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Data Analytics Consulting

//Data Analytics Consulting
Data Analytics Consulting 2018-04-26T20:24:41+00:00

Our Expertise

Amstat Analytics Group has become nationally recognized for helping Fortune 500 companies turn data into value without the hassle of managing complex infrastructure, systems, and tools. From implementation to data migration to tuning and optimization to data engineering and advanced analytics, the Amstat Analytics Group Professional Services team will work with you every step of the way. Our clients cite these reasons for choosing to work with us:

  • All of our principals have Ph.D. in statistics at leading universities including Harvard, Stanford, and Columbia.
  • They have extensive backgrounds in data science and over 100 years of practical experience in quantitative methods.
  • Amstat Analytics Group augments its in-house expertise by collaborating with industry experts and certified service providers to ensure you get the best Hadoop experience.
  • We are experts in advanced analytics such as predictive analytics, diagnostic analytics, prescriptive analytics, cognitive analytics, and operational analytics.
  • We are experts in statistical analysis such as traditional regression, logistic regression, multinomial logistic regression, probit regression, time series analysis, survival analysis, discriminant analysis, multivariate adaptive regression splines, globally-optimal classification tree analysis, predictive modeling, and discrete choice models.
  • We are experts in statistical programming languages such as SAS, R, SPSS, STATA, and Access.
  • We can work in SQL, Python, Scala, and Java – with a wide range of advanced analytics algorithms.

Ph.D. in Statistics at Leading Universities including Harvard, Stanford, and Columbia

All of our principals have Ph.D. in statistics at leading universities including Harvard, Stanford, and Columbia.

Ph.D. in Statistics at Leading Universities including Harvard, Stanford, and Columbia

All of our principals have Ph.D. in statistics at leading universities including Harvard, Stanford, and Columbia.

Nationally Renowned Data Scientists

We include nationally renowned data scientists.

Nationally Renowned Data Scientists

We include nationally renowned data scientists.

Extensive Backgrounds in Data Science

We have extensive backgrounds in data science and over 100 years of practical experience in quantitative methods.

Over 100 Years of Practical Experience in Quantitative Methods

We have extensive backgrounds in statistics and over 100 years of practical experience in quantitative methods.

The Best Hadoop Experience

AMSTAT Consulting augments its in-house expertise by collaborating with industry experts and certified service providers to ensure you get the best Hadoop experience.

The Best Hadoop Experience

AMSTAT Consulting augments its in-house expertise by collaborating with industry experts and certified service providers to ensure you get the best Hadoop experience.

Our Services

We are happy to provide the help you need at any or all of the following steps in earning the data-based answers you request:

  • Assessing your business needs
  • Defining the business case
  • Setting the strategic rationale for a company’s analytics journey
  • Creating advanced algorithms
  • Establishing methodologies for efficiently accessing, classifying, assessing and prioritizing data
  • Deploying analytical models for business users to incorporate into routine business operations
  • Collaborating with the IT organization to facilitate model deployment
  • Testing model accuracy
  • Maintaining analytical models so that they retain their accuracy
  • Determining which data to collect, and having a sound rationale for doing so
  • Identifying the best way to transform and structure raw data
  • Inputting, organizing, and cleaning the data
  • Exploring large data sets in real-time
  • Developing advanced analytics strategies
  • Bringing analytical rigor through the expertise of our team, which can support a multitude of analyses, including predictive modeling, customer segmentation, experimental design, pricing optimization and more.
  • Deploying advanced analytics for decision support
  • De-mystifying and simplifying analytics for business users
  • Implementing advanced analytics algorithms
  • Identifying the analytics use cases that present the highest value opportunities
  • Finding hidden patterns with advanced analytics algorithms
  • Visualizing and reporting results
  • Deriving business insight from the data
  • Monetizing business insight
  • Institutionalizing an analytics culture and associated behaviors among business users
  • Managing the ongoing storage and computing requirements associated with the ever-growing volume of data
  • Allowing unlimited e-mail and phone support
  • Supporting until project is complete

Our approach to data analytics insights offers you:

  • Enhanced decision making by assessing the likely outcomes of alternatives
  • More accurate forecasting and planning
  • Insight into patterns that improve customer satisfaction and sales
  • Dynamic, potentially automated decision making
  • Earlier identification of risks and other critical factors

Use Case Discovery

As part of our Advanced Analytical services, we will conduct a use case discovery session in order to gain a clear understanding of business priorities as well as existing workflows/data sources available for analytics. From there, we will identify use cases, and will create a road map for data development so that you can become self-sufficient.

