Friday 28 March 2014

Data Mining: Unsupervised Techniques

Data mining, the art and science of learning from data, covers a number of different procedures. This course covers key unsupervised learning techniques: association rules, principal components analysis, and clustering. (Introduction to Predictive Modeling covers techniques that are used to predict a record's class or the value of an outcome variable on the basis of a set of records with known outcomes).

Learn with Mr. Anthony Babinec in online course " Data Mining: Unsupervised Techniques" at Statistics.com. For more details please visit at http://www.statistics.com/datamining/.
We, C-eLT, Pune, partner with Statistics.com and offer these courses to Indian participants at special prices payable in INR.

The course will include an integration of supervised and unsupervised learning techniques. This is a hands-on course - participants in the course will have access to an Excel-based comprehensive tool for data-mining, XLMiner, the use of which will be explained in the course. Participants will apply data mining algorithms to real data, and will interpret the results.

Who Should Take This Course:
Marketers seeking to specify customer segments and identify associations among products purchased, environment scientists seeking to cluster observations, analysts who need to identify the key variables out of many, MBA's seeking to update their knowledge of quantitative techniques, managers and scientists who want to see what data-mining can do, and anyone who wants a practical hands-on grounding in basic data-mining techniques.

Course Program:

Course outline: The course is structured as follows
SESSION 1: Principal Components Analysis
  • The goal - dimensionality reduction
  • The principal components
  • Scale variance estimation
  • Normalizing the data
  • Principal components and least orthogonal squares
  • Exercises
 SESSION 2: Clustering
  • What is cluster analysis?
  • Hierarchical methods
  • Nearest neighbor (single linkage)
  • Farthest neighbor (complete linkage)
  • Group average (average linkage)
  • Optimization and the k-means algorithm
  • Similarity measures
  • Other distance measures
  • The curse of dimensionality
  • Exercises
SESSION 3: Association Rules
  • Discovering association rules in transaction databases
  • Support and confidence
  • The apriori algorithm
  • Shortcomings
  • Exercises
SESSION 4: Integration of Supervised and Unsupervised learning
  • Clustering into customer segments
  • Profiling of customer segments
  • Classifying new records by segment
The final lesson is an integration of supervised and unsupervised techniques. To get the full benefit of this course, familiarity with supervised learning is needed, but those not requiring this integration can learn about clustering, association rules and principal components without having had a course in supervised learning.

The instructor, Anthony Babinec, is the president of AB Analytics and served previously as Director of Advanced Products Marketing at SPSS; he worked on the marketing of Clementine and introduced CHAID, neural nets and other advanced technologies to SPSS.

You will be able to ask questions and exchange comments with the instructors via a private discussion board throughout the course.   The courses take place online at statistics.com in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

We, the Center for eLearning and Training (C-eLT), Pune, partner with Statistics.com and offer these courses to Indian participants at special prices payable in INR.

For India Registration and pricing, please visit us at www.india.statistics.com.

Call: 020 6680 0300 / 322

Websites:

Friday 21 March 2014

Discrete Choice Modeling and Conjoint Analysis

When Rupert Murdoch bought the Wall Street Journal in 2007 and revamped it, the Journal fought strong newspaper industry headwinds to reverse a decline in ad sales and boost overall revenue and attract subscribers.  They used conjoint analysis, a technique that has been widely used in market research for 30 years.

In "Discrete Choice Modeling and Conjoint Analysis" with Anthony Babinec, you will learn how to ask survey respondents to rank, rate, or choose among multiple products with multiple attributes, using experimental designs to manipulate the appearance of attribute levels in product concepts. Data in hand, you then use statistical methods to infer how the market would choose among a set of competing product alternatives. This course covers statistical techniques that address questions like this. Conjoint analysis is a marketing research technique that asks respondents to rank, rate, or choose among multiple products or services, where each product is described using multiple characteristics.

For more details please visit at http://www.statistics.com/choice.

Who Should Take This Course?
Market researchers and consultants, analysts studying corporate strategy.

