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Data Science Workshops

The Research Commons' Data Science Workshop Series offers an array of skill-sharing topics, featuring data visualization and management, as well as software environments like R and Python.

Data Science Workshops

Statistical Consultation

If your statistical problems cannot be fully resolved by these workshops, the UMD Libraries also offer one-on-one statistical consultations that are free to all UMD affiliates so feel free to learn more about statistical consultation service and availability.
 

Registration is required to attend our workshops. While workshops are open to everyone, priority registration is given to UMD affiliates.

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Workshops

Data Visualization Tools logo

Data Visualization Tools

This workshop introduces the basics of data visualization using several packages, including Tableau, Gephi, Plot.ly, RAW, Carto, and Piktochart.

 


Introduction to Python logo

Introduction to Python

This workshop introduces the basics of Python, including Jupyter Notebooks, syntax, data types, operators, looping, functions, data import, and data visualization. 


R Workshop

Introduction to R language programming

R is an extremely powerful, versatile and best of all FREE statistical package that runs on Windows, Macintosh and Unix platform. Join this workshop and gain a hands-on experience on learning R! It is okay if you do not have programming experience.

This workshop will cover:

  1. An overall introduction to R
  2. Basic mathematical functions
  3. Summary statistics and summary report
  4. How to create graph in R
  5. Regression analysis in R

R Workshop

Intermediate R Workshop: Linear Models

Linear model is one of the most basic and commonly-used statistical model. This workshop will cover how to use R to fit and analyze linear models. It will cover the following topics:

  1. Simple linear regression
  2. Multiple linear regression
  3. ANOVA testing
  4. Checking assumption violations

Prerequisites:

  1. Basic skills in R. Completion of “Introduction to R language programming” workshop is recommended but not required.  
  2. Completion of a 400 or 600 level statistic course or equivalent. Prior knowledge and study in linear regression and ANOVA is assumed.

Data Carpentry

Year of Data Science: Software & Data Carpentry

The University of Maryland Libraries and the National Socio-Environmental Synthesis Center (SESNYC) have teamed up to build a cohort of certified Software and Data Carpentry instructors at the University of Maryland, College Park. Sponsored by the UMD Year of Data Science initiative, The Libraries and SESYNC are recruiting up to 15 faculty and staff members of the UMD campus community to take part in a one-time training and certification process. The newly established cohort of Carpentries instructors will then coordinate a workshop series to teach data science skills across campus as part of the Year of Data Science during the 2019 spring semester. Please direct all inquiries to carpentries@umd.edu.