Fall 2022
Introduction to SQL
(what SQL is; MySQL syntax; how to use MySQL in actual development)
(what SQL is; MySQL syntax; how to use MySQL in actual development)
Hopelessly Computational: Blue Team Dynamics
(confounding algorithms; web data mining; digital disease surveillance; forecasting)
(confounding algorithms; web data mining; digital disease surveillance; forecasting)
Hopelessly Computational: Wisdom of Crowds
(crowdsourcing; collective intelligence; software design; Internet technology; data generation)
(crowdsourcing; collective intelligence; software design; Internet technology; data generation)
Spring 2022
Introduction to Computational Social Science
(introduction to the field; approaches and methods; case study; data visualization demo)
(introduction to the field; approaches and methods; case study; data visualization demo)
Fall 2021
Python: Introduction to Python
(what is Python; Python environments; variables; operators; statements; comments)
(what is Python; Python environments; variables; operators; statements; comments)
Python: Introduction to Data Structures
(Python collections: lists, tuples, sets, dictionaries; iterations and loops; functions)
(Python collections: lists, tuples, sets, dictionaries; iterations and loops; functions)
Python: Introduction to Data Analysis with Python
(I/O; data manipulation with NumPy and Pandas; exploratory data analysis; basic data visualization)
(I/O; data manipulation with NumPy and Pandas; exploratory data analysis; basic data visualization)
DataViz: Intro to Data Visualization on Web Interfaces
(what is data visualization on web interfaces; choosing open source libraries; Plotly documentations; misleading graphs)
(what is data visualization on web interfaces; choosing open source libraries; Plotly documentations; misleading graphs)
DataViz: Data Visualization with Plotly for JavaScript
(React basics; software installations; formatting data for visualization; creating bar chart and bubble chart)
(React basics; software installations; formatting data for visualization; creating bar chart and bubble chart)
DataViz: Data Visualization with Plotly for Python
(visualizing population change with bar chart; visualizing GDP and life expectancy with scatter plot;
customizing interactive bubble charts; creating facet plots; adding animation to facet plots;
representing geographic data as animated maps; line plots vs area plots)
(visualizing population change with bar chart; visualizing GDP and life expectancy with scatter plot;
customizing interactive bubble charts; creating facet plots; adding animation to facet plots;
representing geographic data as animated maps; line plots vs area plots)
DataViz: Data Dashboarding with Plotly for Python
(Streamlit introduction; installation; Plotly figures deciphered; building Streamlit app)
(Streamlit introduction; installation; Plotly figures deciphered; building Streamlit app)
Exploring University Vaccination Expectations: Text Mining and Qualitative Approaches Back to Back
(software access and installation; introduction to quantitative and qualitative text analysis;
project design; data collection; data preparation; exploratory data analysis;
grounded theory approach; mixed method analysis; limitations of the study; discussions)
(software access and installation; introduction to quantitative and qualitative text analysis;
project design; data collection; data preparation; exploratory data analysis;
grounded theory approach; mixed method analysis; limitations of the study; discussions)
Spring 2021
Python: Introduction to Python
(what is Python; Python environments; variables; operators; statements; comments)
(what is Python; Python environments; variables; operators; statements; comments)
Python: Introduction to Data Structures
(Python collections: lists, tuples, sets, dictionaries; iterations and loops; functions)
(Python collections: lists, tuples, sets, dictionaries; iterations and loops; functions)
Python: Introduction to Data Analysis with Python
(I/O; data manipulation with NumPy and Pandas; exploratory data analysis; basic data visualization)
(I/O; data manipulation with NumPy and Pandas; exploratory data analysis; basic data visualization)
R: Introduction to R
(RStudio interface; getting help; bolts and nuts of using R; data structures and subsetting; reading files of various formats; working with data frames)
(RStudio interface; getting help; bolts and nuts of using R; data structures and subsetting; reading files of various formats; working with data frames)
Winter 2021
R: Connecting SQLite Database and Web Application for Business Intelligence with Shiny
(building a one-stop-shop for business intelligence;
connecting to database and making database queries in R;
making database queries work in Shiny;
creating and deploying Shiny applications to solve business problems)
(building a one-stop-shop for business intelligence;
connecting to database and making database queries in R;
making database queries work in Shiny;
creating and deploying Shiny applications to solve business problems)
Fall 2020
R: Introduction to R
(RStudio interface; getting help; bolts and nuts of using R; data structures and subsetting; reading files of various formats; working with data frames; visualization essentials; bascis of statistical analysis)
(RStudio interface; getting help; bolts and nuts of using R; data structures and subsetting; reading files of various formats; working with data frames; visualization essentials; bascis of statistical analysis)
R: Using RSQLite and Shiny App for Business Intelligence
(building a one-stop-shop for business intelligence;
connecting to database and making database queries in R;
making database queries work in Shiny;
creating and deploying Shiny applications to solve business problems)
