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/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.
dlab-berkeley/Python-Machine-Learning
D-Lab's 6 hour introduction to machine learning in Python. Learn how to perform classification, regression, clustering, and do model selection using scikit-learn in Python.
dlab-berkeley/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.
dlab-berkeley/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.
dlab-berkeley/awesome-dlab
😎 Awesome lists about all kinds of topics and tools interesting to D-Labbers
dlab-berkeley/Python-Deep-Learning
D-Lab's 2-hour introduction to deep learning in Python. Learn how to create and train neural networks using Tensorflow and Keras.
dlab-berkeley/Data-Science-Social-Justice
Materials for D-Lab / UC Berkeley Graduate Division's Data Science for Social Justice summer workshop. These materials provide an introduction to Python, natural language processing, text analysis, word embeddings, and network analysis. They also include discussions on critical approaches to data science to promote social justice.
dlab-berkeley/Python-Data-Wrangling
D-Lab's 3-hour workshop diving deep into Pandas. Learn how to manipulate, index, merge, group, and plot data frames using Pandas functions.
dlab-berkeley/R-Copilot-Assisted-Coding-Workshop
D-Lab's 2-hour workshop on AI-assisted coding in Visual Studio Code using GitHub Copilot
dlab-berkeley/LLMs-Exploratory-Research
D-Lab's 1-hour introduction to using LLMs for exploratory research.
dlab-berkeley/Python-Data-Visualization
D-Lab's 4-hour introduction to data visualization with Python. Learn how to create histograms, bar plots, box plots, scatter plots, compound figures, and more, using matplotlib and seaborn.
dlab-berkeley/Python-Web-APIs
D-Lab's 2 hour introduction to using web APIs in Python. Learn how to obtain data from web platforms using the New York Times API as a case study.
dlab-berkeley/R-Geospatial-Fundamentals
D-Lab's 4-hour introduction to working with geospatial data in R. Learn how to import, visualize, and analyze geospatial data in R.
dlab-berkeley/D-Lab-Workshop-Template
This repository serves as a template for all D-Lab workshops stored on GitHub. Use this template if you're creating a new D-Lab workshop.
dlab-berkeley/MAXQDA-Fundamentals
D-Lab's 2 hour introduction to MAXQDA. Learn how to conduct qualitative data analysis using MAXQDA.
dlab-berkeley/R-Census-Data
Workshop on fetching and mapping census data with tidycensus
dlab-berkeley/Python-Geospatial-Fundamentals
About D-Lab's 4-hour introduction to working with geospatial data in Python. Learn how to import, visualize, and analyze geospatial data in Python.
dlab-berkeley/Python-Text-Analysis
D-Lab's introduction to text analysis with Python. Learn how to preprocess text data, apply bag-of-words methods, engage with word embeddings, and more, using Python.
dlab-berkeley/R-Machine-Learning
D-Lab's 4-hour introduction to machine learning in R. Learn the fundamentals of machine learning, regression, and classification, using tidymodels in R.
dlab-berkeley/SQL-Fundamentals
This workshop introduces the fundamentals of SQL, with a focus on using SQLite (the most ubiquitous database on the planet) for data science tasks.
dlab-berkeley/Cloud-SQL-Databases
This is a hands-on workshop on analyzing Social Media Data using Cloud Databases, specifically Google Cloud Platform's BigQuery
dlab-berkeley/R-Causal-Inference
D-Lab's 2-hour introduction to causal inference. Learn about core concepts in causal inference, including the potential outcomes framework and essential statistical techniques.
dlab-berkeley/Command-Line-Fundamentals
D-Lab's 1-hour introduction to the command line. This workshop provides a basic introduction to interacting with your computer via terminal. It focuses on Bash or Z, which are some of the most commonly used Unix/Linux shells.
dlab-berkeley/dlab-modules
Listing for D-Lab courses
dlab-berkeley/dlab-workshops
Overview of D-Lab workshops and the order in which to take them.
dlab-berkeley/DSSJ-2025
dlab-berkeley/Github-Fundamentals
dlab-berkeley/Instructor-Training
D-Lab's 2-part, 3.5 hour introduction to workshop instruction.
dlab-berkeley/Python-GPT-Fundamentals
D-Lab's 2-hour introduction to Generative Pretrained Transformers (GPT) for beginners. Learn about text encoding, word embeddings, and the transformer architecture upon which GPT is based. Create texts using a GPT model with the Transformers library in Python, and learn about hyperparameters such as temperature.
dlab-berkeley/R-Haas-Workshop
D-Lab's 1.5 hour foray into R basics for the Haas MBA 200 course.