Advanced Analytics

AMSTAT Consulting uses machine learning to perform advanced analytics:

  • Descriptive Analytics
  • Diagnostic Analytics
  • Predictive Analytics
    • Predictive Modeling
    • Automated Modeling
    • Geospatial Analysis
    • Text Analytics
    • Social Network Analysis
    • Entity Analytics
  • Prescriptive Analytics
  • Cognitive Analytics
  • Operational Analytics
    • Supply Chain Analytics
    • Complexity Management
    • End-to-End Optimization
    • Supply Chain Risk Management
    • Advanced Supplier Management

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Statistical Methods

We can work in SQL, Python, Scala, Java, and R – with a wide range of advanced analytics algorithms. We can be instantly productive with real-time analysis of large-scale datasets on topics ranging from user behavior to customer funnel. We can easily publish these results and complex visualizations. We can design and perform the required statistical analyses. We bring analytical rigor through the expertise of our team, which can support a multitude of analyses, including:

  • Customer Segmentation
  • Experimental Design
  • Pricing Optimization
  • Decision Management and Optimization
  • Advanced Statistical Analysis
  • Machine Learning
  • Data Mining
  • Embedded Analytics
  • Unstructured Data Management
  • Complex Data Integration
  • Industry Specific Research
  • Business Processes Analysis

We can design and perform the required statistical analyses.  Here is a sample of some of the analytical tools with which we are familiar:

  • Traditional Regression
  • Logistic Regression
  • Multinomial Logistic Regression
  • Probit Regression
  • Time Series Analysis
  • Survival Analysis
  • Discriminant Analysis
  • Multivariate Adaptive Regression Splines
  • Globally-Optimal Classification Tree Analysis
  • Geospatial Predictive Modeling
  • Discrete Choice Models
  • Panel Data Analysis
  • What if/Scenario Analysis
  • Trend Analysis
  • Image Analysis
  • Principal Component Analysis

We can assess key aspects of your data analytics environment, such as data inventory, accessibility, data quality, coverage, accuracy, and automation.

In a business environment, the main objective of analytics is to improve a business outcome. These outcomes can include:

  • Increasing revenue by reducing customer attrition
  • Increasing cross sell rates with a call center
  • Decreasing costs by identifying fraudulent claims before payment
  • Servicing a component in a production line to minimize downtime

We can:

  • Build accurate predictive models quickly and deliver predictive intelligence to groups and the enterprise
  • Use a range of advanced algorithms and analysis techniques to deliver insights in near real-time
  • Discover insights and solve problems faster by analyzing structured and unstructured data
  • Access varied data from flat files, databases and big data environments such as Hadoop
  • Use an intuitive interface that is easy for everyone to learn and use
  • Uncover valuable insights quickly for rapid time to value
  • Choose from on-premises, cloud and hybrid deployment options to deliver predictive intelligence through embedded services, business intelligence integration or simple reporting

Predictive Modeling

We can:

  • Use many predictive modeling techniques, including neural networks (NNs), clustering, support vector machines (SVMs), and association rules
  • Learn patterns hidden in large volumes of historical data
  • Generalize the knowledge a model learned and apply that to a new situation
  • Apply predictive modeling techniques to a myriad of problems such as recommender systems, fraud and abuse detection, and the prevention of diseases and accidents
  • Predict the risk of customer churn or defection, in case of people data, or the risk of machinery breakdown
  • Compute a score or risk by implementing a regression function
  • Use predictive models to implement a classification function
  • Recommend products and services based on customers’ habits
  • Help healthcare providers design and implement preventive life-saving measures given patients’ susceptibility towards a particular disease

Diagrammatic representation of a model ensemble in which scores from all models are computed and the final prediction is determined by a voting mechanism or the average

Geospatial Analytics

We can:

  • Explore geographic data, such as latitude and longitude, postal codes and addresses
  • Combine it with current and historical data for better insights and predictive accuracy

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Text Analytics

We can:

  • Capture key concepts, themes, sentiments, and trends by analyzing unstructured text data
  • Uncover insights in web activity, blog content, customer feedback, emails and social media comments
  • Use advanced linguistic technologies and Natural Language Processing (NLP) to process unstructured text data
  • Extract and organize the key concepts
  • Analyze relevant terms and phrases in addition to acronyms, emoticons, and slang in the right context
  • Explore and display text data and patterns for instant analysis

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Social Network Analysis

We can:

  • Offer social network analysis capabilities that transform information about relationships into key performance indicators that show the social behavior of individuals and groups
  • Use these indicators to identify social leaders who influence the behavior of others in the network
  • Help those in telecommunications and other industries who are concerned about customer turnover

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Entity Analytics

We can:

  • Use entity analytics that is critical for border security, detecting fraud, identifying criminal suspects, and avoiding presenting different offers to the same person in a marketing campaign
  • Identify n-degree relationships and improve the coherence and consistency of current data by resolving identity conflicts in the records themselves
  • Associate identity and action data with their respective entities in real-time
  • Help your organization have in-context enterprise data that can help improve model quality

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Use big data to make informed decisions

Prescriptive analytics delivers granular insights and forecasts showcasing what is likely to occur, accompanied by relevant, system-driven recommendations on next best actions and tactics to adopt. If we measure selling trends over an extended period of time, we will discern a spike in a particular product and provide certain recommendations.

