Applied Data Science (DSCI)

DSCI 330. Cognition and Computation. 3 Units.

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 332. Geospatial Data Science: Explore, Analyze and Model Spatial, Temporal & Spatiotemporal Data. 3 Units.

This course on Geospatial Data Science focuses on leveraging Data Science Analytical Tools and Open Source Software to explore, process, integrate, analyze, visualize and model geospatial data targeting diverse applications. Students will learn the basic tools in R based code for Exploratory Data Analysis, Data Integration, Visualization of Geospatial Data and Data Modeling. QGIS will also be introduced in this course as an additional open-source tool for data visualization and manipulation. Students will work on geospatial datasets (air quality, water contamination, soil analysis, rocks properties, energy, transportation, public health, agriculture etc) developing entire data science pipelines from data collection to modeling(spatial, temporal or spatiotemporal). Students will be expected to learn the navigation of R Studio and develop data science pipelines using geospatial data. We include Vertex AI integration with Markov Cluster that enables students to leverage LLMs as code assistants. Students will be expected to learn the above and develop a 10 week modeling project focused on the use of spatial modeling methods with R using data relevant to their specific discipline or interest. Resulting scripts will be placed in a git repository for use by other students as open source resources along with documentation demonstrating the reproducible spatial modeling science and analyses for these problems. Examples of graduate projects from previous classes include subsurface modeling (geology), air quality monitoring, effects of air temperature on health and mortality rates(environment/health), earthquake mapping (geophysics/civil engineering), soil stability modeling (civil engineering), aquifer characterization (hydrology), pollution/contaminant mapping (environmental studies/medicine), water quality, crop growth(agriculture), predicting health outcomes integration with socioeconomic and environmental data. Offered as DSCI 332 and DSCI 432.

DSCI 351. Exploratory Data Science. 3 Units.

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. Prereq: (ENGR 130 or ENGR 131 or CSDS 132 or ECSE 132 or DSCI 134) and (STAT 312R or STAT 201R or SYBB 310 or PQHS 431).

DSCI 351M. Exploratory Data Science. 3 Units.

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. Prereq: (ENGR 130 or ENGR 131 or CSDS 132 or ECSE 132 or DSCI 134) and (STAT 312R or STAT 201R or SYBB 310 or PQHS 431).

DSCI 352. Applied Data Science Research. 3 Units.

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. For enrollment, students should provide an abstract of their research plan and data science question that they will address in the course as well as potential datasets that will be used. 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. Prereq: DSCI 351 or DSCI 351M or DSCI 451.

DSCI 352M. Applied Data Science Research. 3 Units.

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. For enrollment, students should provide an abstract of their research plan and data science question that they will address in the course as well as potential datasets that will be used. 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. Prereq: DSCI 351 or DSCI 351M or DSCI 451.

DSCI 353. Statistical and Machine Learning for Inference, Prediction and Reasoning. 3 Units.

In this course, we will use an open data science tool chain to develop reproducible data analyses useful for statistical and machine learning modeling for inference, prediction and reasoning on 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, perform clustering, self-similarity, and dimension reduction and linear and logistic regression to develop both fixed-effect and mixed-effect predictive models. We will introduce machine-learning approaches for classification and tree-based methods. We will use deep learning methods such as TensorFlow and PyTorch to develop neural network models of complex systems. 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 and deep learning packages, used for data analytics, model development, inference prediction and reasoning. With this background, it becomes possible to train linear regression, structural equation, fixed-effects and mixed-effects models along with other machine and deep learning models, while exploring statistically significant relationships. The class will be structured to have a balance of theory and practice. We split class sessions 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. Prereq: DSCI 351 or DSCI 351M.

DSCI 353M. Statistical and Machine Learning for Inference, Prediction and Reasoning. 3 Units.

In this course, we will use an open data science tool chain to develop reproducible data analyses useful for statistical and machine learning modeling for inference, prediction and reasoning on 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, perform clustering, self-similarity, and dimension reduction and linear and logistic regression to develop both fixed-effect and mixed-effect predictive models. We will introduce machine-learning approaches for classification and tree-based methods. We will use deep learning methods such as TensorFlow and PyTorch to develop neural network models of complex systems. 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 and deep learning packages, used for data analytics, model development, inference prediction and reasoning. With this background, it becomes possible to train linear regression, structural equation, fixed-effects and mixed-effects models along with other machine and deep learning models, while exploring statistically significant relationships. The class will be structured to have a balance of theory and practice. We split class sessions 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. Prereq: DSCI 351 or DSCI 351M.

DSCI 354. Data Visualization and Analytics. 3 Units.

This course explores advanced techniques for visualizing and analyzing complex datasets, including point-in time, time-series, spectral, and image data. Students will enhance their exploratory data analysis (EDA) and data cleaning workflows to transform raw information into insights for communication and decision making. A component of this course will focus on creating interactive visualizations (e.g., dynamic plots, Shiny applications, and 3D models). The goal of the course is to develop data visualizations that are tailored for diverse audiences. A mixed reality component is included in this course so students learn to develop visualizations within an immersive environment. Beyond technical skills, the course examines the ethics of data representation and the theory of how audiences interpret information. This course uses a Git repository, open-source resources, and reproducible data science tooling. Offered as DSCI 354, DSCI 354M, and DSCI 454. Prereq: (DSCI 351 or DSCI 351M) and (DSCI 353 or DSCI 353M).

