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Multivariate Data Analysis for Batch
Start date: Monday, September 15, 2008 (week 38)
Language: English
Price: $1,795 US (for additional fee computer will be provided)
Last day to register:
2008-08-25
Multivariate Analysis for Batch applications
Multivariate data analysis with projection methods is ideal for batch modelling, with the characteristic 3-way tables brought about by the batch maturity vector. Discover how to use the latest multivariate techniques to model batch processes and to interpret the models. You will see how models are applied to monitor batch evolution and predict batch quality before completion.
The course consists of lectures, examples and hands-on computer exercises in software SIMCA-P+, using real-life datasets.
After completing the course, participants will know how to:
• Understand the specific nature of batch data
• Be able to model batch data and interpret models in SIMCA-P+
• Be able to use models for prediction of batch quality
• Understand the concept of hierarchical modelling of batch data.
Who should participate?
The course is intended for researchers, scientists and engineers involved in research and development, production and manufacturing. Prior knowledge of Multivariate Data Analysis is preferable, but a review of the basic methodology will be given on the first day.
Course schedule
Lunch daily between 12:00 - 13:00
Day one
09:00 Review of MVA modelling, PCA, PLS MSPC objectives.
Objective of Batch modelling.
BMSPC, soft sensors.
Nature of batch data.
3-way decomposition vs unfolding with 2-way table analysis. Computer exercises followed by discussions.
18:00 End of lectures and exercises.
Day two
09:00 Hierarchical approach to modelling batch data.
What does quality of batch depend on?
Modelling batch data. Observation level. Batch level. Batch-specific issues. Representative training set.
Different phases. Scaling and centering of phases.
Run-to-run control. Time vs Maturity.
Smoothing maturity. Alignments of batches.
Demo using SIMCA-Batch On-Line.
Computer Exercises.
18:00 End of lectures and exercises.
Day three
09:00 Modelling batches in SIMCA-P+.
Import, specifying batch/phase identifiers.
Merging and deleting phases.
Local centering and filtering.
Conditional delete. Diagnostics and interpreting plots.
Control charts and transformations.
Prediction set and prediction control charts.
Creating batch level with different options.
Partial models and predicting the completed batch.
Model validation.
Conclusions. Course summary.
15:00 End of course.
Cost and conditions
Course fee includes coffee, lunch and course documentation. For an additional fee of $230 US, computer will be provided during course. To register, please use the button "Begin registration" in this window frame, or send an email to Umetrics Academy.
Direct link to this course: http://www.umetrics.com/default.asp/pagename/Courselist/c/2/link/10072
|
Multivariate Data Analysis, General
Start date: Tuesday, September 16, 2008 (week 38)
Language: English(UK)
Price: 1400 EUR
Last day to register:
2008-08-26
Multivariate Analysis, General
Using the latest multivariate techniques, participants will learn how to interpret complex data quickly and confidently. Discover the secrets of overviewing data tables and also learn how to build robust predictive models that turn data into decisions.
The course is composed of lectures, demonstrations and computer exercises in software SIMCA-P+, based on real-life datasets.
After completing the course, participants will know how to:
| • |
Apply PCA (Principal Component Analysis) to detect outliers, trends, patterns and to classify groups within complex data |
| • |
Relate multiple responses to multiple inputs using PLS (Partial Least Squares) modelling |
| • |
Interpret models to gain scientific insights |
| • |
Learn how to construct predictive models and apply them in practice. Use multivariate calibration to predict and improve quality |
| • |
Classification of raw materials and ingredients |
| • |
Analysing a multivariate problem. |
Who should participate?
The course is intended for researchers, scientists and engineers from all sectors of industry and academia. No prior knowledge of statistics is assumed.
Course schedule
Lunch daily between 12:00 - 13:00 |
| Day one |
| 9:00 |
Introduction and presentation of the need for multivariate data analysis. Introduction to three kinds of problem: overview, classification, and quantification and prediction.
Principal Component Analysis (PCA) for over-views of data tables: variable scaling, geometrical interpretation, and model evaluation.
PCA examples: quality control of manufacturing, process control (SPC) and environmental monitoring.
Computer exercises followed by discussions. |
| 17:00 |
End of lectures and exercises. |
| Day two |
| 9:00 |
Partial Least Squares (PLS): prediction of responses Y from control parameters X.
Variable scaling, geometrical interpretation, algebraic solution and model evaluation.
Model diagnoses and validation of a PLS model.
PLS examples: process and quality control.