AMSTAT Consulting can:

  • Use prescriptive analytics technology that combines best-in-class models, configuration and deployment options, rules/constraints, prediction, optimization techniques, scenario analysis, and a collaborative Line-of-Business environment for planning and “what-if” analysis to form a powerful foundation for decision management.
  • Help organizations make better decisions by optimizing trade-offs between business goals, such as costs or customer service, while considering predictions, rules, and constraints on available resources, to recommend the best course of action, whether you want to decide on a configuration, a design, a plan, or a schedule
  • Provide organizations in commerce, manufacturing, financial services, healthcare, telco, government and other highly data-intensive industries with the ability to find optimal, actionable choices, such as plans and schedules

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Uncover insights in big data using advanced analytics.

Descriptive analytics is the foundation level that helps users understand what has already occurred by laying out relevant summaries and supporting data in formats that are easy to consume both by end-user staff and management. We can:

  • Start with a big picture view of your metrics like booking, revenue, recurring revenue, margins, or revenue velocity and then drill-down for more granularity
  • Track social media posts, followers, comments, page views, check-ins pins, and a myriad of metrics by using descriptive analytics
  • See patterns that will never be revealed by looking at the raw event counters by assimilating that data using descriptive analytics
  • Inform a new marketing program, guide product development, or shape customer relations
  • Reveal insights that you could not gain using statistical analysis alone by inputting the right synchronous and asynchronous data sets
  • Reveal past performance for sales, production, shipping, or other operations

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Diagnostic analytics is the type of analytics that gets into “root-cause” analysis, and data discovery and exploration. 

AMSTAT Consulting can:

  • Use diagnostic analytics to provide an understanding on cross-functional data, details required for a root-cause analysis, strategy development and accountability revolving around cost, profit and risk, negative performance variations such as increases in costs, reductions in profits, and positive attributes of performance
  • Uncover patterns and correlations which provide insights that drive predictive models
  • Provide information required to develop accurate predictive models.
  • Understand your data faster to answer critical workforce questions
  • Provide the fastest and simplest way for organizations to gain more meaningful insight into their employees and solve complex workforce issues
  • Help managers search, filter and compare people
  • Find the right candidate to fill a position, select high potential employees for succession, and quickly compare succession metrics and performance reviews across select employees to reveal meaningful insights about talent pools
  • Allow for a snapshot of employees across multiple categories such as location, division, performance, and tenure

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Advanced analytics optimizes your supply chain.

Cognitive analytics exploits machine learning to refine trend and pattern analyses on an ongoing, unsupervised basis in order to constantly evaluate processes. It also leads to automatically initiating specific, suitable policies, actions, and workflows. We can:

  • Notice an influx of demand for a product in a certain region and adjust the price slightly to match demand, generating a greater profit
  • Analyze large quantities of structured and unstructured data
  • Parse the data into meaningful units
  • Infer relationships between data sets in ways that have demonstrable practical use
  • Interact with humans naturally in a symbiotic partnership

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Serving clients in the consumer packaged goods, high-tech, life sciences and industrial sectors, we provide multi-echelon inventory optimization, custom supply chain analytics, supply chain risk management, and dynamic pricing services to help companies improve operations and enhance business results. We can help you with:

  • Supply Chain Analytics
  • Complexity Management
  • End-to-End Optimization
  • Supply Chain Risk Management
  • Advanced Supplier Management

Tool-centric approaches create data silos, tool bloat, and frequent cross-team dysfunction.

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We can:

  • Plan, design, and implement a marketing analytics program within a sustainable data-driven environment, providing ongoing service and support to promote better performance, efficiency and more nimble responses to changing dynamics
  • Calculate customer sentiment, correctly spot new trends, or use data-centric research to increase brand or product awareness on a continuing basis

We can help you with:

  • Market Size, Forecasts, Trends, and Segments
  • Brand Awareness, Preference, and Loyalty Rates
  • Pricing and Purchase Dynamics
  • Product/Service Concerns

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AMSTAT Consulting provides invaluable sales insights to help businesses validate and improve current sales practices, and on the flip side can help companies better understand why their sales endeavors may not be working so well, highlighting solutions to make positive changes. AMSTAT Consulting can:

  • Put your sales people in the right place at the right time to maximize profitability
  • Identify customer buying patterns: How much they spend, when they spend, when they return
  • Identify buying trends by location, demographics, and time of year
  • Identify which products sell and which products are not as successful
  • Identify and track successful sales efforts over time to measure long-term effectiveness
  • Track sales behaviors, and measure results: share that intelligence with other sales people, and base improvement efforts on training developed from the most successful sales people’s tactics and approach vs. less successful sales people
  • Identify, target, and track most lucrative opportunities for up-sell, cross-sell and switch-sell deployments

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Four different types of machine learning algorithms are available that can be organized into a taxonomy based on the desired outcome of the algorithm or the type of input available for training the machine. We can use machine learning:

Supervised Learning

  • Most machine learning is supervised learning.
  • Supervised learning algorithms are “trained” using labeled examples where the desired output is known.
  • It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process
  • We know the correct answers, the algorithm iteratively makes predictions on the training data and is corrected by the teacher.
  • Learning stops when the algorithm achieves an acceptable level of performance.
  • Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.
  • The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.
  • Supervised learning problems can be further grouped into regression and classification problems.
    • Classification: A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”.
    • Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”.