DSCI 354M. Data Visualization and Analytics. 3 Units.

This course explores advanced techniques for visualizing and analyzing complex datasets, including point-in time, time-series, spectral, and image data. Students will enhance their exploratory data analysis (EDA) and data cleaning workflows to transform raw information into insights for communication and decision making. A component of this course will focus on creating interactive visualizations (e.g., dynamic plots, Shiny applications, and 3D models). The goal of the course is to develop data visualizations that are tailored for diverse audiences. A mixed reality component is included in this course so students learn to develop visualizations within an immersive environment. Beyond technical skills, the course examines the ethics of data representation and the theory of how audiences interpret information. This course uses a Git repository, open-source resources, and reproducible data science tooling. Offered as DSCI 354, DSCI 354M, and DSCI 454. Prereq: (DSCI 351 or DSCI 351M) and (DSCI 353 or DSCI 353M).

DSCI 355. Applied Data Science (ADS) Tooling: Data Management, Open Source Packages and Infrastructure. 3 Units.

This is an introductory course to provide practical knowledge and resources in Applied Data Sciences(ADS) Tools 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 LLMs as code assistants. 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. Students should have experience in R or Python to enroll in this course. Offered as DSCI 355 and DSCI 455. Prereq: (DSCI 351 or DSCI 351M or DSCI 451) and (DSCI 353 or DSCI 353M or DSCI 453) or Requisites Not Met permission.

DSCI 430. Cognition and Computation. 3 Units.

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 432. Geospatial Data Science: Explore, Analyze and Model Spatial, Temporal & Spatiotemporal Data. 3 Units.

This course on Geospatial Data Science focuses on leveraging Data Science Analytical Tools and Open Source Software to explore, process, integrate, analyze, visualize and model geospatial data targeting diverse applications. Students will learn the basic tools in R based code for Exploratory Data Analysis, Data Integration, Visualization of Geospatial Data and Data Modeling. QGIS will also be introduced in this course as an additional open-source tool for data visualization and manipulation. Students will work on geospatial datasets (air quality, water contamination, soil analysis, rocks properties, energy, transportation, public health, agriculture etc) developing entire data science pipelines from data collection to modeling(spatial, temporal or spatiotemporal). Students will be expected to learn the navigation of R Studio and develop data science pipelines using geospatial data. We include Vertex AI integration with Markov Cluster that enables students to leverage LLMs as code assistants. Students will be expected to learn the above and develop a 10 week modeling project focused on the use of spatial modeling methods with R using data relevant to their specific discipline or interest. Resulting scripts will be placed in a git repository for use by other students as open source resources along with documentation demonstrating the reproducible spatial modeling science and analyses for these problems. Examples of graduate projects from previous classes include subsurface modeling (geology), air quality monitoring, effects of air temperature on health and mortality rates(environment/health), earthquake mapping (geophysics/civil engineering), soil stability modeling (civil engineering), aquifer characterization (hydrology), pollution/contaminant mapping (environmental studies/medicine), water quality, crop growth(agriculture), predicting health outcomes integration with socioeconomic and environmental data. Offered as DSCI 332 and DSCI 432.

DSCI 451. Exploratory Data Science. 3 Units.

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. 3 Units.

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. For enrollment, students should provide an abstract of their research plan and data science question that they will address in the course as well as potential datasets that will be used. 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. Statistical and Machine Learning for Inference, Prediction and Reasoning. 3 Units.

In this course, we will use an open data science tool chain to develop reproducible data analyses useful for statistical and machine learning modeling for inference, prediction and reasoning on 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, perform clustering, self-similarity, and dimension reduction and linear and logistic regression to develop both fixed-effect and mixed-effect predictive models. We will introduce machine-learning approaches for classification and tree-based methods. We will use deep learning methods such as TensorFlow and PyTorch to develop neural network models of complex systems. 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 and deep learning packages, used for data analytics, model development, inference prediction and reasoning. With this background, it becomes possible to train linear regression, structural equation, fixed-effects and mixed-effects models along with other machine and deep learning models, while exploring statistically significant relationships. The class will be structured to have a balance of theory and practice. We split class sessions 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. 3 Units.

This course explores advanced techniques for visualizing and analyzing complex datasets, including point-in time, time-series, spectral, and image data. Students will enhance their exploratory data analysis (EDA) and data cleaning workflows to transform raw information into insights for communication and decision making. A component of this course will focus on creating interactive visualizations (e.g., dynamic plots, Shiny applications, and 3D models). The goal of the course is to develop data visualizations that are tailored for diverse audiences. A mixed reality component is included in this course so students learn to develop visualizations within an immersive environment. Beyond technical skills, the course examines the ethics of data representation and the theory of how audiences interpret information. This course uses a Git repository, open-source resources, and reproducible data science tooling. Offered as DSCI 354, DSCI 354M, and DSCI 454. Prereq: DSCI 451 and DSCI 453.

DSCI 455. Applied Data Science (ADS) Tooling: Data Management, Open Source Packages and Infrastructure. 3 Units.

This is an introductory course to provide practical knowledge and resources in Applied Data Sciences(ADS) Tools 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 LLMs as code assistants. 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. Students should have experience in R or Python to enroll in this course. Offered as DSCI 355 and DSCI 455. Prereq: (DSCI 351 or DSCI 351M or DSCI 451) and (DSCI 353 or DSCI 353M or DSCI 453) or Requisites Not Met permission.