Data Matters™ is a week-long series of one and two-day courses aimed at students and professionals in business, research, and government. Sponsored by the Odum Institute for Research in Social Science at UNC-Chapel Hill, the National Consortium for Data Science, and RENCI, the short-course series gives students the chance to learn about a wide range of topics in data science, analytics, visualization, curation, and more from expert instructors. Learn more on the website. All classes this year are virtual (via Zoom). Among the classes available are:
- Introduction to Programming in R, Jonathan Duggins. Statistical programming is an integral part of many data-intensive careers and data literacy, and programming skills have become a necessary component of employment in many industries. This course begins with necessary concepts for new programmers—both general and statistical—and explores some necessary programming topics for any job that utilizes data.
- Overview of AI and Deep Learning, Ashok Krishnamurthy. Many key advances in AI are due to advances in machine learning, especially deep learning. Natural language processing, computer vision, speech translation, biomedical imaging, and robotics are some of the areas that have benefited from deep learning methods. This course is designed to provide an overview of AI, and in particular, deep learning. Topics include the history of neural networks, how advances in data collection and computing have caused a revival in neural networks, different types of deep learning networks and their applications, and tools and software available to design and deploy deep networks.
- Introduction to Statistical Machine Learning in R, Yufeng Liu. Statistical machine learning and data mining is an interdisciplinary research area which is closely related to statistics, computer sciences, engineering, and bioinformatics. Many statistical machine learning and data mining techniques and algorithms are useful in various scientific areas. This two-day short course will provide an overview of statistical machine learning and data mining techniques with applications to the analysis of real data.
- Geospatial Analytics Using Python, Laura Tateosian. This course will focus on how to explore, analyze, and visualize geospatial data. Using Python and ArcGIS Pro, students will inspect and manipulate geospatial data, use powerful GIS tools to analyze spatial relationships, link tabular data with spatial data, and map data. In these activities, participants will use Python and the arcpy library to invoke key GIS tools for spatial analysis and mapping.
The deadline for registration is August 3 for Monday/Tuesday courses, August 4 for Wednesday courses, and August 7 for Thursday/Friday courses.
Special discounted rates for faculty, staff, and students. Learn more: https://datamatters.org/.