(building a one-stop-shop for business intelligence;
connecting to database and making database queries in R;
making database queries work in Shiny;
creating and deploying Shiny applications to solve business problems)
Fall 2019
R: Intro to R
(R environment; RStudio interface; getting help; reading files of various formats; working with data frames; subsetting; data types; best practices of working with data)
(R environment; RStudio interface; getting help; reading files of various formats; working with data frames; subsetting; data types; best practices of working with data)
R: Using R Markdown to Do Homework and More
(formatting basics; including code; mathematical notations; creating PDF and html files)
(formatting basics; including code; mathematical notations; creating PDF and html files)
R: Text Mining with R
(collecting data via Twitter API; text mining workflow; mining Twitter & Ted Talks data)
(collecting data via Twitter API; text mining workflow; mining Twitter & Ted Talks data)
Data Visualization Toolkits
(forms and trends of data visualization; tools of the trade)
(forms and trends of data visualization; tools of the trade)
Stata: Intro to Stata
(Stata interface; reading files of various formats; getting help; best practices of working with data; exploring data; variable transformation; missing values)
(Stata interface; reading files of various formats; getting help; best practices of working with data; exploring data; variable transformation; missing values)
Tableau: Getting Started
(Tableau interface; workflow; joins; charts; tables; maps)
(Tableau interface; workflow; joins; charts; tables; maps)
Spring 2019
R: Getting Started
(R environment; RStudio interface; workspace; key constructs; getting help; reading files; simple statistics)
(R environment; RStudio interface; workspace; key constructs; getting help; reading files; simple statistics)
R: Basic Operations
(data structures; subsetting; mathematical operations)
(data structures; subsetting; mathematical operations)
Stata: Getting Started
(Stata interface; reading files; getting help; syntax; good practices of working with data)
(Stata interface; reading files; getting help; syntax; good practices of working with data)
Fall 2018
R: Getting Started
(R environment; core constructs; objects; subsetting; basic operations)
(R environment; core constructs; objects; subsetting; basic operations)
Stata: Getting Started
(Stata interface; reading files; getting help; syntax; good practices of working with data)
(Stata interface; reading files; getting help; syntax; good practices of working with data)
Data Visualization Toolkits
(forms and trends of data visualization; chart types; tools of the trade)
(forms and trends of data visualization; chart types; tools of the trade)
R Shiny App I: Visualization with R
(reshaping data structures for visualization; building plots with ggplot2)
(reshaping data structures for visualization; building plots with ggplot2)
R Shiny App II: Web Interfacing Visualization
(building the Shiny App)
(building the Shiny App)
Spring 2018
R: The Environment and Core Concepts
(R environment; core constructs; objects)
(R environment; core constructs; objects)
R: Basic Operations and Functions
(subsetting; basic operations; functions)
(subsetting; basic operations; functions)
Stata: Getting Started
(Stata interface; reading files; getting help; syntax; good practices of working with data)
(Stata interface; reading files; getting help; syntax; good practices of working with data)
Stata: Using Data
(describing your data; creating and transforming variables; handling missing values; subsetting data)
(describing your data; creating and transforming variables; handling missing values; subsetting data)
Data Visualization Toolkits
(forms and trends of data visualization; chart types; tools of the trade)
(forms and trends of data visualization; chart types; tools of the trade)
Tableau: Getting Started
(Tableau interface and workflow; charts, tables and maps)
(Tableau interface and workflow; charts, tables and maps)
Tableau: Getting More Creative
(creating customized maps; filters; creating help button)
(creating customized maps; filters; creating help button)
Fall 2017
R: The Environment and Core Concepts
(R environment; core constructs; objects)
(R environment; core constructs; objects)
R: First Steps to Data Manipulation
(subsetting; basic operations; functions)
(subsetting; basic operations; functions)
Stata: The Preliminaries
(Stata interface; do-file, log file; reading files; getting help; syntax; missing values; subsamples)
(Stata interface; do-file, log file; reading files; getting help; syntax; missing values; subsamples)
Stata: Essentials in Data Checking
(describing your data; graphics in exploratory analysis; variable transformtion; saving results to Excel and Word)
(describing your data; graphics in exploratory analysis; variable transformtion; saving results to Excel and Word)
Stata: Automating Your Work
(saved results; macros; loops)
(saved results; macros; loops)
Tableau: Creating Interactive Graphics
(Tableau interface and workflow; creating charts, tables and maps; filters)
(Tableau interface and workflow; creating charts, tables and maps; filters)
Spring 2017
Stata: Getting Started
(Stata interface; do-file, log file; reading files; describing data; syntax; workflow; reproducibility)
(Stata interface; do-file, log file; reading files; describing data; syntax; workflow; reproducibility)
Stata: Preparing Data for Analysis
(data cleaning commands; string functions; dates and times; subscripting)
(data cleaning commands; string functions; dates and times; subscripting)
Stata: Commands and Graphics in Exploratory Analysis
(graphics; stored results; factor variables; publishable tables)
(graphics; stored results; factor variables; publishable tables)