Course Program:

Course outline: The course is structured as follows

SESSION 1: Fundamental Concepts
  • Ranks, ratings, choices
  • Random utility models

SESSION 2: Designing Conjoint and Choice Studies
  • Samples
  • Surveys
  • Panel data
  • Design of experiments

SESSION 3: Analysis and Interpretation
  • Conjoint analysis of ratings
  • Discrete choice models
  • Prediction

SESSION 4: Case Studies

HOMEWORK:
·     Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.
·     Guided data modeling problems using software
·     In addition to assigned readings, this course also has Discussion tasks, and an end of course data modeling project.

The instructor, Anthony Babinec, is President of AB Analytics and previously Director of Advanced Products Marketing at SPSS, has specialized in the application of statistical and data mining methods to the solution of business problems.

You will be able to ask questions and exchange comments with the instructors via a private discussion board throughout the course.   The courses take place online at statistics.com in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

We, the Center for eLearning and Training (C-eLT), Pune, partner with Statistics.com and offer these courses to Indian participants at special prices payable in INR.

For India Registration and pricing, please visit us at www.india.statistics.com.

Call: 020 6680 0300 / 322

Websites:

Thursday 20 March 2014

Online courses on Applied Statistics - Data Science, Research Science and more

Statistics.com offers 110+ courses for novices, experts, and those in between. Most courses are 4-weeks long and put you in direct contact with leading experts and authors. Whether you want to learn the fundamentals, polish your skills, master new methods or tackle new cutting edge topics, we have a course for you.

Learn topics in Data Science
Data analytics (data mining, predictive modeling, forecasting, social network anaysis, text analytics)
Statistical programming (how to use R, Python, SQL and Hadoop for analytics and statistical analysis)

Research statistics
Biostatistics (controlled clinical trials, epidemiology and environmental science)
Social science (statistical methods needed for designing and analyzing studies - including Bayesian analysis, conducting surveys and analyzing the data they yield, and a unique concentration strand devoted to Rasch methods)

Introductory Statistics

We, the Center for eLearning and Training (C-eLT), Pune, partner with Statistics.com and offer these courses to Indian participants at special prices payable in INR.

Let me tell you in very brief why prescribing these courses for your personnel is such a great idea:
  • The courses enable mastery over statistical skill sets and give your team a competitive edge
  • They enhance your personnel’s ability to make smarter business decisions that are data driven
  • They put professional growth on the fast track
  • The participants have the opportunity to learn from top statisticians who are authors of popular books on Statistics and often professors at leading universities.
  • The courses are online and participants can follow a flexible schedule in terms of time and space


Need advice on what which course to take? Contact us with your goals and background and we will provide some suggestions.

Here is the list of courses –

Data Mining and Prediction
Applied Predictive Analytics, in partnership with CrowdANALYTIX
Data Mining in R - Learning with Case Studies
Data Mining: Unsupervised Techniques
Decision Trees and Rule-Based Segmentation
Political Analytics
Predictive Analytics 1 - Machine Learning Tools
Predictive Analytics 2- Neural Nets and Regression

Data Analytics
Cluster Analysis
Discrete Choice Modeling and Conjoint Analysis
Forecasting Analytics
Interactive Data Visualization
Introduction to Social Network Analysis
Logistic Regression
Statistical Analysis of Microarray Data with R

Using R
Data Mining in R - Learning with Case Studies
Graphics in R
Mapping in R
Modeling in R
R for Statistical Analysis
R Programming - Advanced
R Programming - Intermediate
R Programming - Introduction 1
R Programming - Introduction 2
Visualization in R with ggplot2

Text Analytics
Natural Language Processing
Sentiment Analysis
Text Mining

Operations Research and Risk
Advanced Optimization
Financial Risk Modeling
Introduction to Optimization
Introduction to Quantitative Risk Analysis
Risk Simulation and Queuing

IT/Programming
Advanced Analytics and Machine Learning with Hadoop
Introduction to Analytics using Hadoop
Introduction to Python for Analytics
Social Data Mining With Python
SQL and R - Introduction to Database Queries

Biostatistics
Advanced Survival Analysis
Biostatistics 1
Biostatistics 2
Epidemiologic Statistics
Meta Analysis
Meta Analysis 2
Sample Size and Power Determination
Statistical Analysis of Microarray Data with R
Survival Analysis

