Subject: DSCI

Term: Fall 2019

DSCI 234: Structured and Unstructured Data (100/11340)

TR 08:30-09:45 AM Aug 26-Dec 06

Wu, Y

This course is an introduction to types of data and their representation, storage, processing and analysis. The course has three parts. In the first part of the course, students will develop a basic understanding and the ability to represent, store, process and analyze structured data. Structured data include catalogs, records, tables, logs, etc., with a fixed dimension and well-defined meaning for each data point. Suitable representation and storage mechanisms include lists and arrays. Relevant techniques include keys, hashes, stacks, queues and trees. In the second part of the course, students will develop a basic understanding and the ability to represent, store, process and analyze semi-structured data. Semi-structured data include texts, web pages and networks, without a dimension and structure, but with well-defined meaning for each data point. Suitable representation and storage mechanisms include trees, graphs and RDF triples. Relevant techniques include XML, YAML, JSON, parsing, annotation, language processing. In the third part of the course, students will develop a basic understanding and the ability to represent, store, process and analyze unstructured data. Unstructured data include images, video, and time series data, without neither a fixed dimension and structure, nor well-defined meaning for individual data points. Suitable representation and storage mechanisms include large matrices, EDF, DICOM. Relevant techniques include feature extraction, segmentation, clustering, rendering, indexing, and visualization.

DSCI 343: Introduction to Data Analysis (100/10927)

MWF 02:15-03:05 PM Aug 26-Dec 06

Koyuturk, M

In this class we will give a broad overview of data analysis techniques, covering techniques from data mining, machine learning and signal processing. Students will also learn about probabilistic representations, how to conduct an empirical study and support empirical hypotheses through statistical tests, and visualize the results. Course objectives: -Expose students to different analysis approaches. -Understand probabilistic representations and inference mechanisms. -Understand how to create empirical hypotheses and how to test them.

DSCI 351: Exploratory Data Science (100/4760)

TR 11:30-12:45 PM Aug 26-Dec 06

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 (100/4934)

TR 11:30-12:45 PM Aug 26-Dec 06

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 (100/4756)

F 12:45-02:00 PM Aug 26-Dec 06

Bruckman, L

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 (100/5034)

F 12:45-02:00 PM Aug 26-Dec 06

Bruckman, L

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 432: Introduction to Geostatistics for Subsurface Modeling; Applications to Oil and Gas (100/11197)

TR 10:00-11:15 AM Aug 26-Dec 06

Yarus, J

Geostatistics and subsurface modeling are essential components of evaluating the oil and gas opportunities contained in natural reservoirs, such as the Utica and Marcellus Shale formations of the Appalachian Basin. Reservoir characterization technology has changed dramatically over the last decade. Reservoir modeling software now has a wide range of powerful statistical and geostatistical functionality and has spread rapidly through the industry as PCs have become faster and user interfaces have simplified the application of complex methods. However, the understanding required to make optimum use of this functionality has not kept pace, and users continue to struggle with understanding many fundamental principles. Hence, a number of misunderstandings and poor workflows have become common place. This course addresses these misunderstandings and poor workflows in order to introduce effective reservoir modeling. This course delivers expertise in the applied geostatistical methods that are an essential underpinnings of effective modern reservoir characterization and modeling. Variograms, kriging and stochastic simulation are thoroughly explained from their basics upwards and illustrated in their application to modeling problems.

DSCI 451: Exploratory Data Science (100/4761)

TR 11:30-12:45 PM Aug 26-Dec 06

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 (100/4857)

F 12:45-02:00 PM Aug 26-Dec 06

Bruckman, L

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.