Workshops & Training

Dates Offered:

• September 8, 2:00-3:30 pm.  Register Here

Additional Details:

This series implements web scraping techniques using python to extract online data within social science research. We continue from the previous workshop by using the python library Selenium, a more powerful web scraping tool. Traditional scraping tools struggle to collect data from websites that rely on JavaScript. Selenium enables that along with extra functionality to interact with a page like a human user would by submitting mouse clicks, scrolling and filling out forms. This demonstration will provide Python code, familiarity using Python is encouraged. Feel free to bring your laptop with Anaconda installed so you can follow along. 

Instructor: Jacob Grippin

Pre-requisites: Introductory Python knowledge(Watch CCSS Python recording here)

Learning Objectives:

• Python basics for scraping data you want from dynamic(changing) webpages.
• Python basics for submitting mouse clicks, scrolling, searches, etc.
• Automate longer web scraping projects efficiently. 

Dates Offered:

• September 15, 1:00-2:30 pm.  Register Here

Additional Details:

Geospatial analytics gathers, manipulates and displays geographic information system (GIS) data. Geospatial data analytics rely on geographic coordinates and specific identifiers such as street address and zip code. They are used to create geographic models and data visualizations for more accurate modeling and predictions of trends. Geospatial data analytics lets the eye recognize patterns like distance, proximity, contiguity and affiliation that are hidden in massive datasets. The visualization of spatial data also makes it easier to see how things are changing over time and where the change is most pronounced. Join this workshop series to discover how to implement geospatial analysis into your research through the R programming language. This workshop covers introductory aspects of geospatial analysis through R. Familiarity in R is required. Feel free to bring your laptop with R installed so you can follow along.

Instructor: Kanika Khanna

Pre-requisites: Introductory R knowledge(Watch CCSS R recording here)

Dates Offered:

• September 18, 1:00-2:30 pm.  Register Here

Additional Details:

Geospatial analytics gathers, manipulates and displays geographic information system (GIS) data. Geospatial data analytics rely on geographic coordinates and specific identifiers such as street address and zip code. They are used to create geographic models and data visualizations for more accurate modeling and predictions of trends. Geospatial data analytics lets the eye recognize patterns like distance, proximity, contiguity and affiliation that are hidden in massive datasets. The visualization of spatial data also makes it easier to see how things are changing over time and where the change is most pronounced. Join this workshop series to discover how to implement geospatial analysis into your research through the R programming language. This workshop expands on the previous one by incorporating more advanced features for geospatial analysis in R. Familiarity in R is required. Feel free to bring your laptop with R installed so you can follow along.

Instructor: Kanika Khanna

Pre-requisites: Introductory R knowledge(Watch CCSS R recording here)

This workshop series instructs users on how Machine Learning models can be applied within social science research. Best suited for social scientists with working Python proficiency and quantitative research experience. This workshop provides an overview on machine learning models and how it is currently being used in social science research. 

Instructor: Jonathan Chang

Pre-Requisites: None

This workshop series instructs users on how Machine Learning models can be applied within social science research. Best suited for social scientists with working Python proficiency and quantitative research experience. This workshop explores and represents emergent patterns within data by developing unsupervised Machine Learning models.

Instructor: Jonathan Chang

Pre-requisites: Introductory Python knowledge(Watch CCSS Python recording here)

Geospatial analytics gathers, manipulates and displays geographic information system (GIS) data. Geospatial data analytics rely on geographic coordinates and specific identifiers such as street address and zip code. They are used to create geographic models and data visualizations for more accurate modeling and predictions of trends. Geospatial data analytics lets the eye recognize patterns like distance, proximity, contiguity and affiliation that are hidden in massive datasets. The visualization of spatial data also makes it easier to see how things are changing over time and where the change is most pronounced. Join this workshop series to discover how to implement geospatial analysis into your research through the R programming language. This workshop expands on the previous one by incorporating more advanced features for geospatial analysis in R. Familiarity in R is required. Feel free to bring your laptop with R installed so you can follow along.

Instructor: Kanika Khanna

Pre-requisites: Introductory R knowledge(Watch CCSS R recording here)

This workshop series instructs users on how Machine Learning models can be applied within social science research. Best suited for social scientists with working Python proficiency and quantitative research experience. This workshop explores and represents emergent patterns within data by developing supervised Machine Learning models.

Instructor: Jonathan Chang

Pre-requisites: Introductory Python knowledge(Watch CCSS Python recording here)

Factor analysis is a statistical method used to search for some unobserved variables called factors from observed variables called factors. Factor analysis is used in many areas of statistical analysis such as marketing, social sciences, psychology, and so on. Join to learn how to implement factor analysis in R.

Instructor: Aishat Sadiq

Pre-requisites: Introductory R knowledge(Watch CCSS R recording here)

This workshop series instructs users on how Machine Learning models can be applied within social science research. Best suited for social scientists with working Python proficiency and quantitative research experience. This workshop dives into the intersection of ML and text data through constructing both supervised and unsupervised NLP models.

Instructor: Jonathan Chang

Pre-requisites: Introductory Python knowledge(Watch CCSS Python recording here)

This workshop series instructs users on how Machine Learning models can be applied within social science research. Best suited for social scientists with working R proficiency and quantitative research experience. This workshop explores and represents emergent patterns within data by developing unsupervised Machine Learning models.