Computer exercises followed by discussions. |
| 17:00 |
End of lectures and exercises. |
| Day three |
| 9:00 |
Multivariate calibration: prediction of chemical contents from spectroscopic data. Multivariate classification; two types of classification techniques will be discussed; SIMCA classification and PLS-DA (discriminant analysis). Exercises and discussion of participants’ own data. Course summary. |
| 15:00 |
End of course. |
Cost and conditions
Course fee (+VAT) includes coffee, lunch and course documentation. An invoice will be sent and payment is required within 30 days of the invoice date. Course application is binding. Cancellations registered later than two weeks before the course start will not be refunded. Provided that Umetrics AB is notified, the registering company may substitute its participant(s).
To register, please use the button "Begin registration" in this window frame, or send an email to Umetrics Academy.
Direct link to this course: http://www.umetrics.com/default.asp/pagename/Courselist/c/2/link/2258
|
Design of Experiments, General
Start date: Tuesday, September 23, 2008 (week 39)
Language: English(UK)
Price: 13500 SEK
Last day to register:
2008-09-02
Design of Experiments
Design of experiments is a rational and cost-effective approach to practical experimentation that allows the effect of variables to be assessed using a minimum of resources. Discover how to optimise your company's experiments, products and processes for increased throughput and enhanced profitability.
The course splits between lectures and hands-on analysis of real data using Umetrics' state-of-the-art software package MODDE.
After completing the course, participants will know how to:
| • |
Create efficient designs to match the experimental objectives. |
| • |
Analyse experimental data using sound statistical principles. |
| • |
Improve and optimise products and processes. |
| • |
Report results in a simple graphical format. |
| • |
Design, measure, analyse, interpret, and optimize. |
| • |
Construct predictive models and apply them in practice |
Who should participate?
The course is intended for researchers, scientists and engineers from all sectors of industry and academia. No prior knowledge of statistics is assumed.
Course schedule
Lunch daily between 12:00 - 13:00 |
| Day one |
| 09:00 |
Course start.
How and when should design of experiments be used? Problem formulation, selection of goals, factors, responses, type of model and design.
Full factorials, the basis of other designs. Analysis of full factorial designs I, evaluation of raw data, regression analysis and model interpretation.
Computer exercises followed by discussions. |
| 17:00 |
End of lectures and exercises. |
| Day two |
| 09:00 |
Analysis of full factorials II. Fault detection.
Screening designs, which factors dominate and what are their optimal ranges.
What to do after screening, optimization or modification of the design. Optimization designs, how do we find an optimum or a compromise?
Computer exercises followed by discussions. |
| 17:00 |
End of lectures and exercises. |
| Day three |
| 09:00 |
Robustness testing, verification that the method or process is robust within given specifications.
Exercises using participants' or Umetrics' examples.
Discussion of participants' own data including creation of new designs.
Course summary. |
| 15:00 |
End of course. |
Cost and conditions
Course fee (+ VAT) includes coffee, lunch and course documentation. An invoice will be sent and payment is required within 30 days of the invoice date. Course application is binding. Cancellations registered later than two weeks before the course start will not be refunded. Provided that Umetrics AB is notified, the registering company may substitute its participant(s).
To register, please use the button "Begin registration" in this window frame, or send an email to Umetrics Academy.
Direct link to this course: http://www.umetrics.com/default.asp/pagename/Courselist/c/2/link/2250
|
Multivariate Data Analysis, General
Start date: Tuesday, September 23, 2008 (week 39)
Language: English(UK)
Price: 1400 EUR
Last day to register:
2008-09-02
Multivariate Analysis, General
Using the latest multivariate techniques, participants will learn how to interpret complex data quickly and confidently. Discover the secrets of overviewing data tables and also learn how to build robust predictive models that turn data into decisions.
The course is composed of lectures, demonstrations and computer exercises in software SIMCA-P+, based on real-life datasets.
After completing the course, participants will know how to:
| • |
Apply PCA (Principal Component Analysis) to detect outliers, trends, patterns and to classify groups within complex data |
| • |
Relate multiple responses to multiple inputs using PLS (Partial Least Squares) modelling |
| • |
Interpret models to gain scientific insights |
| • |
Learn how to construct predictive models and apply them in practice. Use multivariate calibration to predict and improve quality |
| • |
Classification of raw materials and ingredients |
| • |
Analysing a multivariate problem. |
Who should participate?
The course is intended for researchers, scientists and engineers from all sectors of industry and academia. No prior knowledge of statistics is assumed.
Course schedule
Lunch daily between 12:00 - 13:00 |
| Day one |
| 9:00 |
Introduction and presentation of the need for multivariate data analysis. Introduction to three kinds of problem: overview, classification, and quantification and prediction.
