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Workshops & Training

Through CCSS discover valuable software, data, and computing tools for working with data! 

  • Image of 291 seminar room in Clark Hall with white chairs facing presenter podium

Fall 2023 Workshops

Workshops are open to the Cornell community!

Advanced Coding

CCSS offers workshops on advanced coding techniques like Machine Learning, Web Scraping, and Geospatial Analysis. 

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    Additional Details:

    This series implements web scraping techniques using python to extract online data within social science research. We start with the python library BeautifulSoup best used for learning the basics of extracting data from online sources. BeautifulSoup provides simple methods for navigating, searching and extracting what you need. 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

    • Understanding HTML website structure(Tabs, Attributes). Locating what you need to scrape.
    • Python basics for scraping data you want from static(non-changing) webpages. 
    • Cleaning up scraped data into organized readable format for further analysis. 

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    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. 

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    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:

    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)

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    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

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    Additional Details:

    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)

  • Dates Offered:

    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)

  • Dates Offered:

    Additional Details:

    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)

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    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)

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    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)

  • Dates Offered:

    Additional Details:

    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)

Replication & Data

CCSS is all about reproducibility. Making sure that your results are clean and accurate prior to submitting for publication. 

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    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)

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    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

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    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)

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    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

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    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

Qualitative Analysis

CCSS has qualitative experts on staff. Join these workshops to learn how to implement Qualitative analysis software applications in your processes. 

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    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. 

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    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

Programming Practice

Programming Practice workshops are in person only. (291 Clark Hall) 

Practice makes perfect. Join hands-on workshops where you will work through Data Science problem sets to advance your programming skills. These workshops are designed for researchers who have introductory knowledge and would like to further hone their coding skills.

These trainings are geared towards operations often used throughout the data analysis process. Topics that will be covered include but are not limited to uploading data, cleaning and manipulation, analysis, and interpreting output.  

Join our interactive hands on practice workshops to improve your R, Python, Stata and web scraping skills.

  • Dates Offered:

    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:

    Pre-Requisites:

  • Dates Offered:

    Instructor: Jonathan Chang

    Requirements:

    Pre-Requisites:

  • Dates Offered:

    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:

  • Dates Offered:

    Instructor: Kanika Khanna

    Requirements:

    Pre-Requisites:

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What do We Offer?

Workshops

CCSS Workshops are given by our staff consultants, Senior Data Science Fellows, and Data Science Fellows. Learn more about our instructors here


Training for Classes and Project Teams  Request Training

 

Training for Classes

We provide class training tailored to your group’s specific needs on topics related to:

  • Data processing and management
  • Use of qualitative software packages such as Atlas.ti, MaxQDA and NVivo
  • Use of statistical software packages such as SAS, SPSS, STATA, and R
  • Use of CCSS research and computing servers

 Training for Project Teams

We provide just-in-time training for project teams in:

  • Qualitative and quantitative research data management and processing
  • Targeted training for a particular phase/time in the project team's research process where extra help is needed
  • We'd love to hear your ideas, suggestions, or questions!

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