Data Matters: Spring Ahead | Virtual | March 13 - 16, 2023
Data Matters™ is a week-long series of one and two-day courses aimed at students and professionals in business, research, and government. The short course series is sponsored by the Odum Institute for Research in Social Science at UNC-Chapel Hill, the National Consortium for Data Science, and RENCI. Our first-ever springtime series, Data Matters: Spring Ahead, will feature a selection of our most popular two-day courses. Learn more on their website.
Among the classes available are:
- Introduction to Effective Information Visualization, Eric Monson. Visualization is a powerful way to reveal patterns in data, attract attention, and get your message across to an audience quickly and clearly. However, there are many steps in that journey from information to influence, and many questions – what visualization tools to use, how to get data into the right format, and which choices to make when putting it all together to tell your story? This course will quickly walk participants through a wide variety of data and chart types to help even beginners feel comfortable embarking on a new visualization project.
- Visualization for Data Science in R, Angela Zoss. Data science skills are increasingly important for research and industry projects. With complex data science projects, however, come complex needs for understanding and communicating analysis processes and results. Ultimately, an analyst’s data science toolbox is incomplete without visualization skills. Incorporating effective visualizations directly into the analysis tool you are using can facilitate quick data exploration, streamline your research process, and improve the reproducibility of your research. This course is designed for two audiences: experienced visualization designers looking to apply open data science techniques to their work, and data science professionals who have limited experience with visualization.
- Basics of R for Data Science and Statistics, Justin Post. This course introduces participants to discrete choice models, econometric models of how people choose between discrete outcomes, such as mode of travel to work or type of treatment for pain. The course will cover the subset of discrete choice models known as random utility models. These models are often used in disciplines such as economics, transportation, and public health. No prior knowledge is expected, and the course will cover logistic regression, multinomial logistic regression, and nested logistic regression. Hands-on exercises will be conducted in R.
- Introduction to Python, Laura Tateosian. Python is a consistently top ranking programming language. Python syntax is easy to learn and the language is well-suited for rapid data exploration, as well as larger data science projects. This course will help you add basic Python skills to your data science tool belt, so that you can then go on to explore some of the vast number of libraries written in Python. Learning Python is important for any aspiring data scientist. This course is designed for students with some prior exposure to computer programming, but no Python experience. Participants will be introduced to core Python elements for working with data.
The deadline for registration is March 8 for Monday/Tuesday courses and March 9 for Wednesday/Thursday courses.