Werner Stuetzle (wxs@uw.edu)
Introduction to Data Science (IDS) is a survey course introducing the essential elements of data science: data collection and management; summarizing and visualizing data; basic ideas of statistical inference; machine learning. Students will gain hands-on experience using the Python programming language and Jupyter notebooks. IDS can be viewed as a hybrid between a computing course focused on programming and algorithms and a statistics course focusing on estimation and inference.
Many important decisions made by individuals and society at large are or should be data driven. Therefore, understanding the fundamentals of data science is essential for functioning as an informed citizen.
Basic understanding and hands-on experience with manipulating, analyzing, and presenting data are increasingly important during education and in the workplace.
Students completing STAT 180 should be able to
list the steps involved in data science from data acquisition to insight and describe the role of each step;
distinguish different ways of collecting data and their impact on the conclusions that can be drawn from the data;
manage, summarize and visualize data using the Python programming language and Jupyter notebooks;
explain the basic concepts of statistial inference and implement simulation based inference methods;
apply machine learning methods and assess the quality of predictions.
All undergraduates, not just students with primary interest in science or engineering. No previous exposure to programming or statistics is expected.
Either a minimum grade of 2.5 in MATH 098, a minimum grade of 3.0 in MATH 103, a score of 151-169 on the MPT-GS placement test, or score of 145-153 on the MPT-AS placement test.
"Computational and Inferential Thinking" by Ani Adhikari and John DeNero. Free download from https://www.inferentialthinking.com/chapters/intro
"Introductory Statistics with Randomization and Simulation" by David M. Diez, Christopher D. Barr, and Mine Cetinkaya-Rundel. Free download from https://www.openintro.org/stat/
Python and Jupyter notebooks will be used for class notes, in-class demos, homeworks, and labs. Students will have access to a JupyterHub allowing them to work with notebooks through a web browser. While Python is available free of charge for those wo wish to install it on their private computers, doing so is not required.
If you have already established accommodations with Disability Resources for Students (DRS), please communicate your approved accommodations to me at your earliest convenience so we can discuss your needs in this course.
If you have not yet established services through DRS, but have a temporary health condition or permanent disability that requires accommodations (conditions include but not limited to; mental health, attention-related, learning, vision, hearing, physical or health impacts), you are welcome to contact DRS at 206-543-8924 or uwdrs@uw.edu or disability.uw.edu. DRS offers resources and coordinates reasonable accommodations for students with disabilities and/or temporary health conditions. Reasonable accommodations are established through an interactive process between you, your instructor(s) and DRS. It is the policy and practice of the University of Washington to create inclusive and accessible learning environments consistent with federal and state law.
Students at the University of Washington (UW) are expected to maintain the highest standards of academic conduct, professional honesty, and personal integrity. Plagiarism, cheating, and other misconduct are serious violations of the University of Washington Student Conduct Code (WAC 478-120). Any suspected cases of academic misconduct will be handled according to University of Washington regulations. For more information, see the University of Washington Community Standards and Student Conduct website.