Subject: DSCI

Term: Spring 2026

DSCI 330: Cognition and Computation (100/8602)

MW 03:20-04:35 PM Jan 12-Apr 27

Turner, M

An introduction to (1) theories of the relationship between cognition and computation; (2) computational models of human cognition (e.g. models of decision-making or concept creation); and (3) computational tools for the study of human cognition. All three dimensions involve AI and data science: theories compare natural and artificial intelligence and are tested against archives of data from brain imagining to linguistic corpora; models are derived from and tested against datasets of e.g., financial decisions (markets), legal rulings and findings (juries, judges, courts), legislative actions, and healthcare decisions, and moreover are often constructed via AI; computational tools aggregate data and operate upon it analytically, for search, recognition, tagging, machine learning, statistical description, and hypothesis testing, employing the full range of computational powers. Offered as COGS 330, COGS 430, DSCI 330 and DSCI 430.

DSCI 352: Applied Data Science Research (100/7891)

R 04:00-05:15 PM Jan 12-Apr 27

Tran, Q

This is a project based data science research class, in which project teams identify a research project under the guidance of a domain expert professor. The research is structured as a data analysis project including the 6 steps of developing a reproducible data science project, including 1: Define the ADS question, 2: Identify, locate, and/or generate the data 3: Exploratory data analysis 4: Statistical modeling and prediction 5: Synthesizing the results in the domain context 6: Creation of reproducible research, Including code, datasets, documentation and reports. During the course special topic lectures will include Ethics, Privacy, Openness, Security, Ethics. Value. The M section of DSCI 352 is for students focusing on Materials Data Science. Offered as DSCI 352, DSCI 352M and DSCI 452.

DSCI 352M: Applied Data Science Research (100/8000)

R 04:00-05:15 PM Jan 12-Apr 27

Tran, Q

This is a project based data science research class, in which project teams identify a research project under the guidance of a domain expert professor. The research is structured as a data analysis project including the 6 steps of developing a reproducible data science project, including 1: Define the ADS question, 2: Identify, locate, and/or generate the data 3: Exploratory data analysis 4: Statistical modeling and prediction 5: Synthesizing the results in the domain context 6: Creation of reproducible research, Including code, datasets, documentation and reports. During the course special topic lectures will include Ethics, Privacy, Openness, Security, Ethics. Value. The M section of DSCI 352 is for students focusing on Materials Data Science. Offered as DSCI 352, DSCI 352M and DSCI 452.

DSCI 353: Data Science: Statistical Learning, Modeling and Prediction (100/7890)

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

French, R

In this course, we will use an open data science tool chain to develop reproducible data analyses useful for inference, modeling and prediction of the behavior of complex systems. In addition to the standard data cleaning, assembly and exploratory data analysis steps essential to all data analyses, we will identify statistically significant relationships from datasets derived from population samples, and infer the reliability of these findings. We will use regression methods to model a number of both real-world and lab-based systems producing predictive models applicable in comparable populations. We will assemble and explore real-world datasets, use pair-wise plots to explore correlations, perform clustering, self-similarity, and logistic regression develop both fixed-effect and mixed-effect predictive models. We will introduce machine-learning approaches for classification and tree-based methods. Results will be interpreted, visualized and discussed. We will introduce the basic elements of data science and analytics using R Project open source software. R is an open-source software project with broad abilities to access machine-readable open-data resources, data cleaning and assembly functions, and a rich selection of statistical packages, used for data analytics, model development, prediction, inference and clustering. With this background, it becomes possible to start performing variable transformations for linear regression fitting and developing structural equation models, fixed-effects and mixed-effects models along with other statistical learning techniques, while exploring for statistically significant relationships. The class will be structured to have a balance of theory and practice. We'll split class into Foundation and Practicum a) Foundation: lectures, presentations, discussion b) Practicum: coding, demonstrations and hands-on data science work. The M section of DSCI 353 is for students focusing on Materials Data Science. Offered as DSCI 353, DSCI 353M and DSCI 453.

