This hands-on workshop is designed for truly beginners of R. It will be offered twice in the Spring semester, so please pick the date which works better for you. Instead of using RStudio, we will introduce Anaconda and Jupyter notebook which we will use in this workshop. Starting from variable assignments and basic data types in R, the workshop will cover vectors, matrices, data frames, factors, and lists in R. It will fit you well if you would like to take your very first steps with R. With the knowledge gained in the 90 minutes, you will master the basics of this widely used open-source programming language and be ready to undertake your first very own data analysis.
This intermediate R workshop will teach you classic machine learning theory and methods to perform classification on multivariate datasets. Classification is useful for developing clusters of similar records to guide further exploratory data analysis, for smart sampling, or for labeling.
Python is a general-purpose programming language that is becoming ever more popular for data science. This workshop is designed for beginners who had zero experience with Python, as it will offer you an introduction to the basic concepts of Python. Anaconda and Jupyter notebook will be introduced. Basic data types in Python, Python lists, functions, and packages will be covered. You will learn a fundamental Python package, NumPy, and powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses. The workshop will be offered twice in the Spring semester, so please pick the date which works better for you.
This hands-on workshop is designed for truly beginners of R. It will be offered twice in the Spring semester, so please pick the date which works better for you. Instead of using RStudio, we will introduce Anaconda and Jupyter notebook which we will use in this workshop. Starting from variable assignments and basic data types in R, the workshop will cover vectors, matrices, data frames, factors, and lists in R. It will fit you well if you would like to take your very first steps with R. With the knowledge gained in the 90 minutes, you will master the basics of this widely used open-source programming language and be ready to undertake your first very own data analysis.
For social science research, gathering data from more than a few people on their perceptions poses significant logistical challenges. How then can we scale up our research to collect survey data from more than ten thousand people? What are some basic steps? What technical skills are necessary? What kinds of questions does that kind of size enable us to address? In this Technical Research Discussion, we will discuss the planning and implementation of a large Amazon Mechanical Turk survey to uncover perspectives on street images as part of a larger study on urban environmental health. Several survey design issues are discussed. The role of code to automate the survey process is covered, with some detailed examples using python, javascript, and R. We'll also cover what we'd do differently, based on our experience to date. You will finish this workshop with greater context and specific information about conducting research using Amazon Mechanical Turk, the role of automation in the process, and the technical requirements for doing so.