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For more information and to register, please click on a course title. You can also register by telephone, fax or email.
Multivariate Data Analysis, General
Start date: Tuesday, September 02, 2008 (week 36)
Language: English(UK)
Price: 1400 EUR
Last day to register:
2008-08-12
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/2201
|
Multivariate Data Analysis for "Omics"
Start date: Tuesday, September 02, 2008 (week 36)
Language: English(UK)
Price: 995 EUR
Last day to register:
2008-08-12
Multivariate Analysis for "Omics"
Multivariate data analysis is essential in the process of extracting information from the complex data sets involved in “omics” studies. Discover how to build valid and predictive models based on data from genomic, proteomic and metabolomic studies, with the latest multivariate techniques. The focus on this course is how to use state-of art multivariate tools to extract putative biomarkers in a statistically significant way.
The course is composed of lectures, demonstrations and computer exercises using SIMCA-P+12 software on real-life datasets. Examples from plant biology and medicine will be used to highlight the usefulness of the applied strategies.
After completing the course, participants will know how to:
| • |
Insights on chemometrics strategies for ”omics” studies |
| • |
Apply PCA to detect outliers, trends and patterns in "omics" data |
| • |
An awareness of sources of variability and the effect on modeling and interpretation |
| • |
Understanding of how to use OPLS in classification |
| • |
Understanding on how to compare multiple treatments |
| • |
Understanding on how to apply multivariate tools for putative biomarker identification |
| • |
Validate models for robustness and predictability |
Who should participate?
The course is intended for researchers involved in “omics” studies with little or no knowledge in multivariate data analysis. The examples presented in the lectures are focused on MS- and NMR-based metabolomics, for consistency. The exercises include genomics, proteomics and metabolomics data sets so participants from different field can choose from their own interest. Practice on own data is also welcome and appreciated.
Course schedule
Lunch daily between 12:00 - 13:00 |
| Day one |
| 09:00 |
Introduction to multivariate data analysis
Principal component analysis, PCA
Apply PCA to detect outliers, trends and patterns in metabolomic data
Interpret models to gain scientific insights
Exercises in SIMCA-P+ |
| 17:00 |
End of lectures and exercises and course dinner |
| Day two |
| 09:00 |
From PCA to Orthogonal projections to latent structures, OPLS
Putative biomarkers identification using OPLS
How to compare multiple treatments
Validate models for robustness and predictability
Exercises in SIMCA-P+ and discussions
Course summary |
| 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. OBSERVE THAT OWN LAPTOPS IS REQUIERED FOR EXCERCISES. PROGRAM INSTALLATION INSTRUCTIONS WILL BE SENT OUT WITH THE FINAL COURSE INFORMATION. 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/2248
|
Design of Experiments, General
Start date: Tuesday, September 02, 2008 (week 36)
Language: English(UK)
Price: 1400 EUR
Last day to register:
2008-08-12
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/2257
|
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 16, 2008 (week 38)
Language: English(UK)
Price: 1095 GBP
Last day to register:
2008-08-26
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 optimize 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 optimize products and processes. |
| • |
Report results in a simple graphical format. |
| • |
Design, measure, analyse, interpret, and optimise. |
| • |
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. |
| 17:30 |
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. |
| 17:30 |
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. |
| 16:00 |
End of course. |
Cost and conditions
The course fee (+VAT) includes lunch each day and the course dinner on Tuesday evening. The course will be held at our offices in Winkfield. B&B accommodation is available at the nearby Harte & Garter hotel in Windsor at preferential rates. Umetrics will organise accommodation at the hotel but delegates are responsible for settling their own bills. Cancellations received later than two weeks before the course starts will be charged at the full rate. Providing that Umetrics is notified, the registering company may substitute participants.
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/2214
|
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
|
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, 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 det | |
| |
Overview
Course schedule