all on the same dataset to gain knowledge.
Online at statistics.com.
Apr 30 - May 1 TRANSFORMING DATA TO DECISIONS: A Roadmap to Revenue for Data Analytics [NEW], by The Modeling Agency
Washington, DC, USA.

May 2014

WhenEvent
On-Demand Preditive Analytics Applied - An Online Introduction, New to predictive analytics? Take this 5 1/2 hour online course to ramp up before at Predictive Analytics World Toronto. On-demand at any time-start now for 3 months access of self-paced e-learning at your convenience. Instructor: Eric Siegel, Ph.D., Conference Chair, Predictive Analytics World
Toronto, Canada
May 5 Predictive Analytics & Data Mining: Project Planning, Opportunity Identification and a Roadmap for Low-Risk / High-Impact Projects, by The Modeling Agency
Washington, DC, USA.
May 6-7 Predictive Analytics & Data Mining: Model Development (MD), A Work-Along Course for Data Preparation, Modeling Methods, Tools And Techniques, by The Modeling Agency
Washington, DC, USA.
May 5-9 Predictive Analytics & Data Mining: Strategic Implementation (SI), a Comprehensive and Active Experience of the 6-Phase Model Development Methodology, by The Modeling Agency
Washington, DC, USA.
May 12 Advanced Methods Hands-on: Predictive Modeling Techniques, This is a full day workshop at Predictive Analytics World Toronto. Once you know the basics of predictive analytics and have prepared data for modeling, which algorithms should you use? What are the similarities and differences? Which options affect the models most? This workshop dives into the key predictive analytics algorithms for supervised learning, including decision trees, linear and logistic regression, neural networks, k-nearest neighbor, support vector machines, and model ensembles. Instructor: Dean Abbott, President, Abbott Analytics
Toronto, Canada
May 13 The Best and the Worst of Predictive Analytics: Predictive Modeling Methods and Common Data Mining Mistakes, This one-day session at Predictive Analytics World Toronto surveys standard and advanced methods for predictive modeling. Dr. Elder will describe the key inner workings of leading algorithms, demonstrate their performance with business case studies, compare their merits, and show you how to pick the method and tool best suited to each predictive analytics project. Methods covered include classical regression, decision trees, neural networks, ensemble methods, uplift modeling and more. A free copy of John Elder's book Statistical Analysis and Data Mining Applications is included. Instructor: Dr. John Elder, CEO & Founder, Elder Research, Inc.
Toronto, Canada

June 2014

WhenEvent
On-Demand Preditive Analytics Applied - An Online Introduction, New to predictive analytics? Take this 5 1/2 hour online course to ramp up before Predictive Analytics World Chicago. On-demand at any time - start now for 3 months access of self-paced e-learning at your convenience. Instructor: Eric Siegel, Ph.D., Conference Chair, Predictive Analytics World
Chicago, IL
Jun 6 - Jul 4 Text Mining. (4 weeks, online) This course extends data mining's standard predictive methods to unstructured text. Tokenizations, dictionary creation, vector generation, feature generation.
Online at statistics.com.
Jun 16 The Best and the Worst of Predictive Analytics: Predictive Modeling Methods and Common Data Mining Mistakes, This one-day session at Predictive Analytics World Chicago surveys standard and advanced methods for predictive modeling. Dr. Elder will describe the key inner workings of leading algorithms, demonstrate their performance with business case studies, compare their merits, and show you how to pick the method and tool best suited to each predictive analytics project. Methods covered include classical regression, decision trees, neural networks, ensemble methods, uplift modeling and more. A free copy of John Elder's book Statistical Analysis and Data Mining Applications is included. Instructor: Dr. John Elder, CEO & Founder, Elder Research, Inc.
Chicago, IL
Jun 19 R For Predictive Modeling: A Hands-on Introduction, This one-day session at Predictive Analytics World Chicago provides a hands-on introduction to R, the well-known open-source platform for data analysis. Real examples are employed in order to methodically expose attendees to best practices driving R and its rich set of predictive modeling packages, providing hands-on experience and know-how. R is compared to other data analysis platforms, and common strengths and pitfalls in using R are discussed. Instructor: Max Kuhn, Director, Nonclinical Statistics, Pfizer
Chicago, IL
Jun 19 Advanced Methods Hands-on: Predictive Modeling Techniques, This is a full day workshop at Predictive Analytics World Chicago. Once you know the basics of predictive analytics and have prepared data for modeling, which algorithms should you use? What are the similarities and differences? Which options affect the models most? This workshop dives into the key predictive analytics algorithms for supervised learning, including decision trees, linear and logistic regression, neural networks, k-nearest neighbor, support vector machines, and model ensembles. Instructor: Dean Abbott, President, Abbott Analytics
Chicago, IL
Jun 28 - Jul 26 Data Mining in R. (4 weeks, online) This course uses cases to teach you how to use R (with which you should be familiar) for predictive modeling. Taught by Luis Torgo, author of "Data Mining with R."
Online at statistics.com.

