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STAT265

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STAT 265 - Forecasting

StatisticsGraduateCDSS - Clg of Comp Data Sci & Society

Subject

STAT

Course Number

265

Department

Statistics

Course Level

Graduate

Course Title

Forecasting

Course Description

Forecasting has been used to predict elections, climate change, and the spread of COVID-19. Poor forecasts led to the 2008 financial crisis. In our daily lives, good forecasting ability can help us plan our work, be on time to events, and make informed career decisions. This practically-oriented class will provide students with tools to make good forecasts, including Fermi estimates, calibration training, base rates, scope sensitivity, and power laws.

Minimum

3

Maximum

3

Grading Basis

Default Letter Grade; S/U Option

Prerequisites

Stat 134, Data/Stat C140, EECS 126, Math 106, IND ENG 172, or equivalent; and familiarity with Python; or consent of instructor. Strongly Recommended: Compsci 61A, Data/Compsci C88C, or equivalent.

Repeat Rules

Course is not repeatable for credit.

Credit Restriction Courses. Students will receive no credit for this course if following the course(s) have already been completed.

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Credit Replacement Courses. Upon passing, students can use the following course(s) to replace a deficient grade for this course.

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Course Objectives

We’ll discuss several historical instances of successful and unsuccessful forecasts, and practice making forecasts about our own lives, about current events, and about scientific progress.

Student Learning Outcomes

Identify well-defined versus poorly-defined forecasting questions. Formulate questions that are relevant to their own life or work. Provide forecasts that are well-calibrated. Utilize a variety of forecasting tools, such as base rates, to improve their forecasts. Use forecasts to inform decisions. Understand how forecasts evolve across time in response to new information. Understand common forecasting pitfalls, such as improper independence assumptions, and how to identify and guard against them. Work in teams to improve forecasts. Utilize and filter data across a variety of sources to inform their forecasts.

Formats

Lecture

Term

Fall and Spring

Duration (in weeks)

15

Minimum Hours

3

Maximum Hours

3

Lecture Mode of Instruction

In Person

Minimum Hours

7

Maximum Hours

7