Expertise & Capabilities

Adsurgo is a professional services company solving problems through direct engagement consulting services and training workshops focused on the use of analytics.

CAPABILITIES

What We Do

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Applied Statistics

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Quality Control

ASCEND WITH ANALYTICS

Industries

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Biopharma & Healthcare

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DoD/Government

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Manufacturing

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Technology

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Energy

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Financial Services

ANALYTICS COURSES

Now Registering

Event	Date	Description

Reliability Engineering and Survival Analysis (1.0 days)	September 29, 2026 9:00 am	Statistical Methods for Reliability Engineering and Survival Analyses: This course covers the graphical and quantitative methods used reliability and survival analysis using JMP software. We will explore statistical methods to characterize and predict reliability and probability of survival. Topics include censoring, probability distributions—particularly Weibull and Lognormal, sample size determination, degradation/stability analysis, reliability with covariates/predictor variables, survival analysis, and fitting parametric survival models. Statistical Methods for Reliability Engineering and Survival Analyses Objectives: Understand why and how to censor data from survival studies Be able to interpret nonparametric survival plots and models Determine the best probability distribution and parameter estimates to characterize reliability Be able to model the impact of predictor variables (covariates) Calculate sample size estimates for reliability demonstrations Model system performance with survival and parametric survival techniques 1. Introduction to Reliability Methods • Censoring and event plots • Functions and definitions: reliability, survival, hazard, cumulative • Nonparametric survival methods • Probability distributions for reliability analysis 2. Reliability and Survivability Models • Survival analysis • Reliability with a single predictor variable • Reliability with multiple predictor variables using parametric survival models 3. Advanced Approaches • Degradation and stability analysis • Sample size determination • Accelerated life tests	View Outline and Register
Data Visualization and Storytelling (1.0 days)	October 27, 2026 9:00 am	Data Visualization and Storytelling (1.0 days): This course will provide principals to effectively communicate your technical results professionally. The methods will allow you to interactively discover deeper relationships graphically more efficiently. We will provide the foundations for creating better graphical information to accelerate the insight discovery process and enhance the understandability of reported results. First principles and the “human as part of the system” aspects of information visualization from multiple leading sources are explored using representative univariate, multivariate, time series, geographic, and unstructured text data sets. Data Visualization and Storytelling Objectives: Understand data visualization principals Know the characteristics of data that help guide appropriate visualizations Be able to apply a disciplined process for designing good graphics and dashboards Create graphs for different data types to include univariate, multivariate, categorical, text, time series, geographic, and modeling output. Be able to tell the story of applied statistical analysis integrating the narrative with appropriate displays Introduction • Introduction, definitions, and historical perspectives • Examples of data visualizations and interactivity • Characteristics of data Designing Effective Graphics • Human side of data visualization • Principles of good graphic design • Data visualization methodology and best practices • Dashboards Creating the Right Graph • Univariate plots: distributions, histograms, and boxplots • Multivariate plots: scatterplots, contour plots, treemaps • Categorical data plots: pie/doughnut graphs, mosaic plots • Text/Unstructured data: word clouds, topic plots, word and documents clusters • Time series data: trend lines/forecasts, waterfall plots, sparklines • Geographic data: maps, custom maps Graphics from Modeling • Model adequacy checks • Factor profilers and optimization • Comparing competing models Telling the story • Data storytelling steps and principals • Best practices • Oral presentation tips	View Outline and Register
Quality by Design (QbD) using DOE (3.0 days)	October 27, 2026 9:00 am	Quality by Design (QbD) using DOE (3.0 days): This course focuses on This course focuses on how to establish a systematic approach to pharmaceutical development that is defined by Quality-by-Design (QbD) principles using design of experiments (DOE). In addition, this course teaches the application of statistics for setting specifications, assessing measurement systems (assays), developing a control plan as part of a risk management strategy, and ensuring process control/capability. All concepts are taught within the product quality system framework defined by requirements in regulatory guidance documents. Analyses in this course use the point-and-click interface of JMP Software by SAS. Quality by Design (QbD) using DOE Objectives: implement QbD principles from discovery through product discontinuation apply statistics to set specifications and validate measurement systems (assays) utilize risk management tools to identify and prioritize potential critical process parameters identify critical process parameters and develop a functional relationship between those process parameters and your critical-to-quality attributes (CQAs) establish your design space develop a control plan as part of a risk management strategy ensure your process is in (statistical) control and capable. Outline 1. Introduction to Quality by Design (QbD) • Quality by Design (QbD) principles • Product Quality System framework 