Principal Component Analysis (PCA) for over-views of data tables: variable scaling, geometrical interpretation, and model evaluation.
PCA examples: quality control of manufacturing, process control (SPC) and environmental monitoring.
Computer exercises followed by discussions. |
| 17:00 |
End of lectures and exercises. |
| Day two |
| 9:00 |
Partial Least Squares (PLS): prediction of responses Y from control parameters X.
Variable scaling, geometrical interpretation, algebraic solution and model evaluation.
Model diagnoses and validation of a PLS model.
PLS examples: process and quality control.
Computer exercises followed by discussions. |
| 17:00 |
End of lectures and exercises. |
| Day three |
| 9:00 |
Multivariate calibration: prediction of chemical contents from spectroscopic data. Multivariate classification; two types of classification techniques will be discussed; SIMCA classification and PLS-DA (discriminant analysis). Exercises and discussion of participants’ own data. Course summary. |
| 15:00 |
End of course. |
Cost and conditions
Course fee (+VAT) includes coffee, lunch and course documentation. An invoice will be sent and payment is required within 30 days of the invoice date. Course application is binding. Cancellations registered later than two weeks before the course start will not be refunded. Provided that Umetrics AB is notified, the registering company may substitute its participant(s).
To register, please use the button "Begin registration" in this window frame, or send an email to Umetrics Academy.
Direct link to this course: http://www.umetrics.com/default.asp/pagename/Courselist/c/2/link/2264
|
Multivariate Data Analysis, General
Start date: Tuesday, September 30, 2008 (week 40)
Language: English(UK)
Price: 13500 SEK
Last day to register:
2008-09-09
Multivariate Analysis, General
Using the latest multivariate techniques, participants will learn how to interpret complex data quickly and confidently. Discover the secrets of overviewing data tables and also learn how to build robust predictive models that turn data into decisions.
The course is composed of lectures, demonstrations and computer exercises in software SIMCA-P+, based on real-life datasets.
After completing the course, participants will know how to:
| • |
Apply PCA (Principal Component Analysis) to detect outliers, trends, patterns and to classify groups within complex data |
| • |
Relate multiple responses to multiple inputs using PLS (Partial Least Squares) modelling |
| • |
Interpret models to gain scientific insights |
| • |
Learn how to construct predictive models and apply them in practice. Use multivariate calibration to predict and improve quality |
| • |
Classification of raw materials and ingredients |
| • |
Analysing a multivariate problem. |
Who should participate?
The course is intended for researchers, scientists and engineers from all sectors of industry and academia. No prior knowledge of statistics is assumed.
Course schedule
Lunch daily between 12:00 - 13:00 |
| Day one |
| 9:00 |
Introduction and presentation of the need for multivariate data analysis. Introduction to three kinds of problem: overview, classification, and quantification and prediction.
Principal Component Analysis (PCA) for over-views of data tables: variable scaling, geometrical interpretation, and model evaluation.
PCA examples: quality control of manufacturing, process control (SPC) and environmental monitoring.
Computer exercises followed by discussions. |
| 17:00 |
End of lectures and exercises. |
| Day two |
| 9:00 |
Partial Least Squares (PLS): prediction of responses Y from control parameters X.
Variable scaling, geometrical interpretation, algebraic solution and model evaluation.
Model diagnoses and validation of a PLS model.
PLS examples: process and quality control.
Computer exercises followed by discussions. |
| 17:00 |
End of lectures and exercises. |
| Day three |
| 9:00 |
Multivariate calibration: prediction of chemical contents from spectroscopic data. Multivariate classification; two types of classification techniques will be discussed; SIMCA classification and PLS-DA (discriminant analysis). Exercises and discussion of participants’ own data. Course summary. |
| 15:00 |
End of course. |
Cost and conditions
Course fee (+VAT) includes coffee, lunch and course documentation. An invoice will be sent and payment is required within 30 days of the invoice date. Course application is binding. Cancellations registered later than two weeks before the course start will not be refunded. Provided that Umetrics AB is notified, the registering company may substitute its participant(s).
To register, please use the button "Begin registration" in this window frame, or send an email to Umetrics Academy.
Direct link to this course: http://www.umetrics.com/default.asp/pagename/Courselist/c/2/link/2253
|
Design of Experiments, General
Start date: Tuesday, September 30, 2008 (week 40)
Language: English(UK)
Price: 1400 EUR
Last day to register:
2008-09-09
Design of Experiments
Design of experiments is a rational and cost-effective approach to practical experimentation that allows the effect of variables to be assessed using a minimum of resources. Discover how to optimise your company's experiments, products and processes for increased throughput and enhanced profitability.