Clinical Trials
Adaptive Designs for Clinical Trials
Biostatistics in R: Clinical Trial Applications
Clinical Trials - Pharmacokinetics and Bioequivalence
Introduction to Statistical Issues in Clinical Trials
Safety Monitoring Committees in Clinical Trials
Sample Size and Power-Analysis for Cluster-Randomized and Multi-Site Studies

Social Science
Advanced Structural Equation Modeling
Analysis of Survey Data from Complex Sample Designs
Introduction to Assessment and Measurement
Introduction to Structural Equation Modeling
Many-Facet Rasch Measurement
Modeling Longitudinal and Panel Data: GEE
Practical Rasch Measurement - Core Topics
Practical Rasch Measurement - Further Topics
Rasch Applications, Part 1: How to Construct a Rasch Scale
Rasch Applications, Part 2: Clinical Assessment, Survey Research, and Educational Measurement
Survey Analysis
Survey Analysis in R
Survey Design and Sampling Procedures

Spatial Analytics
Mapping in R
Spatial Analysis Techniques in R
Spatial Statistics with Geographic Information Systems

Statistical Modeling
Advanced Logistic Regression
Advanced Structural Equation Modeling
Categorical Data - Applied Modeling
Generalized Linear Models
Introduction to Smoothing and P-spline Techniques using R
Introduction to Statistical Modeling
Introduction to Structural Equation Modeling
Logistic Regression
Mixed and Hierarchical Linear Models
Modeling Count Data
Modeling in R
Modeling Longitudinal and Panel Data: GEE
Multivariate Statistics
Regression Analysis

Survey Statistics
Analysis of Survey Data from Complex Sample Designs
Survey Analysis
Survey Analysis in R
Survey Design and Sampling Procedures

Bayesian
Bayesian Regression Modeling via MCMC Techniques
Bayesian Statistics in R
Introduction to Bayesian Computing and Techniques
Introduction to Bayesian Hierarchical and Multi-level Models
Introduction to Bayesian Statistics

Engineering
Advanced Survival Analysis
Introduction to Design of Experiments
Prediction & Tolerance Intervals; Measurement and Reliability
Probability Distributions
Statistical Process Control
Survival Analysis

Environmental
Ecological and Environmental Sampling
Spatial Analysis Techniques in R
Spatial Statistics with Geographic Information Systems

Introductory
Calculus Review
Introduction to Statistics 1 AP: Inference for a Single Variable
Introduction to Statistics 2 AP: Working with Bivariate Data
Statistics  1 – Probability and Study Design
Statistics 2 – Inference and Association
Statistics 3 – ANOVA and Regression

Methods
Analysis and Sensitivity Analysis for Missing Data
Bootstrap Methods
Categorical Data Analysis
Cluster Analysis
Introduction to Resampling Methods
Maximum Likelihood Estimation
Missing Data
Principal Components and Factor Analysis

Review/Prep
Calculus Review
Designing Valid Statistical Studies
Introduction to Resampling Methods
Introduction to Statistical Modeling
Matrix Algebra Review
Survey of Statistics for Beginners

We, the Center for eLearning and Training (C-eLT), Pune, partner with Statistics.com and offer these courses to Indian participants at special prices payable in INR.

For India Registration and pricing, please visit us at www.india.statistics.com.

Call: 020 6680 0300 / 322

Websites:

Monday 17 March 2014

Meta Analysis

"The scandalous failure of scientists to cumulate scientifically," a 2006 talk by Sir Iain Chalmers, referred to the tendency of scientists to look at individual studies in isolation, rather than as part of a systematic review of the "body of evidence" on a given subject.  Meta-analysis provides the statistical methodology to aggregate multiple studies for a general conclusion.  Learn more in Dr. Michael Borenstein's and Dr. Hannah Rothstein's online course, “Meta Analysis,” at statistics.com. For more details please visit at http://www.statistics.com/meta/.

One study reports a half-second longer reaction time for drivers using a cell phone, while another reports no effect from using a cell phone.  Conclusions from additional studies lie in between.  How to resolve these differences?  When done right, Meta analysis can aggregate multiple studies for a general conclusion. 