DSCI 353M: Data Science: Statistical Learning, Modeling and Prediction (100/7961)

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

French, R

In this course, we will use an open data science tool chain to develop reproducible data analyses useful for inference, modeling and prediction of the behavior of complex systems. In addition to the standard data cleaning, assembly and exploratory data analysis steps essential to all data analyses, we will identify statistically significant relationships from datasets derived from population samples, and infer the reliability of these findings. We will use regression methods to model a number of both real-world and lab-based systems producing predictive models applicable in comparable populations. We will assemble and explore real-world datasets, use pair-wise plots to explore correlations, perform clustering, self-similarity, and logistic regression develop both fixed-effect and mixed-effect predictive models. We will introduce machine-learning approaches for classification and tree-based methods. Results will be interpreted, visualized and discussed. We will introduce the basic elements of data science and analytics using R Project open source software. R is an open-source software project with broad abilities to access machine-readable open-data resources, data cleaning and assembly functions, and a rich selection of statistical packages, used for data analytics, model development, prediction, inference and clustering. With this background, it becomes possible to start performing variable transformations for linear regression fitting and developing structural equation models, fixed-effects and mixed-effects models along with other statistical learning techniques, while exploring for statistically significant relationships. The class will be structured to have a balance of theory and practice. We'll split class into Foundation and Practicum a) Foundation: lectures, presentations, discussion b) Practicum: coding, demonstrations and hands-on data science work. The M section of DSCI 353 is for students focusing on Materials Data Science. Offered as DSCI 353, DSCI 353M and DSCI 453.

DSCI 354: Data Visualization and Analytics (100/8601)

TR 10:00-11:15 AM Jan 12-Apr 27

Bruckman, L

Data Visualization and Analytics students will learn data visualization and analytics techniques focused on different types of data such as time-series, spectral, or image data science problems. This class will focus on increasing analysis of complex data sets through visualization by enhancing exploratory data analysis and data cleaning. This class will focus on creating effective data visualizations to communicate data analytics results to different audiences. Different datasets will be provided to develop different types of visualizations and analytics. Types of data visualizations include in interactive plots (e.g., bar graphs change over time), applications that allow users to adjust the visualizations based on their decisions (e.g., shiny applications), interactive maps, 3-D plots of data, etc. Discussing how an audience understands information and brings in data as well as the ethics of making data visualizations will be discussed. The class will also include ways to increase modeling and analysis with effective visualizations for credible, data-driven decision making. This will include a git repository for other students to use these codes as open source resources and the preparation of reproducible data science analyses for different types of problems. Offered as DSCI 354, DSCI 354M, and DSCI 454.

DSCI 354M: Data Visualization and Analytics (100/8676)

TR 10:00-11:15 AM Jan 12-Apr 27

Bruckman, L

Data Visualization and Analytics students will learn data visualization and analytics techniques focused on different types of data such as time-series, spectral, or image data science problems. This class will focus on increasing analysis of complex data sets through visualization by enhancing exploratory data analysis and data cleaning. This class will focus on creating effective data visualizations to communicate data analytics results to different audiences. Different datasets will be provided to develop different types of visualizations and analytics. Types of data visualizations include in interactive plots (e.g., bar graphs change over time), applications that allow users to adjust the visualizations based on their decisions (e.g., shiny applications), interactive maps, 3-D plots of data, etc. Discussing how an audience understands information and brings in data as well as the ethics of making data visualizations will be discussed. The class will also include ways to increase modeling and analysis with effective visualizations for credible, data-driven decision making. This will include a git repository for other students to use these codes as open source resources and the preparation of reproducible data science analyses for different types of problems. Offered as DSCI 354, DSCI 354M, and DSCI 454.

DSCI 355: Applied Data Science (ADS) Tooling: Software Development, Infrastructure and Data Management (100/10915)

MW 12:45-02:00 PM Jan 12-Apr 27

Barcelos, E

This is an introductory course to provide practical knowledge and resources in Applied Data Sciences that can be applied to different areas where code is developed for data analysis and modeling. This course focuses on practical aspects of Applied Data Science to complement the traditional ADS curriculum. When new code, pipelines, algorithms and models are developed, they need to be implemented, scalable, reusable, understandable and efficient. Most of those aspects are not traditionally covered in core ADS classes. This course proposes to fill the gap in some important areas which includes creating and publishing code packages and R and Python, agile software development, coding good practices and documentation, version control (git), Data Management, foundations of infrastructure and advanced deep learning topics. This course represents an opportunity for students to learn useful applied data science tools to boost their careers and provide practical experience in developing data science solutions and applications. Graduate students will work on a hands-on open source project which could cover different aspects of the course depending on their interest. One example is making a R/Python package based on their research or developing an automated data analysis pipeline with efficient and well documented code. ADS Tooling is an introductory course that provides foundational and practical concepts and implementations of data science technologies useful to any domain, and therefore the course is open to students in any school. Offered as DSCI 355 and DSCI 455.