Instructor: Aishat Sadiq

Pre-requisites: Introductory R knowledge(Watch CCSS R recording here)

Dates Offered:

• September 28, 1:30-2:30 pm.  Register Here

Additional Details:

Replication of results is a core requirement of the scientific method. This workshop will demonstrate generating tables through R code that are publication ready. This is the best way to report results as the output is completely reproducible. This workshop goes over the code used in R for creating tables that can be inserted directly into your publications. 

Instructor: Aishat Sadiq

Pre-requisites: Introductory R knowledge(Watch CCSS R recording here)

Dates Offered:

• October 11, 3:30-4:45 pm.  Register Here

Additional Details:

The Cornell Federal Statistical Research Data Center (FSRDC) provides access to confidential federal data from several agencies, including the U.S. Census Bureau. The Cornell FSRDC administrator, Nichole Szembrot, will give an overview of the available data and proposal process. This workshop is recommended for faculty and Ph.D. students.

Instructor: Nichole Szembrot

Pre-Requisites: None

Dates Offered:

• October 16, 3:00-4:00 pm.  Register Here

Additional Details:

Replication of results is a core requirement of the scientific method. This workshop will demonstrate generating tables through Stata code that are publication ready. This is the best way to report results as the output is completely reproducible. This workshop goes over the code used in Stata for creating tables that can be inserted directly into your publications. 

Instructor: Jacob Grippin

Pre-requisites: Introductory Stata knowledge(Watch CCSS Stata recording here)

Dates Offered:

• October 19, 1:30-2:30 pm.  Register Here

Additional Details:

The American Community Survey (ACS) is the premier source for detailed population and housing data for the United States. ACS releases new data every year that you can access with different data tools. Attend this workshop for a lesson on how to pull the ACS data you want through the interactive online tools of IPums and the census website.

Instructor: Aishat Sadiq

Pre-Requisites: None

Dates Offered:

• November 10, 1:30-4:00 pm.  Register Here

Discussion topics:

• Process of reviewing the manuscript, data, code, output, and other documentation
• Preparing a Readme file
• Preparing a Data Availability Statement
• Preparing the replication package (consisting of data, code, and other documentation) to make it portable, independently understandable, easily reusable, and ready for publication, archiving, and sharing
• Discuss common mistakes in manuscripts and codes so you can avoid them
• Present CCSS services to assist your research, including Data Archiving and Replication Service

Instructor: Florio Arguillas

Pre-Requisites: None

Dates Offered:

• September 22, 1:30-4:00 pm.  Register Here

Additional Details:

Atlas.ti is a powerful workbench for qualitative data analysis. No matter your field, Atlas.ti will meet your qualitative analysis needs. Sophisticated tools help you to arrange, reassemble, and manage your material in creative ways.

Instructor: Jacob Grippin

Pre-Requisites: None

Learning Objectives:

• Uploading files(transcripts, surveys, recordings, images, etc.) and creating Atlas.ti projects.
• Coding, identifying themes.
• Analyzing results. Creating organized reports.
• Saving your work. Collaborating as a team. 

Dates Offered:

• October 20, 1:30-4:00pm.  Register Here

Additional Details:

MaxQDA is a qualitative and mixed-method analysis software package that has increasingly become popular here at Cornell. Unlike Atlas.ti and NVivo, its Mac and Windows versions are identical allowing for seamless cross-platform integration. This workshop will cover understanding the MaxQDA environment, creating a project, adding and working with documents, coding and organizing the code system, memos, lexical search and autocoding, MaxDictio, retrieving coded segments, and reporting of results.

Workshop Topics:

• Overview of the MaxQDA environment
• Creating, saving, and exporting Projects
• Coding and identifying themes
• Creating reports

Instructor: Florio Arguillas

Pre-Requisites: None

Dates Offered:

• September 8, 4:00-5:00pm.  Register Here

Additional Details:

This series implements web scraping techniques using python to extract online data within social science research. We continue by working on sample web scraping problems using BeautifulSoup or Selenium to hone our python scraping skills for later use. Please bring your laptop with Anaconda installed so you can participate in solving the sample problems.

Instructor: Jacob Grippin

Requirements:

• Laptop with Anaconda installed (download Anaconda here) or 

• Account on CCSS Cloud Computing Solutions. Apply here. 

Pre-Requisites:

• Introductory Web Scraping Knowledge(watch CCSS recording here)

• Introductory Python knowledge(Watch CCSS Python recording here)

Dates Offered:

• Thursday September 21, 2:30-5:00pm. Register Here

Instructor: Jonathan Chang

Requirements:

• Laptop with Anaconda installed (download Anaconda here) or

• Account on CCSS Cloud Computing Solutions. Apply here. 

Pre-Requisites:

• Introductory Python knowledge(watch CCSS recording here)

Dates Offered:

• Friday September 29, 2:30-5:00pm.  Register Here

Instructor: Jacob Grippin

Requirements:

• Laptop (Register for the workshop to be given free access to Stata for use during the workshop) or

• Account on CCSS Cloud Computing Solutions. Apply here. 

Pre-Requisites:

• Introductory Stata knowledge(watch CCSS recording here)

Dates Offered:

• Friday October 13, 2:30-5:00pm. Register Here

Instructor: Kanika Khanna

Requirements:

• Laptop with R installed (install: R Windows, R Mac, RStudio) or 

• Account on CCSS Cloud Computing Solutions. Apply here. 

Pre-Requisites:

• Introductory R knowledge(watch CCSS recording here)