The course splits between lectures and hands-on analysis of real data using Umetrics' state-of-the-art software package MODDE.
After completing the course, participants will know how to:
| • |
Create efficient designs to match the experimental objectives. |
| • |
Analyse experimental data using sound statistical principles. |
| • |
Improve and optimise products and processes. |
| • |
Report results in a simple graphical format. |
| • |
Design, measure, analyse, interpret, and optimize. |
| • |
Construct predictive models and apply them in practice |
Who should participate?
The course is intended for researchers, scientists and engineers from all sectors of industry and academia. No prior knowledge of statistics is assumed.
Course schedule
Lunch daily between 12:00 - 13:00 |
| Day one |
| 09:00 |
Course start.
How and when should design of experiments be used? Problem formulation, selection of goals, factors, responses, type of model and design.
Full factorials, the basis of other designs. Analysis of full factorial designs I, evaluation of raw data, regression analysis and model interpretation.
Computer exercises followed by discussions. |
| 17:00 |
End of lectures and exercises. |
| Day two |
| 09:00 |
Analysis of full factorials II. Fault detection.
Screening designs, which factors dominate and what are their optimal ranges.
What to do after screening, optimization or modification of the design. Optimization designs, how do we find an optimum or a compromise?
Computer exercises followed by discussions. |
| 17:00 |
End of lectures and exercises. |
| Day three |
| 09:00 |
Robustness testing, verification that the method or process is robust within given specifications.
Exercises using participants' or Umetrics' examples.
Discussion of participants' own data including creation of new designs.
Course summary. |
| 15:00 |
End of course. |
Cost and conditions
Course fee (+ VAT) includes coffee, lunch and course documentation. An invoice will be sent and payment is required within 30 days of the invoice date. Course application is binding. OBSERVE THAT OWN LAPTOPS IS REQUIERED FOR EXCERCISES. PROGRAM INSTALLATION INSTRUCTIONS WILL BE SENT OUT WITH THE FINAL COURSE INFORMATION. Cancellations registered later than two weeks before the course start will not be refunded. Provided that Umetrics AB is notified, the registering company may substitute its participant(s).
To register, please use the button "Begin registration" in this window frame, or send an email to Umetrics Academy.
Direct link to this course: http://www.umetrics.com/default.asp/pagename/Courselist/c/2/link/2262
|
Multivariate Data Analysis for Spectroscopy
Start date: Monday, October 06, 2008 (week 41)
Language: English
Price: $1,795 US (for additional fee computer will be provided)
Last day to register:
2008-09-15
Multivariate Analysis (MVA) for Spectroscopic Applications
MVA and chemometrics are important tools for spectroscopy and Process Analytical Technology (PAT). Use of NIR spectra for qualitative and quantitative method requires MVA. This course starts with the basics of calibration and spectroscopy, including the data analysis covering statistics, regression, and MVA. Hands-on examples are used throughout the course. Students will construct calibration models for moisture, assay and classification. Related topics in equipment qualification and chemometric method validation will also be discussed. The course concludes with an application workshop, where students are encouraged to bring their own data sets for examination.
Instructor:
Frederick H. Long, Ph.D.
President, Spectroscopic Solutions, LLC
Dr. Long founded Spectroscopic Solutions, a PAT consulting and training firm, in 2001. His firm has done work for numerous pharmaceutical and consumer health companies including GSK, Novartis, Schering-Plough, Johnson & Johnson, Colgate-Palmolive, Actevis (world’s third largest generic drug firm). Dr. Long received his Ph.D. in Chemical Physics from Columbia University and a S.B. and S.M. in Physics from MIT.
After completing the course, each participant will be able to:
| • |
Analyze their spectroscopic data |
| • |
Produce validated calibration models using spectral information |
Who should participate?
The course is intended for researchers, scientists and engineers from all sectors of industry and academia who analyze spectroscopic data. No prior knowledge of statistics is assumed.
Course schedule
Lunch daily between 12:00 - 13:00 |
| Day one |
| 08:30 |
Introduction to statistics
Regression analysis
Designed experiments
Linear algebra
Multivariate statistics |
| 17:00 |
End of lectures and exercises |
| Day two |
| 08:30 |
PCA
Introduction to NIR
Spectral pre-processing
PLS
Examples |
| 17:00 |
End of lectures and exercises. |
| Day three |
| 8:30 |
Classification
Method Validation
Applications Workshop |
| 17:00 |
End of course |
Cost and conditions
Course fee includes coffee, lunch and course documentation. For an additional fee of $230 US, computer will be provided during course. To register, please use the button "Begin registration" in this window frame, or send an email to Umetrics Academy.
