business rules builder

automation for data cleaning; dirty data is the most common analytics problem

exploratory analysis & visualizations; learn about the problem space

descriptive statistics, nonparmetrics; learn and share factoids about the problem to build situational awareness

linear regression models, nonlinear regression models; estimate the relationships among your variables and create predictive models (machine learning); also use simulated data to create linear regression models and learn something new

multivariate exploratory techniques; organize data into meaningful clusters, classify variables (reduce/relate variables), principal components & classification analysis

process analysis, quality control, multivariate statistical process control; understand critical process parameters which impact critical quality attributes

design of experiments, power analysis and interval estimation; experiment and discover; also use simulated data to execute virtual experiements

tabulation options; everyone needs a summary table for their presentation to management

business rules builder

automation for data cleaning; dirty data is the most common analytics problem

exploratory analysis & visualizations; learn about the problem space

descriptive statistics, nonparmetrics; learn and share factoids about the problem to build situational awareness

linear regression models, nonlinear regression models; estimate the relationships among your variables and create predictive models (machine learning); also use simulated data to create linear regression models and learn something new

multivariate exploratory techniques; organize data into meaningful clusters, classify variables (reduce/relate variables), principal components & classification analysis

process analysis, quality control, multivariate statistical process control; understand critical process parameters which impact critical quality attributes

design of experiments, power analysis and interval estimation; experiment and discover; also use simulated data to execute virtual experiements

tabulation options; everyone needs a summary table for their presentation to management

business rules builder

automation for data cleaning; dirty data is the most common analytics problem

exploratory analysis & visualizations; learn about the problem space

descriptive statistics, nonparmetrics; learn and share factoids about the problem to build situational awareness

linear regression models, nonlinear regression models; estimate the relationships among your variables and create predictive models (machine learning); also use simulated data to create linear regression models and learn something new

multivariate exploratory techniques; organize data into meaningful clusters, classify variables (reduce/relate variables), principal components & classification analysis

process analysis, quality control, multivariate statistical process control; understand critical process parameters which impact critical quality attributes

design of experiments, power analysis and interval estimation; experiment and discover; also use simulated data to execute virtual experiements

tabulation options; everyone needs a summary table for their presentation to management

business rules builder

data connector for PI SDK and PI AF

automation for data cleaning; dirty data is the most common analytics problem

exploratory analysis & visualizations; learn about the problem space

descriptive statistics, nonparmetrics; learn and share factoids about the problem to build situational awareness

linear regression models, nonlinear regression models; estimate the relationships among your variables and create predictive models (machine learning); also use simulated data to create linear regression models and learn something new

multivariate exploratory techniques; organize data into meaningful clusters, classify variables (reduce/relate variables), principal components & classification analysis

process analysis, quality control, multivariate statistical process control; understand critical process parameters which impact critical quality attributes

design of experiments, power analysis and interval estimation; experiment and discover; also use simulated data to execute virtual experiements

tabulation options; everyone needs a summary table for their presentation to management

automation for data cleaning; dirty data is the most common analytics problem

exploratory analysis & visualizations; learn about the problem space

descriptive statistics, nonparmetrics; learn and share factoids about the problem to build situational awareness

linear regression models, nonlinear regression models; estimate the relationships among your variables and create predictive models (machine learning); also use simulated data to create linear regression models and learn something new

multivariate exploratory techniques; organize data into meaningful clusters, classify variables (reduce/relate variables), principal components & classification analysis

process analysis, quality control, multivariate statistical process control; understand critical process parameters which impact critical quality attributes

design of experiments, power analysis and interval estimation; experiment and discover; also use simulated data to execute virtual experiements

tabulation options; everyone needs a summary table for their presentation to management