Each course will involve a 4-hour morning session focused on instructor presentation of the material.
Visit our website for more information: https://uiowa.edu/datascience.
Follow the below link to register for the January Data Science Institute. There is no fee associated with the institute. You can register for all the sessions or individual sessions (see below).
The number of seats per session is limited to 50. There will be a waitlist accessed through the registration survey after the quota is filled for each session.
January 9, 2017 - 8:30 Am - 12:30 Pm: Introduction to Python Data Analytics - Dr. Kang Pyo Lee
This introductory class will cover the basic elements in Python data analytics and provide hands-on practice. The topics to be covered include an introduction to data analytics and Python, Python basics for data analysis, and data preparation/exploration/visualization using widely used data analytics libraries in Python. Participants will also be able to learn how to take advantage of an interactive web-based data analytics environment called Jupyter Notebook. No prior experience in Python is necessary for this class. Participants will be expected to bring their own laptops.
January 10, 2017 - 8:30 Am - 12:30 Pm: Introduction to Tidyverse/R - Dr. Brandon Lebeau
This hands on course will provide an introduction to the tidyverse, a data analysis framework in R that provides users a simple vocabulary to data analysis. Topics covered will include: introduction to data visualization, data management, and exploratory data analysis using the tidyverse. This course is intended to be hands on, therefore attendees are encouraged to bring a laptop with R and RStudio already installed. No prior R knowledge is needed.
January 11, 2017 - 8:30 Am - 12:30 Pm: Introduction to Stata - Dr. Frederick J. Boehmke
This introductory course in Stata will cover the basics of data preparation and manipulation in Stata. Topics addressed will include importing data, merging and combining data sets, cleaning data, variables, variable manipulations, loops, and data visualization.