Pinned Repositories
Bash-Git
D-Lab's 3 hour introduction to basic Bash commands and using version control with Git and Github.
Computational-Social-Science-Training-Program
This course is a rigorous, year-long introduction to computational social science. We cover topics spanning reproducibility and collaboration, machine learning, natural language processing, and causal inference. This course has a strong applied focus with emphasis placed on doing computational social science.
Excel-Fundamentals
D-Lab's six-hour introduction to the basics of Microsoft Excel (with support materials for Google Sheets). Learn Excel functions for handling text, math, dates, logic, and calculations; learn to create charts and pivot tables.
Machine-Learning-in-R
Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles
Python-Fundamentals
D-Lab's 6-part, 12-hour introduction to Python. Learn how to create variables, use methods and functions, work with if-statements and for-loops, and do data analysis with Pandas, using Python and Jupyter.
Python-Fundamentals-Legacy
D-Lab's 12 hour introduction to Python. Learn how to create variables and functions, use control flow structures, use libraries, import data, and more, using Python and Jupyter Notebooks.
Qualtrics-Fundamentals
D-Lab's 3 hour introduction to Qualtrics Fundamentals. Learn how to design and manage your own surveys in Qualtrics.
R-Fundamentals
D-Lab's 4 part, 8 hour introduction to R Fundamentals. Learn how to create variables and functions, manipulate data frames, make visualizations, use control flow structures, and more, using R in RStudio.
R-Fundamentals-Legacy
D-Lab's 12 hour introduction to R Fundamentals. Learn how to create variables and functions, manipulate data frames, make visualizations, use control flow structures, and more, using R in RStudio.
Stata-Fundamentals
D-Lab's 9 hour introduction to performing data analysis with Stata. Learn how to program, conduct data analysis, create visualization, and conduct statistical analyses in Stata.
D-Lab's Repositories
dlab-berkeley/git-fundamentals
A starting point for discovering the wonderful world of Git, GitHub, and Git Annex (Assistant)
dlab-berkeley/python-for-everything
Materials for teaching the Python for Everything workshop at UC Berkeley's D-lab
dlab-berkeley/MachineLearningWG
D-Lab's Machine Learning Working Group at UC Berkeley, with supervised & unsupervised learning tutorials in R and Python
dlab-berkeley/python-text-analysis-legacy
Text Analysis Workshops for UC Berkeley's D-Lab
dlab-berkeley/programming-fundamentals
Introduction to Programming for UC Berkeley's D-Lab
dlab-berkeley/R-for-Data-Science
D-Lab R-intensive teaching materials
dlab-berkeley/regular-expressions-in-python
dlab-berkeley/Geospatial-Fundamentals-in-R-sp
dlab-berkeley/javascript-viz
A D-Lab intro to JavaScript visualization using the IPython notebook.
dlab-berkeley/LaTeX-Fundamentals
dlab-berkeley/Leaflet-Maps-in-R
A 3-hour intensive workshop to introduce the R Leaflet package
dlab-berkeley/regex-intro
dlab-berkeley/cloud-computing-working-group
dlab-berkeley/dlab-berkeley.github.io
Tech overview site showcasing D-Lab's online offerings
dlab-berkeley/ArcGIS-Online-Fundamentals
dlab-berkeley/BerkeleyX-support
Public repo to support BerkeleyX instructors and researchers
dlab-berkeley/Computational-Text-Analysis-2017
An introduction to Computational Text Analysis in four 2hr sessions designed to help beginners build intuition, and to interact with workflows for natural language processing, supervised, and unsupervised approaches. Created for CTAWG in 2017 by Ben Gebre-Medhin
dlab-berkeley/dlab-methods
dlab-berkeley/RStudio-Project-Management
Resources to help you start managing data science projects.
dlab-berkeley/sas-analysis
Data Analysis with SAS
dlab-berkeley/sas-intro
Introduction to SAS
dlab-berkeley/chunking_congress
Example for using Congressional data in a Computational Text Analysis workflow from CTAWG
dlab-berkeley/Computational-Text-Analysis-2015
3-day intensive created in 2015 for CTAWG by Nick Adams: https://github.com/dlabctawg/
dlab-berkeley/Computational-Text-Analysis-2016
Text Analysis Fundamentals: Methods and Approaches (In Three 2hr Session) for CTAWG in 2016 by Ben Gebre-Medhin
dlab-berkeley/google-cloud-speech-api
Example of using Google Cloud Speech API
dlab-berkeley/GWATC
Computational Text Analysis Working Group