July 2014

WhenEvent
Jul 18 - Aug 15 Predictive Analytics 1 - Machine Learning Tools. (4 weeks, online) Introduces the concept of predictive modeling and use of holdout samples and various metrics for model assessment; covers K-nearest-neighbor (KNN), Naive Bayes, CART, ensembles.
Online at statistics.com.
Jul 19 - Aug 16 Natural Language Processing. (4 weeks, online) This course teaches the concepts and techniques of NLP - text preprocessing, corpus creation, lexical analysis, tagging, parsing, semantic analysis. Taught by author Nitin Indurkhya, who first coined the term Big Data in 1998.
Online at statistics.com.

August 2014

WhenEvent
Aug 1 - Aug 29 SQL and R - Introduction to Database Queries. (4 weeks, online) An introduction to using SQL to extract data from relational databases, and then work with it in R. Database structure, SQL functions for selection, counting and arithmetic, subqueries, joins, and then work in R & Plyr.
Online at statistics.com.
Aug 15 - Sep 12 Introduction to Social Network Analysis (SNA). (4 weeks, online) Basic terms and metrics, constructing plots, measuring tie strength and trust, content analysis, propagation, sampling and analysis, illustrations of applications. Taught Jen Golbeck, Director of the Human-Computer Interaction Lab at the University of Maryland, College Park.
Online at statistics.com.
Aug 15 - Sep 12 Introduction to Python for Analytics. (4 weeks, online) An introduction for those with some programming or command line scripting familiarity. Data structures, strings, data handling, Pandas, merging, joining, visualization with matplotlib.
Online at statistics.com.
Aug 22 - Sep 19 Predictive Analytics 2 - Neural Nets and Regression. (4 weeks, online) Logistic and linear regression, discriminant analysis, neural networks. Assumes familiarity with the modeling process and model assessment taught in Predictive Analytics 1.
Online at statistics.com.
Aug 29 - Sep 26 Sentiment Analysis. (3 weeks, online) Sentiment Analysis refers to the process of identifying, extracting and classifying opinions in text segments (used in CRM, online advertising and brand analysis). Taught by author Nitin Indurkhya, who first coined the term Big Data in 1998.
Online at statistics.com.

September 2014

WhenEvent
Sep 12 - Oct 10 Forecasting Analytics. (4 weeks, online) Leach how to choose an appropriate time series model, fit the model, to conduct diagnostics, and use the model for forecasting. Regression models, Moving Average, exponential smoothing, Autoregressive models. Taught by Galit Shmueli, author of numerous books in data mining and analytics.
Online at statistics.com.
Sep 26 - Oct 24 Bayesian Statistics in R (4 weeks, online) Learn how to run Bayesian regression models in R - linear, linear regression, poisson, logit and negative binomial regression, and ordinal regression. Use JAGS and R-INLA. Taught by Peter Congdon, author of several books on Bayesian computing.
Online at statistics.com.

October 2014

WhenEvent
Oct 10 - Nov 7 Data Mining: Unsupervised Techniques (4 weeks, online) This course covers Principal Components Analysis, hierarchical and k-means clustering, association rules, and the integration of unsupervised methods into predictive modeling.
Online at statistics.com.
Oct 24 - Nov 21 Interactive Data Visualization. (4 weeks, online) The focus is not on presentation, but on exploration and analysis. Time series, scatterplots, trellis plots, parallel coordinate plots, treemaps. Choose different variables, different plots, and different filters all on the same dataset to gain knowledge.
Online at statistics.com.
Oct 31 - Nov 28 Introduction to Analytics using Hadoop. (4 weeks, online) Hands-on: set up your own Hadoop development environment. HDFS, MapReduce, data flow, functional programming with Mappers and Reducers, Hadoop streaming.
Online at statistics.com.
Oct 31 - Nov 28 Cluster Analysis (4 weeks, online) This course is an in-depth look at hierarchical clustering (divisive vs. agglomerative), k-means clustering, Normal-mixture models, and two-step clustering. Taught by Anthony Babinec, president of AB Analytics.
Online at statistics.com.

November 2014

WhenEvent
Nov 21 - Dec 19 Risk Simulation and Queuing. (4 weeks, online) This course covers simulation to analyze risk, poisson arrival rate simulation, queuing, decision analysis (decision tree, payoff matrix, expected value of information, tornado charts. Taught by Cliff Ragsdale, author of "Spreadsheet Modeling and Decision Analysis: A Practical Introduction - Management Science"
Online at statistics.com.

December 2014

WhenEvent

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