Direct link to this course: http://www.umetrics.com/default.asp/pagename/Courselist/c/2/link/10073
|
Advanced chemometrics in “omics”
Start date: Tuesday, October 07, 2008 (week 41)
Language: English(UK)
Price: 1495 EUR
Last day to register:
2008-09-16
Advanced chemometrics in “omics”
This advanced 2 day course focuses on how to efficiently apply the philosophy and tools of chemometrics throughout an “omics” study, e.g. metabolomics. Emphasis will be on the need and benefit of applying chemometrics from the very start of a project through the sample preparation, data processing and advanced modeling to facilitate the biological interpretation. Examples from plant biology, nutrition and medicine will be used to highlight the usefulness of the applied strategies.
This course is delivered by AcureOmics AB in cooperation with Umetrics.
After completing the course, participants will know how to:
| • |
Understanding on advanced strategies for solving problems in Metabolomics |
| • |
Knowledge required to obtain good quality data in Metabolomic studies |
| • |
An awareness of sources of variability and how to use it to your advantage |
| • |
Understanding of OPLS and its extensions |
| • |
How to apply the multivariate tools for modeling of dynamic biological systems |
Who should participate?
Researchers involved in “omics” with a basic knowledge in chemometric methods, mass spectrometry and NMR spectroscopy.
Course schedule
Lunch daily between 12:00 - 13:00 |
| Day 1 |
| 09:00 |
DoE based data quality and data generation
• Sample preparation
• Experimental protocols
• Sample selection
• Analytical instruments
• Example: Representative Selection of Objects for Metabolomic Studies
• Example: Sample Preparation and Characterization
• Example: Data Processing |
| 17:00 |
End day 1, Course Dinner |
| Day 2 |
|
Modeling Complex Dynamic Biological Systems
• Hierarchical methods
• Review of OPLS and OPLS-DA
• O2PLS in combined profiling
• Dynamic/time series modeling of biological systems
• Example: Data integration and combined profiling
• Example: Class Specific Studies & Dynamic Studies
• Examples from plant biology, nutrition and medicine
Summary
• Reflections / discussion
• Key issues / rules of thumb / take home messagePartial |
| 17:00 |
End of course |
Cost and conditions
Course fee (+VAT) includes coffee, lunch and course documentation. An invoice will be sent and payment is required within 30 days of the invoice date. Course application is binding. Cancellations registered later than two weeks before the course start will not be refunded. Provided that Umetrics AB is notified, the registering company may substitute its participant(s).
Early Bird registration: If you book a course at least 2 months prior to the course date (before August 7th, 2008) the price is €1 295/person.
To register, please use the button "Begin registration" in this window frame, or send an email to Umetrics Academy.
Direct link to this course: http://www.umetrics.com/default.asp/pagename/Courselist/c/2/link/2256
|
Multivariate Data Analysis for PAT
Start date: Tuesday, October 14, 2008 (week 42)
Language: English(UK)
Price: 1145 GBP
Last day to register:
2008-09-23
Multivariate Analysis for PAT
Process Analytical Technology (PAT) is a drive within pharmaceutical manufacturing to improve process understanding, process consistency and provide a framework for continuous improvement. This course enables understanding of the central role of multivariate analysis in this process. Delegates will learn how to interpret complex data quickly and confidently, and find out how to build robust predictive models that turn data into decisions.
The course is composed of lectures, demonstrations and computer exercises in software SIMCA-P+, based on real-life datasets.
After completing the course, participants will know how to:
| • |
Apply PCA (Principal Component Analysis) to detect outliers, trends, patterns and to classify groups within complex data |
| • |
Relate multiple responses to multiple inputs using PLS (Partial Least Squares) modelling |
| • |
Interpret models to gain scientific insights |
| • |
Learn how to model processes and use multivariate calibration to improve quality |
| • |
Characterise raw materials and ingredients |
| • |
Construct predictive models and apply them in practice |
Who should participate?
The course is intended for researchers, scientists and engineers in life science industry, or with an interest in PAT. No prior knowledge of statistics is assumed.
Course schedule
Lunch daily between 12:00 - 13:00 |
| Day one |
| 09:00 |
Introduction and presentation of three kinds of problem: overview, classification, and quantification and prediction.
Principal Component Analysis (PCA) for overview of data tables: variable scaling, geometrical interpretation, and mo | |
| |
Overview
Course schedule