Subject: DSCI
Term: Fall 2025
DSCI 351: Exploratory Data Science (100/9638)
TR 11:30-12:45 PM Aug 25-Dec 05
French, R
In this course, we will learn data science and analysis approaches to identify statistically significance relationships and better model and predict the behavior of these systems. We will assemble 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 basic elements of statistical analysis using R Project open source software for exploratory data analysis and model development. 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. The M section of DSCI 351 is for students focusing on Materials Data Science.
Offered as DSCI 351, DSCI 351M and DSCI 451.
DSCI 351M: Exploratory Data Science (100/9742)
TR 11:30-12:45 PM Aug 25-Dec 05
French, R
In this course, we will learn data science and analysis approaches to identify statistically significance relationships and better model and predict the behavior of these systems. We will assemble 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 basic elements of statistical analysis using R Project open source software for exploratory data analysis and model development. 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. The M section of DSCI 351 is for students focusing on Materials Data Science.
Offered as DSCI 351, DSCI 351M and DSCI 451.
DSCI 352: Applied Data Science Research (100/10221)
R 04:00-05:15 PM Aug 25-Dec 05
Barcelos, E
Weekly 75 minute in-person instruction supplemented by guided, in-person support for the semester-long project.
DSCI 352M: Applied Data Science Research (100/10222)
R 04:00-05:15 PM Aug 25-Dec 05
Barcelos, E
Weekly 75 minute in-person instruction supplemented by guided, in-person support for the semester-long project.
DSCI 451: Exploratory Data Science (100/9639)
TR 11:30-12:45 PM Aug 25-Dec 05
French, R
In this course, we will learn data science and analysis approaches to identify statistically significance relationships and better model and predict the behavior of these systems. We will assemble 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 basic elements of statistical analysis using R Project open source software for exploratory data analysis and model development. 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. The M section of DSCI 351 is for students focusing on Materials Data Science.
Offered as DSCI 351, DSCI 351M and DSCI 451.
DSCI 451: Exploratory Data Science (800/10006)
French, R
In this course, we will learn data science and analysis approaches to identify statistically significance relationships and better model and predict the behavior of these systems. We will assemble 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 basic elements of statistical analysis using R Project open source software for exploratory data analysis and model development. 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. The M section of DSCI 351 is for students focusing on Materials Data Science.
Offered as DSCI 351, DSCI 351M and DSCI 451.
DSCI 452: Applied Data Science Research (100/10223)
R 04:00-05:15 PM Aug 25-Dec 05
Barcelos, E
Weekly 75 minute in-person instruction supplemented by guided, in-person support for the semester-long project.