Study Data Analytics this Fall at WashU

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Think like a data scientist. There are data everywhere, you just have to know where to look. These courses will help you learn about data and analytics — also known as “the new oil.”

Introduction to Statistics

Instructor: TBA
(U20 Math 1011)

This course covers basic concepts of statistics, including data collection (sampling and designing experiments), data organization (tables, graphs, frequency distributions, numerical summarization of data), and statistical inference (elementary probability and hypothesis testing). Prerequisite: high school algebra. This course is not equivalent to L24 1011.

Quantitative Reasoning

Bryn Lutes
(U74 Sci 117M)

The objective of this course is to help students develop the ability to reason and think quantitatively and critically in order to make informed decisions about issues that they will confront in their personal lives and in their lives as citizens. It will provide students with the quantitative skills needed in their future college course work and their careers. In addition, it will emphasize written and oral communication.

Data Science

Learn the core tools of the trade for data scientists — programming and statistics.

Programming with Python

Mark Pedigo, Jon Corbett
(U82 CIM 133)

An introductory course for students with little or no programming experience. Topics include the software development process, documentation, debugging, and testing within the commonly used Python environment. At the end of the course students should be able to write and debug basic programs to display and interpret data using accepted programming conventions and styles.

Applied Statistics

Dave Dixon, Michael Yingling
(U20 Math 205)

This is a first course in statistics with examples and applications from a variety of disciplines, and emphasis on the social, behavioral, and natural sciences. Students learn about key topics and statistical methods that may be applied to areas such as economics, mathematics, psychology, business, and health sciences, to name a few. The course provides a foundation in descriptive and inferential statistics, and in probability. Students learn numerical and graphical methods of describing data and study some of the more common distributions. Topics include hypothesis testing, confidence-interval estimation, correlation, regression, analysis of variance, contingency tables, quality control, and nonparametric statistics. This course may be applied to University College majors in economics, managerial economics, mathematics, and political science. This course is entirely web-based, with all course components online.

Data Visualization 

Learn how to make meaningful visual representations for data-informed decision-making.

Data Analysis and Visualization in Tableau

Manan Shroff
(U44 Bus 206)

In this course, students will create interactive visualizations in order to gain meaningful insights about a dataset. Students will learn the basic functions of Tableau, including filtering, sorting, formatting common chart types, and visualization aesthetics. Advanced topics will include dashboard actions, calculation functions, and parameters. Students will also learn to explore, dissect, and reproduce existing visualizations created by Tableau experts. A variety of datasets will be provided but students will also have the opportunity to bring in their own datasets for analysis. This course is ideal for students who have an understanding of descriptive statistics and have analyzed datasets using other data tools (Excel, R, SPSS, SAS, etc.).

Geo-Spatial Analytics

Learn how to make beautiful maps that also tell a location-based story and inform decision makers in the public and private sectors.

Introduction to GIS

Jennifer Moore
(U82 CIM 200)

This course introduces students to the fundamental principles and applications of geographic information systems (GIS), their underlying geospatial science and spatial thinking. This problem-based course explores applications of GIS to spatial questions in the areas of social science, business, the humanities and earth sciences. Example topics include understanding spatial data types; map coordinate systems and projections; basic spatial data analysis; acquiring, editing, creating and managing geospatial data; and processing and visualizing data using GIS. This hands-on course works through problems using (mainly) ESRI ArcGIS software (including ArcMap and ArcCatalog), but other open-source tools will also be introduced. Students who complete this course should be able to apply skills to think through a spatial problem and employ GIS tools to address it.

Advanced GIS

Mollie Webb
(U82 CIM 300)

This course is designed to move beyond fundamental data presentation and map production skills. Primary emphasis will be on applying fundamental GIS concepts, performing spatial analysis, developing proficiency with GIS software applications, resolution of problems, and efficient delivery of results. A semester project will provide experience in the planning and execution of real-world projects using geospatial technology. Course objectives include applying fundamental GIS concepts, performing spatial analysis, developing proficiency with core ArcGIS software and selected extensions, resolution of problems, and efficient delivery of results. Completion of an introductory level GIS course is a prerequisite.

Spatial Data Modeling and Design

Derek Scott
(U82 CIM 421)

This course expands on the fundamental principles of geographic information systems (GIS) and introduces advanced spatial database concepts and a visual programming environment for automating geoprocessing tasks. The course is divided into two parts: the first exploring spatial database design with emphasis on the ESRI Geodatabase, and the second focusing on automating workflows using ESRI ModelBuilder. Topics include data needs assessment; conceptual modeling, logical design, and physical implementation; using models to perform multi-step spatial analyses; and the automation of repetitive processes with iteration tools. Lectures are supplemented with lab exercises to develop proficiency and problem-solving skills using ArcGIS software and associated tools.

Public Sector Applications of GIS

Briana Shawver
(U82 CIM 427)

This course examines the use of geographic information systems (GIS) and geographic information science in the public sector, with a focus on GIS applications in local government and municipality services. The course requires an understanding of fundamental GIS principles, and will address practical application of fundamental and advanced GIS concepts and practices. Topics addressed include GIS implementation at the organization and department levels, problem solving with GIS, and geospatial project management. Lectures are integrated with lab sessions using GIS software including ArcGIS, Google Earth, and Open Street Map.

GIS Clinic

Bill Winston, Mollie Webb
(U82 CIM 422)

The GIS Clinic is the culminating experience in the GIS Certificate Program. Students complete a project in a real work setting to provide direct experience with geospatial concepts and data. Students apply concepts and tools covered in all courses comprising the GIS Certificate program. GIS Clinic requires students to work on projects beginning to end, under supervision, and independently. The Clinic provides professional services to the University Community as well as outside organizations. Possible clinic settings include working with faculty on research projects using GIS, working with local organizations to develop GIS data, and working on regional GIS initiatives.

Heath Care Analytics

Apply data science tools to the essential question of today like how to get ready for the next pandemic.

Introduction Psychological Statistics

Shelly Cooper
(U09 Psych 300)

Descriptive statistics including correlation and regression. Inferential statistics including non-parametric and parametric tests of significance through two-way analysis of variance. Course emphasizes underlying logic and is not primarily mathematical, although knowledge of elementary algebra is essential. PREREQ: Psych 100.

Education Analytics

Learn how data science can be applied to education.

Introduction to Educational Tests and Measurements

Lauren Rea Preston
(U08 Educ 4610)

Basic concepts of tests and measurements for teachers (and other school personnel). Topics: test reliability and validity; fundamentals of test construction and standardization; analysis of major types of group tests used in schools, including achievement and aptitude tests; meaning and interpretation of test scores; development of school testing programs. Teacher-made tests a central concern. Prerequisite: Educ 4052 or the equivalent, or with instructor approval.