This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets. No theory instruction will be provided. Prerequisites: D-Lab’s Python Fundamentals introductory series or equivalent knowledge. Workshop materials: https://github.com/dlab-berkeley/Python-Machine-Learning-Fundamentals Software Requirements:Installation Instructions for Python Anaconda Is Python Not working on your laptop? Attend the workshop anyway, we can provide you with a cloud-based solution until you figure out the problems with your local installation.

Geospatial data are an important component of data visualization and analysis in the social sciences, humanities, and elsewhere. The R programming language is a great platform for exploring these data and integrating them into your research. This workshop focuses on fundamental operations for reading, writing, manipulating and mapping vector data, which encodes location as points, lines and polygons. Part I: Core concepts, vector data, and plotting Basic geospatial concepts Basic vector data Geospatial data structures (the sf package) Basic plotting (base::plot and the ggplot3 package) Managing coordinate reference systems (CRS) Advanced plotting (the tmap package) Map overlays Part II: Spatial analysis Spatial measurement queries Spatial relationship queries Buffer analysis Spatial and non-spatial joins Aggregation Continued mapping practice Prerequisites: D-Lab’s R Fundamentals or equivalent knowledge; previous experience with base R is assumed and basic familiarity with the tidyverse. Workshop Materials: https://github.com/dlab-berkeley/R-Geospatial-Fundamentals Software Requirements: Requirements for R and RStudio

It is said that 80% of data analysis is spent on the process of cleaning and preparing the data for exploration, visualization, and analysis. This R workshop will introduce the dplyr and tidyr packages to make data wrangling and manipulation easier. Participants will learn how to use these packages to subset and reshape data sets, do calculations across groups of data, clean data, and other useful tasks. Prerequisites: D-Lab’s R Fundamentals or equivalent knowledge; previous experience with base R is assumed. Workshop Materials: https://github.com/dlab-berkeley/R-Data-Wrangling Software Requirements:Installation Instructions for R and RStudio

Not sure where your professional future lies or how to approach the process in an organized and strategic manner. This presentation provides a framework and resources (including MyIDP, Versatile PhD, LinkedIn and more) for moving forward with confidence towards the next step in your professional future.