DSCI 430: Cognition and Computation (100/8603)

MW 03:20-04:35 PM Jan 12-Apr 27

Turner, M

An introduction to (1) theories of the relationship between cognition and computation; (2) computational models of human cognition (e.g. models of decision-making or concept creation); and (3) computational tools for the study of human cognition. All three dimensions involve AI and data science: theories compare natural and artificial intelligence and are tested against archives of data from brain imagining to linguistic corpora; models are derived from and tested against datasets of e.g., financial decisions (markets), legal rulings and findings (juries, judges, courts), legislative actions, and healthcare decisions, and moreover are often constructed via AI; computational tools aggregate data and operate upon it analytically, for search, recognition, tagging, machine learning, statistical description, and hypothesis testing, employing the full range of computational powers. Offered as COGS 330, COGS 430, DSCI 330 and DSCI 430.

DSCI 452: Applied Data Science Research (100/7960)

R 04:00-05:15 PM Jan 12-Apr 27

Tran, Q

This is a project based data science research class, in which project teams identify a research project under the guidance of a domain expert professor. The research is structured as a data analysis project including the 6 steps of developing a reproducible data science project, including 1: Define the ADS question, 2: Identify, locate, and/or generate the data 3: Exploratory data analysis 4: Statistical modeling and prediction 5: Synthesizing the results in the domain context 6: Creation of reproducible research, Including code, datasets, documentation and reports. During the course special topic lectures will include Ethics, Privacy, Openness, Security, Ethics. Value. The M section of DSCI 352 is for students focusing on Materials Data Science. Offered as DSCI 352, DSCI 352M and DSCI 452.

DSCI 452: Applied Data Science Research (800/8141)

Barcelos, E

This is a project based data science research class, in which project teams identify a research project under the guidance of a domain expert professor. The research is structured as a data analysis project including the 6 steps of developing a reproducible data science project, including 1: Define the ADS question, 2: Identify, locate, and/or generate the data 3: Exploratory data analysis 4: Statistical modeling and prediction 5: Synthesizing the results in the domain context 6: Creation of reproducible research, Including code, datasets, documentation and reports. During the course special topic lectures will include Ethics, Privacy, Openness, Security, Ethics. Value. The M section of DSCI 352 is for students focusing on Materials Data Science. Offered as DSCI 352, DSCI 352M and DSCI 452.

DSCI 453: Data Science: Statistical Learning, Modeling and Prediction (100/7892)

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

French, R

In this course, we will use an open data science tool chain to develop reproducible data analyses useful for inference, modeling and prediction of the behavior of complex systems. In addition to the standard data cleaning, assembly and exploratory data analysis steps essential to all data analyses, we will identify statistically significant relationships from datasets derived from population samples, and infer the reliability of these findings. We will use regression methods to model a number of both real-world and lab-based systems producing predictive models applicable in comparable populations. We will assemble and explore real-world datasets, use pair-wise plots to explore correlations, perform clustering, self-similarity, and logistic regression develop both fixed-effect and mixed-effect predictive models. We will introduce machine-learning approaches for classification and tree-based methods. Results will be interpreted, visualized and discussed. We will introduce the basic elements of data science and analytics using R Project open source software. R is an open-source software project with broad abilities to access machine-readable open-data resources, data cleaning and assembly functions, and a rich selection of statistical packages, used for data analytics, model development, prediction, inference and clustering. With this background, it becomes possible to start performing variable transformations for linear regression fitting and developing structural equation models, fixed-effects and mixed-effects models along with other statistical learning techniques, while exploring for statistically significant relationships. The class will be structured to have a balance of theory and practice. We'll split class into Foundation and Practicum a) Foundation: lectures, presentations, discussion b) Practicum: coding, demonstrations and hands-on data science work. The M section of DSCI 353 is for students focusing on Materials Data Science. Offered as DSCI 353, DSCI 353M and DSCI 453.

