Schedule and Details
9.45 - 10.45 am: Data Management I
This talk demonstrates how to use many of Stata’s data management commands to clean a dataset. Topics covered include converting numbers stored as strings to numbers stored as numbers, converting dates stored as strings to formatted date variables, labeling variables and values, identifying and replacing unusual data, converting missing value codes to missing values, and identifying duplicate records in a dataset.
11 am - 12 pm: Data Management II
This talk is a continuation of Data Management 1. It covers topics such as importing data from the internet, appending and merging files, using frames to work with multiple datasets simultaneously, reshaping data from “long” format to “wide” and vice versa, using contract and collapse to create summary data, using egen to create new variables, and using -by- and -if- to work with subsets of data.
1.15-2.15 pm: Using Python Within Stata
Python is an increasingly popular programming language that can be used for many statistical and nonstatistical applications. This talk will demonstrate how to use Python interactively within Stata, how to execute Python scripts within Stata, how to use Python in Stata do-files, and how to integrate Python into Stata commands and .ado files. No prior experience with Python is necessary.
2.30-4.30 pm: Introduction to Machine Learning and AI Using Stata
This talk will briefly review the history of machine learning (ML) and artificial intelligence (AI), introduce the concepts and jargon, and demonstrate how to use these tools in Stata. Specific examples will include Lasso and elasticnet methods, Bayesian methods and MCMC, support vector machines (SVM) using Python integration, random forests and gradient boosting machines using H2O, and the user-written commands “chatgpt”, “claude”, “gemini”, and “grok”.
