Sessions are from 10am-12pm and 1pm-3pm in Bobst LL1, Room 41 in the Elmer Holmes Bobst Library.
Finding and organizing project files, with emphasis on specifics of Mac and Windows systems
Key shortcuts and skills around command-line tools on Mac and Windows systems–automate the boring stuff
File naming, bulk renaming, and description
Setting up personal storage environment and cloud-storage clients, establishing backup systems
Installation of open-source software and understanding software dependencies
Understanding of how data files (text, tabular) store (encode) characters and sustainable ways to keep files usable over many years
Introduction to common formats for storing and collecting data (tabular, JSON, SQL)
Cleaning and transforming untidy data
Special case studies of spatial data files, image files, time-based media files–how they work and why they are more complex
Identify the ethical issues and data privacy risks associated with generative AI use
Think critically about generative AI use to support research and analyze the quality and accuracy of AI outputs
Explore NYU supported generative AI offerings
Evaluate Generative AI Tools for Academic Research
Sessions are from 10am-12pm and 1pm-3pm in Bobst LL1, Room 41 at the Elmer Holmes Bobst Library.
Interface of R and RStudio
Data types and indexing (assignments, objects, vectors, matrices, data frames, lists)
R built-in functions and syntax
Working directory
Packages
Importing data from CSV, Excel, .txt, SPSS, Stata files etc...
Exporting data as CSV
Preparing data for analysis (renaming variables, variable types, missing values)
Computing, transforming and recoding variables
Subset datasets by row and by columns
Descriptive statistics, frequency tables, cross tabulation tables
Graphics (boxplot, histogram, scatterplot, partitioning window)
Basic analyses (t-test, correlation, ANOVA, regression, chi-square)
Extracting, generating, and modifying strings
Extracting dates from strings and producing dates in a desired format
Subsetting data sets
Merging two data sets
Converting panel data from long to wide format and vice versa
Producing for loops and using R’s apply functions
Writing your own functions
Conditional statements
Basic tidyverse functions
Sessions are from 10am-12pm and 1pm-3pm in Bobst LL1, Room 41 at the Elmer Holmes Bobst Library.
Understanding of basic principles of data visualization
Exploring data and types of data
Base plot function
Graphs for quantitative data (boxplots, scatterplots, bar graphs, histograms etc...)
Changing graph elements: titles, point size, colors
Extensive use of ggplot2
Here is a sampling of projects NYU graduate students have worked on using the types of skills covered in this track:
Analysis of a publicly available data set: Do Black & LatinX Students in NYC Have Equal Access to Computer Science Instruction?
Data Set Creation and Text Analysis: Uncovering the Mui Tsai Experience
Data Set Creation and Statistical Analysis: Testing the effects of Facebook usage in an ethnically polarized setting (data set is here)