We can:

  • Determine the input feature representation of the learned function.
    • The accuracy of the learned function depends strongly on how the input object is represented. Typically, the input object is transformed into a feature vector, which contains a number of features that are descriptive of the object. The number of features should not be too large, because of the curse of dimensionality; but should contain enough information to accurately predict the output.
  • Determine the structure of the learned function and corresponding learning algorithm
  • Complete the design
  • Run the learning algorithm
    • Some supervised learning algorithms require the user to determine certain control parameters. These parameters may be adjusted by optimizing performance on a subset (called a validation set) of the training set, or via cross-validation.
  • Evaluate the accuracy of the learned function

We can:

  • Use supervised learning in applications that use historical data to predict likely future events
  • Use supervised learning techniques to make best guess predictions for the unlabeled data
  • Feed the data back into the supervised learning algorithm as training data and use the model to make predictions on new unseen data
  • Use supervised machine learning algorithms
    • Regression – Linear REgression, LASSO Regression, Logistic Regression, Ridge Regression
    • Decision Tree -Gradient Boosting, Random Forests
    • Naïve Bayes
    • Neighbors
    • Gaussian processes
    • Neural Networks
    • Support Vector Machine (SVM)

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Unsupervised Learning

  • About 10 to 20 percent of machine learning is unsupervised learning.
  • Unsupervised learning is a type of machine learning where the system operates on unlabeled examples. In this case, the system is not told the “right answer.”
  • The algorithm tries to find a hidden structure or manifold in unlabeled data.
  • Unsupervised learning is where you only have input data (X) and no corresponding output variables.
  • The goal of unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data.
  • These are called unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own devices to discover and present the interesting structure in the data.
  • Unsupervised learning problems can be further grouped into clustering and association problems.
    • Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.
    • Association:  An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.

We can:

  • Use unsupervised learning techniques to discover and learn the structure in the input variables
  • Use unsupervised clustering – a statistical and data science technique to detect clusters and cluster structures without any a priori knowledge or training set to help the classification algorithm.
  •  Use approaches to unsupervised learning:
    • Clustering
      • k-means
      • mixture models
      • Hierarchical clustering
    • Anomaly detection
    • Neural Networks
      • Hebbian Learning
      • Generative Adversarial Networks
    • Approaches for learning latent variable models such as
      • Expectation–maximization algorithm (EM)
      • Method of moments
      • Blind signal separation techniques
        • Principal component analysis
        • Independent component analysis
        • Non-negative matrix factorization
        • Singular value decomposition

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Semisupervised Learning

  • Problems where you have a large amount of input data (X) and only some of the data is labeled (Y) are called semi-supervised learning problems.
  • These problems sit in between both supervised and unsupervised learning.
  • Methods
    •  Generative models
      • Generative approaches to statistical learning first seek to estimate {\displaystyle p(x|y)}, the distribution of data points belonging to each class.
      • Generative models assume that the distributions take some particular form {\displaystyle p(x|y,\theta )} parameterized by the vector {\displaystyle \theta }. If these assumptions are incorrect, the unlabeled data may actually decrease the accuracy of the solution relative to what would have been obtained from labeled data alone.  However, if the assumptions are correct, then the unlabeled data necessarily improve performance.
    • Low-density separation
      • It attempts to place boundaries in regions where there are few data points (labeled or unlabeled). One of the most commonly used algorithms is the transductive support vector machine, or TSVM (which, despite its name, may be used for inductive learning as well).
    • Graph-based methods
      • Graph-based methods for semi-supervised learning use a graph representation of the data
    • Heuristic approaches
      • It is not intrinsically geared to learning from both unlabeled and labeled data, but instead, makes use of unlabeled data within a supervised learning framework.

We can:

  • Use semisupervised learning for the same applications as supervised learning. But this technique uses both labeled and unlabeled data for training – typically, a small amount of labeled data with a large amount of unlabeled data
  • Use this type of learning with methods such as classification, regression, and prediction
    • Clustering
    • Autoencoders – Multilayer Perception, Restricted Boltzmann machines
    • EM
    • TSVM
    • Prediction and Classification
    • Manifold regularization
  • Use semisupervised learning when the cost associated with labeling data is too high to allow for a fully labeled training process
  • Interpret semisupervised learning in at least two different ways.
    • We can use unlabeled data to inform a computer algorithm of the structural information of the data that is relevant to supervised learning.
    • The primary goal is unsupervised learning, and labels are viewed as side information to help the algorithm find the right intrinsic data structure.

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Reinforcement Learning

AMSTAT Consulting uses reinforcement learning to discover for itself which actions yield the greatest rewards through trial and error. Reinforcement learning has three primary components:

  • The agent – the learner or decision maker
  • The environment – everything the agent interacts with
  • Actions – what the agent can do

Algorithms for reinforcement learning include:

  • Criterion of optimality
    • Policy
      • The agent’s action selection is modeled as a map called policy
      • The policy map gives the probability of taking action “a” when in state “s.”
    • State-value function
  • Brute Force
    •  The brute force approach entails two steps:
      •  For each possible policy, sample returns while following it.
      • Choose the policy with the largest expected return
  • Value Function
    • Value function approaches attempt to find a policy that maximizes the return by maintaining a set of estimates of expected returns for some policy (usually either the “current” [on-policy] or the optimal [off-policy] one).
    • These methods rely on the theory of MDPs, where optimality is defined in a sense that is stronger than the above one: A policy is called optimal if it achieves the best-expected return from an initial state (i.e., initial distributions play no role in this definition). Again, an optimal policy can always be found amongst stationary policies.

We can:

  • Use Markov decision processes (MDPs) in reinforcement learning. MDPs assume the state of the environment is perfectly observed by the agent
  • Use a more general model called partially observable MDPs (or POMDPs) to find the policy that resolves the state uncertainty while maximizing the long-term reward when this is not the case
  •  Use reinforcement learning to discover for itself which actions yield the greatest rewards through trial and error

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Generalization, Evaluation and Model Selection

We can:

  • Use all types of machine learning that develop models that enable the learning machine to perform accurately on new, unseen examples or tasks
  • Improve these models by using the machine
  • Want the fit to be not too much, not too little, but just right
  • Look at data of any complexity and size and build a model that sizes well to that data
  • Look at all the data or a subset to create an accurate model
    • One of the more powerful machine learning algorithms is a random forest.  A random forest takes individual decision trees and combines them. When a new input is entered into the system, it runs down all of the trees. The result is either an average or a weighted average of all the terminal nodes that are reached.
  • Validate a model to determine whether it can make effective predictions
  • Use a training data set to develop the model
  • Use known out-of-sample data to test it
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Dr. Vincent Salyers, Dean, MacEwan University

“I have worked closely with them on the data analysis/results of two research projects so feel as though I am knowledgeable about their expertise. On all accounts, the company provided me with reliable statistical analysis and results that I could translate into publishable format. They are conscientious experts who provide keen insights into appropriate statistical analysis given various data sets. I highly recommend them for your statistical support needs!”

Dr. Vincent Salyers, Dean

Dr. Zamir S. Brelvi MD, PhD., CEO & Co-Founder, EndoLogic

“We have been very pleased with working with them. The service was custom tailored and on time completion. The statistical report was detailed with excellent graphics. The cost of the services was affordable for a start-up company such as EndoLogic! Dr. Ann is very detail oriented and likes to know the project thoroughly that is being analyzed.”

Dr. Zamir S. Brelvi MD, PhD., CEO & Co-Founder

Dr. Raj Singhal, MD., Director, Pediatric Anesthesiology, Phoenix Children’s Hospital

“Dr. Ann has been instrumental in helping with our statistical needs. In addition to her professionalism, she has been prompt and thorough with all of our requests. Dr. Ann’s work is impeccable, and I would recommend her services to anyone in need of assistance with statistical methods or interpretation. We plan on using Dr. Ann for all of our future needs, and I am thrilled to have been introduced to her.”

Dr. Raj Singhal, MD., Director, Pediatric Anesthesiology

Dr. Haritha Boppana, MD, DHA, GHS Greenville Memorial Hospital 

“I am a physician and was in need of statistical analysis of research data. I found them on online search. Dr. Ann called me and explained the process involved in data analysis. Dr. Ann was always very prompt, helpful, intelligent and took time explaining the various tests used in conducting data analysis. Thank you so much!! I look forward to working with you in the future.”

Dr. Haritha Boppana, MD, DHA

Dr. Nancy Allen, Ph.D., Curriculum and Technology Consultant

“My project required the analysis of a complex survey that required a great deal of help in organizing the data and analyses. In addition, the project required a quick turn-around. They asked all the right questions, made realistic and helpful suggestions, and completed the project in a timely manner. They were professional and helpful throughout the process. I highly recommend them.”

Dr. Nancy Allen, Ph.D.,

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Dr. David Fetterman
Dr. David FettermanAdvisory Board (Fetterman & Associates, President)
EDUCATION

Ph.D., Stanford University
Master’s Degree, Stanford University
Master’s Degree, Stanford University

EXPERIENCE

Stanford University, Professor
School of Medicine, Stanford University, Director of Evaluation

HONORS (selected)

American Educational Research Association Research on Evaluation Distinguished Scholar Award, 2013
American Evaluation Association Advocacy and Use Evaluation Award, 2014
Lazarsfield Award for Contributions to Evaluation Theory, American Evaluation Association, 2000
Mensa Education and Research Foundation Award for Excellence, 1990.
Myrdal Award for Cumulative Contributions to Evaluation Practice, American Evaluation Association, 1995
Outstanding Higher Education Professional, Neag School of Education, University of Connecticut, 2008
Who’s Who in America, 1990, 1995-1996, 1999, 2008-2012
Who’s Who in American Education, 1989 90, 1995-96, 2003
Who’s Who in Science and Engineering, 2010, 2011
Who’s Who in the World, 2011, 2012, 2013

PROJECTS (selected)

$15 Million Digital Divide Project, Hewlett-Packard Philanthropy and Education
W. K. Kellogg Foundation
Tobacco Prevention, Minority Initiative Sub-recipient Grant Office, University of Arkansas at Pine Bluff
Tsholofelo Community, South Africa
Corte Madera, Portola Valley School District, CA
Family and Children Services, Palo Alto, CA
Ministry of Health and Jimma University, Ethiopia
BUILD, Palo Alto
Case Method, Columbia School of Journalism
Digital Media Center, Knight Foundation
Knight New Media Center, Knight Foundation
Te Puni Kokiri, Ministry of Maori Development, New Zealand
National Institute of Multimedia Education, Japan
Knight Foundations, Western Knight Center for Specialized Journalism
Mosaic’s Project, California State University
Arkansas Department of Education
One East Palo Alto. City Revitalization Project Hewlett Foundation
National Indian Child Welfare Association. Intertribal Council of Michigan, Hannahville Indian Community
Independent Development Trust, Cape Town, South Africa
California Arts Council

BOOKS (selected)

Fetterman, D.M. (2013). Empowerment Evaluation in the Digital Villages: Hewlett-Packard’s $15 Million Race Toward Social Justice. Stanford: Stanford University Press. (See Stanford Social Innovations site: http://www.ssireview.org/articles/entry/empowerment_evaluation_in_the_digital_villages_hewlett_packards_15_million)
Fetterman, D.M., Kaftarian, S., and Wandersman, A. (2014) (eds.) Empowerment Evaluation: Knowledge and Tools for Self-assessment, Evaluation Capacity Building, and Accountability. Thousand Oaks, CA: Sage.
Fetterman, D.M., Rodriguez-Campos, L., and Zukowski, A. (in press). Collaborative, Participatory, and Empowerment Evaluation: Stakeholder Involvement Approaches to Evaluation. New York: Guilford Publications.
Fetterman, D.M., Kaftarian, S., and Wandersman, A. (2015). Empowerment Evaluation: Knowledge and Tools for Self-assessment, Evaluation Capacity Building, and Accountability. Thousand Oaks, CA: Sage.
Fetterman, D.M. and Wandersman, A. (2005). Empowerment Evaluation Principles in Practice. New York: Guilford Publications. (Preview.)
Fetterman, D.M. (2001). Foundations of Empowerment Evaluation. Thousand Oaks, CA: Sage. (Preview.)
Fetterman, D.M., Kaftarian, S., Wandersman, A. (Eds.) (1996). Empowerment Evaluation: Knowledge and Tools for Self-assessment and Accountability. Newbury Park, CA: Sage.
(Preview.)
Fetterman, D.M. (Ed.) (1993). Speaking the Language of Power: Communication, Collaboration, and Advocacy. London, England: Falmer Press. (Preview.)

CHAPTERS AND ARTICLES (selected – over 100)

Fetterman, D.M. (in press). Empowerment Evaluation. The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. Thousand Oaks, CA: Sage.
Fetterman, D.M. and Ravitz, J. (in press). Evaluation Capacity Building. The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. Thousand Oaks, CA: Sage.
Fetterman, D.M. (in press). Empowerment Evaluation: Linking Theories, Principles, and Concepts to Practical Steps. In Secolsky, C. and Denison, D.B. (eds.) Handbook on Measurement, Assessment, and Evaluation in Higher Education (2nd edition). New York: Routledge.
Fetterman, D.M. (2015). Empowerment Evaluation. International Encyclopedia of the Social and Behavioral Sciences, 2nd edition.
Mansh, M., White, W., Gee-Tong, L., Lunn, M., Obedin-Maliver, J., Stewart, L., Goldsmith, E., Brenman, S., Tran, E., Wells, M., Fetterman, D.M., Garcia, G. (2015). Sexual and Gender Minority Identity Disclosure During Undergraduate Medical Education: “In the Closet” in Medical School. Academic Medicine, 90(5):634-644.
Wang JY, Lin H, Lewis PY, Fetterman DM, Gesundheit N. (2015). Is a career in medicine the right choice? The impact of a physician shadowing program on undergraduate premedical students. Acad Med. May, 90(5):629-33. doi: 10.1097/ACM.0000000000000615
White, W., Brenman, S., Paradis, E., Goldsmith, E.S., Lunn, M.R., Obedin-Maliver, J., Stewart, L., Tran, E., Wells, M., Chamberlain, L.J., Fetterman, D.M., and Garcia, G. (2015). Lesbian, Gay, Bisexual, and Transgender Patient Care: Medical Students’ Preparedness and Comfort. Teaching and Learning in Medicine: An International Journal. Volume, 27, Issue 3: 254-263
Obedin-Maliver, J., Goldsmith, E.S., Stewart, L., White, W., Tran, E., Brenman, S., Wells, M., Fetterman, D.M., Garcia, G., Lunn, M.R. (2011). Lesbian, Gay, Bisexual, and Transgender-Related Content in Undergraduate Medical Education. JAMA, 306(9):971-977.
Fetterman, D.M., Kaftarian, S., and Wandersman, A. (2015). Empowerment evaluation is a systematic way of thinking: A response to Michael Patton Empowerment evaluation: Knowledge and tools for self-assessment, evaluation capacity building, and accountability. Evaluation and Program Planning 52 (2015) 10–14
Fetterman, D.M. (2011). Empowerment Evaluation and Accreditation Case Examples: California Institute of Integral Studies and Stanford University. In Secolsky, C. and Denison, D.B. (eds.) Handbook on Measurement, Assessment, and Evaluation in Higher Education. New York: Routledge.
Fetterman, D.M., Deitz, J., and Gesundheit, N. (2010). Empowerment evaluation: A collaborative approach to evaluating and transforming a medical school curriculum. Academic Medicine, 85(5):813-820.
Fetterman, D.M. (2009). Empowerment evaluation at the Stanford University School of Medicine: Using a Critical Friend to Improve the Clerkship Experience. Ensaio: Avaliação e Políticas Públicas em Educação. Rio je Janeiro, 17(63):197-204.
Fetterman, D.M. (2004). Empowerment Evaluation’s Technological Tools of the Trade. Harvard Family Research Project. The Evaluation Exchange, X 3, p. 8-9.
Fetterman, D.M. (2003). Empowerment Evaluation Strikes a Responsive Chord. In S. Donaldson & Scriven, M. (Eds.) Evaluating social programs and problems: Visions for the new millennium. Hillsdale, NJ: Erlbaum.
Fetterman, D.M. and Bowman, C. (2001). Experiential Education and Empowerment Evaluation: Mars Rover Educational Program Case Example. Journal of Experiential Education.
Fetterman, D.M. (2002). Web surveys to Digital Movies: Technological Tools of the Trade. Educational Researcher, 31(6):29-37 or http://aera.net
Fetterman, D.M. (1998). Teaching in the Virtual Classroom at Stanford University. The Technology Source.
Fetterman, D.M. (1998). Webs of Meaning: Computer and Internet Resources for Educational Research and Instruction. Educational Researcher, 27(3):22-30.
Fetterman, D.M. (1998). Learning with and about technology: A middle school nature area. Meridian, 1(1)
Fetterman, D.M. (1996). Empowerment Evaluation: An Introduction to Theory and Practice. In Fetterman, D.M., Kaftarian, S., and Wandersman, A. (eds.) Empowerment Evaluation: Knowledge and Tools for Self-Assessment and Accountability. Newbury Park, CA: Sage.
Fetterman, D.M. (1996). Conclusion: Reflections on Emergent Themes and Next Steps. In Fetterman, D.M., Kaftarian, S., and Wandersman, A. (eds.) Empowerment Evaluation: Knowledge and Tools for Self-Assessment and Accountability. Newbury Park, CA: Sage.
Fetterman, D.M. (1996). Videoconferencing On-Line: Enhancing Communication Over the Internet. Educational Researcher, 25(4)
Fetterman, D.M. (1995). In Response to Dr. Daniel Stufflebeam’s: “Empowerment Evaluation, Objectivist Evaluation, and Evaluation Standards: Where the Future of Evaluation Should Not Go and Where It Needs to Go,” Evaluation Practice, June 1995, 16(2):179-199.
Fetterman, D.M. (1994). Gifted and Talented Education Program Evaluation. In Sternberg, R.J. (ed.) Encyclopedia of Human Intelligence. New York, NY: Macmillan Publishing Company.
Fetterman, D.M. (1994). The Terman Study. In Sternberg, R.J. (ed.) Encyclopedia of Human Intelligence. New York, NY: Macmillan Publishing Company.
Fetterman, D.M. (1994). Keeping Research on Track. New Directions for Program Evaluation. No. 63, Fall. San Francisco, CA: Jossey-Bass, pp. 103-105.
Fetterman, D.M. (1994). Empowerment Evaluation. Presidential Address. Evaluation Practice, 15(1):1-15.
Fetterman, D.M. (1992). Hevrah: Our Intellectual Community. Anthropology and Education Quarterly, 23(4): 271-274.
Fetterman, D.M. (1992) Evaluate Yourself. Storrs, CT: National Research Center on the Gifted and Talented.
Fetterman, D.M. (1991). Evaluation in Multi-Site and Multi-Focus Projects. Revitalizing Rural America: New Strategies for the Nineties. Georgia Center for Continuing Education. Athens, GA: The University of Georgia.
Fetterman, D.M. (1990). Health and Safety Issues: Colleges Must Take Steps to Avert Serious Problems. The Chronicle of Higher Education, March 21, A48.
Fetterman, D.M. (1989). Anthropology Can Make a Difference. In Trueba, H., G. Spindler, and Spindler, L. (Eds.) What Do Anthropologists Have to Say About Dropouts? New York, NY: Falmer Press, 1989.
Fetterman, D.M. (1988). Stanford Special Review on Health and Safety Phase II: A Report on Allegations. Internal Audit Department. Stanford, CA: Stanford University.
Fetterman, D.M. (1988). Gifted and Talented Education. In Gorton, R.A., Schneider, G.T., and Fisher, J.C. (Eds.) Encyclopedia of School Administration and Supervision. Phoenix, AZ: Oryx Press.
Fetterman, D.M. (1986). Operational Auditing in a Teaching Hospital: A Cultural Approach, Internal Auditor, 43(2):48-54.
Fetterman, D.M. (1986). Evaluating Organizational Culture in a Teaching Hospital: The Use of Cultural Concepts and Techniques. In K. Sedgwick (Ed.), Association of College and University Auditors. Logan, Utah: Utah State University.
Fetterman, D.M. (1982). Ibsen’s Baths: Reactivity and Insensitivity (A misapplication of the treatment-control design in a national evaluation). Educational Evaluation and Policy Analysis, 4 (3):261-279.
Fetterman, D.M. (1981). Protocol and Publication: Ethical Obligations. Anthropology and Education Quarterly, 7(1):82-83.