Aim of the course:
Meta-Analysis refers to the statistical analyses that are used to synthesize summary data from a series of studies. If the effect size (or treatment effect) is consistent across all the studies in the synthesis, then the meta-analysis yields a combined effect that is more precise than any of the separate estimates, and also allows us to conclude that the effect is robust across the kinds of studies sampled. By contrast, if the effect size (or treatment effect) varies from one study to the next, the meta-analysis may allow us to identify the reason for the variation and report (for example) that the treatment is more effective in a particular kind of patient, or in a particular dose range.

In this course we will discuss the logic of meta-analysis and the way that it is being used in many fields, including medicine, education, social science, ecology, business, and others. Participants will learn how to conduct a meta-analysis (how to compute an effect size, compute summary effects, assess heterogeneity of effects, test for differences in effect size across subgroups, and more). We will also discuss various controversies in meta-analysis (such as the question of mixing apples and oranges, the criticism of garbage-in-garbage-out). We will also draw on recent headline-making analyses such as the Avandia meta-analysis.

Participants will get hands-on experience in performing analyses using Excel(tm) and also using Comprehensive Meta-Analysis (CMA). All participants will have access to a free trial of CMA for the duration of the course. At the conclusion of the course, all participants should feel comfortable conducting a meta-analysis from start to finish using this or other software.

Who Should Take This Course:
Researchers who plan to perform a meta-analysis, or who want to be able understand meta-analyses that have been published by others.

Course Program:
·         Readings in the course text (see "Requirements" section)
·         Discussion forum with instructor
·         Homework (see below)
·         End of course data modeling project (see below)
·         Short narrated software demos
·         Supplemental readings available online
·         Supplemental - Archive of prior course discussions

Course outline: The course is structured as follows

SESSION 1: Computing the Overall Effect in Meta Analysis
  • What is meta analysis
o    Meta analysis in various fields
o    Meta analysis in medicine: Saving heart attack patients
o    Meta analysis in education: Some examples
o    Meta analysis in criminal justice: The "Scared Straight" jail program
  • The role of meta analysis
o    In planning research
o    In setting policy
  • Organizations for evidence-based policy
o    The Cochrane Collaboration (medicine)
o    The Campbell Collaboration (social science)
  • Computing a treatment effect
o    Focusing on treatment effects rather than p-values
o    From binary data
o    From continuous data
o    From correlational data
  • Computing an overall effect
o    Weighted means
o    Basic statistics
  • Forest plots
o    Basic issues

SESSION 2: Fixed vs. Random Effects in Meta Analysis
  • Heterogeneity among effect sizes
o    Assessing heterogeneity
  • Fixed effect vs. random effects models
o    Conceptual differences between these models
o    Computational formulas for these models

SESSION 3: Differences in Treatment Effects in Meta Analysis
  • Understanding differences in treatment effects
o    Moderator variables
o    Analysis of variance
o    Meta regression
  • Forest Plot
o    Advanced issues

SESSION 4: Publication Bias and other Issues in Meta Analysis
  • Publication bias
o    Funnel plots
  • Multiple subgroups within studies
  • Multiple outcomes within studies
  • Common criticisms of meta analysis
o    Apples and Oranges
o    Garbage in, Garbage out
o    Discrepancies between randomized trials and meta analyses

HOMEWORK:
Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software and end of course data modeling project.

In addition to assigned readings, this course also has an end of course data modeling project, short narrated software demos, and supplemental readings available online.

Dr. Michael Borenstein (Director, Biostat, Inc.) and Hannah R. Rothstein (Professor, Baruch College and the Graduate Center of the City University of New York) are co-authors (with Hedges and Higgins) of “Introduction to Meta-Analysis,” and co-editors (with Sutton) of “Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments.”

You will be able to ask questions and exchange comments with the instructors via a private discussion board throughout the course.   The courses take place online at statistics.com in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

We, the Center for eLearning and Training (C-eLT), Pune, partner with Statistics.com and offer these courses to Indian participants at special prices payable in INR.

For India Registration and pricing, please visit us at www.india.statistics.com.

Call: 020 6680 0322

Websites: