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Data Science Bootcamp January 17-19

Discover software and techniques to support your data, including machine learning, APIs, and web scraping.  

  • Image of using data in the mind

Will you be performing research or coursework involving data science?

Get a head start before the semester by learning intermediate/advanced data science skills at the CCSS Data Science Bootcamp! Over the course of 3 days(January 17-19), the bootcamp will go over Machine learning and how to implement in your research. We will also cover extracting online data through the use of API's and web scraping techniques. 

Schedule and Logistics

In-Person Location: Mann Library B30B

Registration is mandatory to attend. Completing registration will send you the in-person and virtual details along with events placed on your outlook calendar. 

Register 

Schedule

1/17-18 | Machine Learning Series

Tues. Jan. 17

  • 10:00-11:00 am | Machine Learning in the Social Sciences
  • 11:15-12:45 pm | Exploratory Data Analysis for Machine Learning
  • 12:45-1:30 pm | Lunch
  • 1:30-3:00 pm | Supervised Learning using Python

Wed. Jan. 18

  • 10:00-11:00 am | Supervised Learning using Python
  • 11:15-12:45 pm | Natural Language
  • Processing(NLP) in the Social Sciences

1/19 | Web Scraping/API Series

Thurs. Jan. 19

  • 10:00-11:00 am | Web Scraping and APIs for extracting Social Science data through the web
  • 11:15-12:45 pm | Web Scraping Techniques with Python
  • 12:45-1:30 pm | Lunch
  • 1:30-3:00 pm | API Techniques using Python

 

Series Descriptions

Click on the series below to expand the descriptions. 

  • Machine Learning Series

    Machine Learning Series

    This series introduces basic concepts of machine learning (ML) and shows examples of how ML can be applied within social science research. This series offers an overview of supervised learning, unsupervised learning, and natural language processing methods through hands-on workshops in Python. It is best suited for social scientists with working Python proficiency and quantitative research experience. 

    Courses:

    • Machine Learning in the Social Sciences: Learn the fundamentals regarding data and machine learning models and how ML is currently being used in social science research.  
    • Exploratory Data Analysis for Machine Learning: Gain hands-on experience with data exploration and processing techniques consistently used within ML projects. 
    • Supervised Learning using Python: Walk through building a supervised classifier model from data preparation, model selection, to generating predictions. 
    • Unsupervised Learning using Python: Explore and represent emergent patterns within data by developing your own unsupervised ML models.
    • Natural Language Processing in the Social Sciences: Dive into the intersection of ML and text data through constructing both supervised and unsupervised NLP models.

  • Web Scraping/API Series

    Web Scraping/API Series

    This series describes how you can use web scraping techniques and API's to extract online data within social science research. This series offers an overview along with hands on examples on how to implement these techniques. It is best suited for social scientists with working Python proficiency and quantitative research experience. 

    Courses:

    • Web Scraping and APIs for extracting Social Science data: Learn the purpose of web scraping for collecting Social Science Data
    • Web Scraping with Python: Gain hands-on experience with extracting data from the web using Python
    • APIs with Python: Gain hands-on experience with extracting data from online providers through the use of APIs in Python

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