RADIO INTERVIEWS (recent at: http://www.davidfetterman.com/RadioInterviews.htm)

Empowerment Evaluation in the Digital Villages (book), KAZI FM, Houston, Texas, March 29, 2013.
Chronicle of Philanthropy article about evaluation and nonprofit survival (Chronicle), WPFM FM, Washington, D.C. March 25, 2013.
Empowerment Evaluation in the Digital Villages (book), Kathryn Zox Show, March 13, 2013.
Empowerment Evaluation in the Digital Villages (book), Money Matters Network, Host Stu Taylor, January 28, 2013.
Empowerment Evaluation in the Digital Villages (book), WKXL-AM, Concord, New Hampshire, Host Bill Kearney, January 17, 2013.
Empowerment Evaluation in the Digital Villages (book), WPHM-AM Detroit, Host Paul Miller, January 14, 2013.
Empowerment Evaluation in the Digital Villages (book), Business Matters Radio, Host Thomas White, January 14, 2013.

BLOGS (selected)

Fetterman, D.M. (2014) David Fetterman on Google Glass Part I: Redefining Communications. AEA365. American Evaluation Association. http://aea365.org/blog/david-fetterman-on-google-glass-part-i-redefining-communications/ (April 17.)
Fetterman, D.M. (2014) David Fetterman on Google Glass Part II: Using Glass as an Evaluation Tool. AEA365. American Evaluation Association. http://aea365.org/blog/david-fetterman-on-google-glass-part-ii-using-glass-as-an-evaluation-tool/ (April 18.)
Fetterman, D.M. (2013). In These Uncertain Times, Charities Need a Survival Plan. The Chronicle of Philanthropy. March 10. http://philanthropy.com/article/In-These-Uncertain-Times/137741/
Fetterman, D.M. (2013). Surviving the Fiscal Cliff: The one thing every nonprofit should do in the face of federal tax increases and spending cuts. Stanford Social Innovation Review. http://www.ssireview.org/blog/entry/surviving_the_fiscal_cliff (January).
Fetterman, D.M. (2012). Empowerment Evaluation in the Digital Villages. Stanford Social Innovation Review. http://www.ssireview.org/articles/entry/empowerment_evaluation_in_the_digital_villages_hewlett_packards_15_million (December)
Fetterman, D.M. (2012). Corporate Philanthropy Tackles the Digital Divide. Stanford Social Innovation Review. http://www.ssireview.org/blog/entry/corporate_philanthropy_tackles_the_digital_divide (November)

ENCYCLOPEDIA (selected): The International Encyclopedia of Education and Encyclopedia of Human Intelligence

Dr. Ann E.K. Um
Dr. Ann E.K. UmPresident and CEO
EDUCATION

Doctorate Degree, Columbia University
Master’s Degree, Stanford University
Master’s Degree, Columbia University

EXPERIENCE

Harvard Medical School, DFCI, Research Data Manager
Harvard Medical School, Brigham and Women’s Hospital, Data Science Manager
The University of Texas, Assistant Professor

PUBLICATIONS (selected)

Autonomy Support, Self-Concept, and Mathematics Performance: A Structural Equation Analysis. Saarbrucken, Germany: VDM Verlag, 2010.
Motivation and Mathematics Achievement: A Structural Equation Analysis, Saarbrucken. Saarbrucken, Germany: VDM Verlag, 2008.
Motivation and Mathematics Performance: A Structural Equation Analysis. Michigan, Ann Arbor: ProQuest, 2006.
Motivation and Mathematics Performance: A Structural Equation Analysis (doctoral dissertation). Columbia University, New York, 2005.

PRESENTATIONS (selected)

Motivation and Mathematics Performance: A Structural Equation Analysis, National Council on Measurement in Education, Montreal, Quebec, Canada, 2005.
Comparing Eighth Grade Diagnostic Test Results for Korean and American Students, National Council on Measurement in Education, Chicago, Illinois, 2003.

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This Sliding Bar can be switched on or off in theme options, and can take any widget you throw at it or even fill it with your custom HTML Code. Its perfect for grabbing the attention of your viewers. Choose between 1, 2, 3 or 4 columns, set the background color, widget divider color, activate transparency, a top border or fully disable it on desktop and mobile.