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/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.
dlab-berkeley/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.
dlab-berkeley/Bash-Git
D-Lab's 3 hour introduction to basic Bash commands and using version control with Git and Github.
dlab-berkeley/R-Deep-Learning
Workshop (6 hours): Deep learning in R using Keras. Building & training deep nets, image classification, transfer learning, text analysis, visualization
dlab-berkeley/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.
dlab-berkeley/R-Geospatial-Fundamentals-Legacy
This is the repository for D-Lab's Geospatial Fundamentals in R with sf workshop.
dlab-berkeley/python-berkeley
python resources of berkeley curated at a place
dlab-berkeley/R-Machine-Learning-Legacy
D-Lab's 6 hour introduction to machine learning in R. Learn the fundamentals of machine learning, regression, and classification, using tidymodels in R.
dlab-berkeley/R-Data-Wrangling-Legacy
D-Lab's 6 hour introduction to data wrangling with R. Learn how to manipulate dataframes using the tidyverse in R.
dlab-berkeley/R-Data-Visualization-Legacy
D-Lab's 3 hour introduction to data visualization with R. Learn how to create histograms, bar plots, box plots, scatter plots, compound figures, and more using ggplot2 and cowplot.
dlab-berkeley/Python-Text-Analysis-Legacy-2023
D-Lab's 12 hour introduction to text analysis with Python. Learn how to perform bag-of-words, sentiment analysis, topic modeling, word embeddings, and more, using scikit-learn, NLTK, Gensim, and spaCy in Python.
dlab-berkeley/Qualtrics-Fundamentals
D-Lab's 3 hour introduction to Qualtrics Fundamentals. Learn how to design and manage your own surveys in Qualtrics.
dlab-berkeley/Geospatial-Fundamentals-in-QGIS
dlab-berkeley/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.
dlab-berkeley/Python-Web-Scraping
D-Lab's 2 hour introduction to web scraping in Python. Learn how to scrape HTML/CSS data from websites using Requests and Beautiful Soup.
dlab-berkeley/DIGHUM101-2023
Practicing the Digital Humanities, UC Berkeley Summer Session 2023
dlab-berkeley/Python-Intermediate-Legacy
D-Lab's 3-part, 6 hour workshop diving deeper into Python. Learn how to create functions, use if-statements and for-loops, and work with Pandas, using Python and Jupyter.
dlab-berkeley/R-Data-Visualization
D-Lab's 2-hour introduction to data visualization with R. Learn how to create histograms, bar charts, box plots, scatter plots, and more using ggplot2.
dlab-berkeley/Survey-Fundamentals
dlab-berkeley/IRB-Fundamentals
D-Lab's 3 hour introduction to the fundamentals of navigating Institutional Review Boards (IRB).
dlab-berkeley/R-Data-Wrangling
D-Lab's 4 hour two-part workshop on data wrangling in R using tidyverse.
dlab-berkeley/DH-Text-Analysis
D-Lab's introduction to text analysis for Digital Humanities.
dlab-berkeley/DIGHUM101-2024
Python Programming for Digital Humanities, UC Berkeley Summer 2024, taught by Prashant Sharma
dlab-berkeley/Git-Playground
This repository is for D-Lab workshops that require practicing with Git.
dlab-berkeley/HAAS-Python-Workshop
dlab-berkeley/FSRDC-Fundamentals
dlab-berkeley/Natural-Language-Processing-Part-Two-DS-Discovery
dlab-berkeley/Practical-Programming-Working-Group
dlab-berkeley/prompt-engineering
D-Lab's 1-hour introduction to prompt engineering with ChatGPT. Learn what prompt engineering is, best practices for prompting, and techniques to resolve errors.
dlab-berkeley/Python-Machine-Learning-DS-Discovery
D-Lab's 6 hour introduction to machine learning in Python, tailored for DS Discovery Fellows. Learn how to perform classification, regression, clustering, and do model selection using scikit-learn in Python.