DSCI 453: Data Science: Statistical Learning, Modeling and Prediction (800/8142)

French, R

In this course, we will use an open data science tool chain to develop reproducible data analyses useful for inference, modeling and prediction of the behavior of complex systems. In addition to the standard data cleaning, assembly and exploratory data analysis steps essential to all data analyses, we will identify statistically significant relationships from datasets derived from population samples, and infer the reliability of these findings. We will use regression methods to model a number of both real-world and lab-based systems producing predictive models applicable in comparable populations. We will assemble and explore real-world datasets, use pair-wise plots to explore correlations, perform clustering, self-similarity, and logistic regression develop both fixed-effect and mixed-effect predictive models. We will introduce machine-learning approaches for classification and tree-based methods. Results will be interpreted, visualized and discussed. We will introduce the basic elements of data science and analytics using R Project open source software. R is an open-source software project with broad abilities to access machine-readable open-data resources, data cleaning and assembly functions, and a rich selection of statistical packages, used for data analytics, model development, prediction, inference and clustering. With this background, it becomes possible to start performing variable transformations for linear regression fitting and developing structural equation models, fixed-effects and mixed-effects models along with other statistical learning techniques, while exploring for statistically significant relationships. The class will be structured to have a balance of theory and practice. We'll split class into Foundation and Practicum a) Foundation: lectures, presentations, discussion b) Practicum: coding, demonstrations and hands-on data science work. The M section of DSCI 353 is for students focusing on Materials Data Science. Offered as DSCI 353, DSCI 353M and DSCI 453.

DSCI 454: Data Visualization and Analytics (100/8667)

TR 10:00-11:15 AM Jan 12-Apr 27

Bruckman, L

Data Visualization and Analytics students will learn data visualization and analytics techniques focused on different types of data such as time-series, spectral, or image data science problems. This class will focus on increasing analysis of complex data sets through visualization by enhancing exploratory data analysis and data cleaning. This class will focus on creating effective data visualizations to communicate data analytics results to different audiences. Different datasets will be provided to develop different types of visualizations and analytics. Types of data visualizations include in interactive plots (e.g., bar graphs change over time), applications that allow users to adjust the visualizations based on their decisions (e.g., shiny applications), interactive maps, 3-D plots of data, etc. Discussing how an audience understands information and brings in data as well as the ethics of making data visualizations will be discussed. The class will also include ways to increase modeling and analysis with effective visualizations for credible, data-driven decision making. This will include a git repository for other students to use these codes as open source resources and the preparation of reproducible data science analyses for different types of problems. Offered as DSCI 354, DSCI 354M, and DSCI 454.

DSCI 455: Applied Data Science (ADS) Tooling: Software Development, Infrastructure and Data Management (100/10976)

MW 12:45-02:00 PM Jan 12-Apr 27

Barcelos, E

This is an introductory course to provide practical knowledge and resources in Applied Data Sciences that can be applied to different areas where code is developed for data analysis and modeling. This course focuses on practical aspects of Applied Data Science to complement the traditional ADS curriculum. When new code, pipelines, algorithms and models are developed, they need to be implemented, scalable, reusable, understandable and efficient. Most of those aspects are not traditionally covered in core ADS classes. This course proposes to fill the gap in some important areas which includes creating and publishing code packages and R and Python, agile software development, coding good practices and documentation, version control (git), Data Management, foundations of infrastructure and advanced deep learning topics. This course represents an opportunity for students to learn useful applied data science tools to boost their careers and provide practical experience in developing data science solutions and applications. Graduate students will work on a hands-on open source project which could cover different aspects of the course depending on their interest. One example is making a R/Python package based on their research or developing an automated data analysis pipeline with efficient and well documented code. ADS Tooling is an introductory course that provides foundational and practical concepts and implementations of data science technologies useful to any domain, and therefore the course is open to students in any school. Offered as DSCI 355 and DSCI 455.