Subject: DSCI

Term: Spring 2015

DSCI 351: Exploratory Data Science for Energy and Manufacturing (100/12165)

TR 11:30-12:45 PM Jan 12-Apr 27

French, R

In this course, we will learn data science and analysis approaches applicable to energy and manufacturing technologies, to identify statistically significance relationships and better model and predict the behavior of these systems. We will assembly and explore real-world datasets, perform clustering and pair plot analyses to investigate correlations, and logistic regression will be employed to develop associated predictive models. Results will be interpreted, visualized and discussed. We will introduce the basic elements of data science and analytics using R Project for Statistical Computing. R is an open-source software project with broad abilities to access machine-readable open-data resources, data cleaning and munging functions, and a rich selection of statistical packages, used for data analytics, model development and prediction. This will include an introduction to R data types, reading and writing data, looping, plotting and regular expressions, so that one can start performing variable transformations for linear fitting and developing structural equation models, while exploring for statistically significant relationships. R Analytics will be applied to the case of energy systems (such as PV power plant degradation, and building energy efficiency) over time, by analyzing system responses, combined with results of experiments to identify fundamental principles that are statistically significant in the observed system performance. And it will be applied to manufacturing systems to understand the principles of statistical process control and identify critical factors of variability and uniformity. Offered as DSCI 351 and DSCI 451.

DSCI 451: Exploratory Data Science for Energy and Manufacturing (100/12251)

TR 11:30-12:45 PM Jan 12-Apr 27

French, R

In this course, we will learn data science and analysis approaches applicable to energy and manufacturing technologies, to identify statistically significance relationships and better model and predict the behavior of these systems. We will assembly and explore real-world datasets, perform clustering and pair plot analyses to investigate correlations, and logistic regression will be employed to develop associated predictive models. Results will be interpreted, visualized and discussed. We will introduce the basic elements of data science and analytics using R Project for Statistical Computing. R is an open-source software project with broad abilities to access machine-readable open-data resources, data cleaning and munging functions, and a rich selection of statistical packages, used for data analytics, model development and prediction. This will include an introduction to R data types, reading and writing data, looping, plotting and regular expressions, so that one can start performing variable transformations for linear fitting and developing structural equation models, while exploring for statistically significant relationships. R Analytics will be applied to the case of energy systems (such as PV power plant degradation, and building energy efficiency) over time, by analyzing system responses, combined with results of experiments to identify fundamental principles that are statistically significant in the observed system performance. And it will be applied to manufacturing systems to understand the principles of statistical process control and identify critical factors of variability and uniformity. Offered as DSCI 351 and DSCI 451.