2. Primer on Statistical Analysis • basic statistics • hypothesis testing • ANOVA and regression • Blocking 3. Foundational Requirements for QbD Studies • setting specifications • Measurement Systems Analysis (MSA) for assays 4. Introduction to Design of Experiments (DOE) • steps to DOE • defining critical-to-quality attributes (CQAs) • identifying and prioritizing potential process parameters 5. Screening Designs – Identifying Critical Process Parameters • factorial designs • fractional factorial designs • D-optimal designs 6. Response Surface Designs – Develop Functional Relationships and Establish Design Space • Central Composite Designs (CCDs) • Box-Behnken designs (Self Study) • I-optimal designs 7. Specialized Designs • Definitive Screening Designs • A Optimal Designs 8. Utilizing Systematic Understanding from QbD Studies • presenting results • developing a control plan as part of a risk management strategy • process control and capability 9. Advanced DOE Methodologies (Self Study) • split-plot designs • robust parameter design • supersaturated designs • constrained designs • mixture designs	View Outline and Register
Advanced Design of Experiments (1.0) Days	November 9, 2026 9:00 am	JMP Advanced DOE (1.0 days): This course builds on the DOE Using JMP material by providing more insight into the metrics of good designs such as prediction variance, power analysis, design optimality, and correlation between factors. We build on custom designs with constrained design regions/disallowed combinations and augmenting existing designs with new runs. We introduce new classes of designs useful in many practical applications: split plot designs (hard-to-change factors), space filling designs, mixture designs, and supersaturated (more factors than runs) designs. JMP Advanced DOE Objectives: Understand the impact of power, prediction variance, optimality criterion (A, D, or I) and correlation/alias structure in designs Create custom designs in the presence of a constrained factor space Augment designs with additional runs taking advantage of what you learned from those tests already conducted. Know when to use and be able to construct split-plot designs, supersaturated designs, space filling designs, and mixture designs Outline: DOE review • Classical designs • Metrics of a good design • Custom designs and optimality criterion Designs Methods • Constrained designs and disallowed combinations • Augmenting designs Advanced Designs • split-plot designs • supersaturated designs • space filling designs • mixture designs	View Outline and Register
Applied Statistics for Scientists (2.0 days)	November 10, 2026 9:00 am	Applied Statistics for Scientists (2.0 days): This course provides instruction on how to apply scientific approaches to data analysis by focusing on appropriate methods for analysis: descriptive statistics, hypothesis testing, analysis of variance and model building. Applied Statistics for Scientists Objectives: describe and analyze the distribution of data develop summary statistics generate and analyze statistical intervals and hypothesis tests to make data-driven decisions understand issues related to sampling and calculate appropriate sample sizes describe the relationship between and among two or more factors or responses Outline: 1. Descriptive Statistics • introducing summary statistics • describing a distribution of values • creating a summary table and tabulating data 2. Intervals • producing confidence intervals • producing tolerance intervals 3. Hypothesis Testing • introducing hypothesis testing • performing means (and variance) tests • performing proportion tests 4. ANOVA • defining analysis of variance and other terminology • discussing assumptions and interpretation • interpreting hypothesis statements for ANOVA • performing one-way ANOVA 5. Simple Linear Regression • producing scatterplots and performing correlation • performing simple linear regression 6. Model Building • introducing model building • performing N-way ANOVA • performing multiple linear regression • performing ANCOVA 7. Model Diagnostics • evaluating the validity of model assumptions • evaluating model fit	View Outline and Register
Design of Experiments (DOE) using JMP (1.0 days)	November 11, 2026 9:00 am	DOE using JMP (1.0 days): This course focuses on how to establish a systematic approach to pharmaceutical development using design of experiments (DOE). All concepts are taught within the product quality system framework. Design of Experiments (DOE) using JMP: identify critical quality attributes (CQAs) that will be used as responses in your designs utilize risk management tools to identify and prioritize potential critical process parameters identify critical process parameters and develop a functional relationship between those process parameters and your critical-to-quality attributes (CQAs) using both screening and response surface designs be able to design and analyze screening designs including a factorial, fractional factorial, and D-optimal design understand the need for adding center points to a design be able to design and analyze response surface designs including central composite designs (CCDs), Box-Behnken designs, and I-optimal designs be able to design and analyze definitive screening designs present results of DOE studies Outline: Introduction to Design of Experiments (DOE) steps to DOE defining critical-to-quality attributes (CQAs) identifying and prioritizing potential process parameters Screening Designs – Identifying Critical Process Parameters factorial designs fractional factorial designs D-optimal designs Response Surface Designs – Develop Functional Relationships and Establish Design Space Central Composite Designs (CCDs) Box-Behnken designs (Self Study) I-optimal designs Other Design of Experiments Classes and Training	View Outline and Register

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Expertise & Capabilities

What We Do

Analytical Workflow Automation

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JMP Advanced DOE Objectives:

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Design of Experiments (DOE) using JMP:

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