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@dlab-berkeley

D-Lab

The social science data lab at UC Berkeley

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  1. Python-Fundamentals Python-Fundamentals Public

    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 J…

    Jupyter Notebook 46 24

  2. R-Fundamentals R-Fundamentals Public

    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 RS…

    R 38 11

  3. Bash-Git Bash-Git Public

    D-Lab's 3 hour introduction to basic Bash commands and using version control with Git and Github.

    133 85

  4. Qualtrics-Fundamentals-Legacy Qualtrics-Fundamentals-Legacy Public

    D-Lab's 3 hour introduction to Qualtrics Fundamentals. Learn how to design and manage your own surveys in Qualtrics.

    14 5

  5. Stata-Fundamentals Stata-Fundamentals Public

    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.

    Stata 90 43

  6. Excel-Fundamentals Excel-Fundamentals Public

    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 cr…

    14 8

Repositories

Showing 10 of 123 repositories
  • Demystifying_AI Public
    dlab-berkeley/Demystifying_AI’s past year of commit activity
    0 0 0 0 Updated Apr 4, 2025
  • Python-SQL-Fundamentals Public

    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/Python-SQL-Fundamentals’s past year of commit activity
    Jupyter Notebook 2 0 0 0 Updated Apr 3, 2025
  • Computational-Social-Science-Training-Program Public

    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/Computational-Social-Science-Training-Program’s past year of commit activity
    Jupyter Notebook 233 99 3 0 Updated Apr 3, 2025
  • R-SQL-Fundamentals Public

    D-Lab's 2-hour introduction to usingSQL in R. Learn how to query and retrieve data from relational databases in R.

    dlab-berkeley/R-SQL-Fundamentals’s past year of commit activity
    0 0 0 0 Updated Mar 31, 2025
  • Qualtrics-Fundamentals Public

    D-Lab's two-part introduction to Qualtrics. Learn how to design and manage your own surveys in Qualtrics.

    dlab-berkeley/Qualtrics-Fundamentals’s past year of commit activity
    0 0 0 0 Updated Mar 25, 2025
  • LLMs-Exploratory-Research Public

    D-Lab's 2-hour introduction to using LLMs for exploratory research.

    dlab-berkeley/LLMs-Exploratory-Research’s past year of commit activity
    6 1 0 0 Updated Mar 20, 2025
  • GitHub-Fundamentals Public

    D-Lab's 2 hour introduction to using the version control software Git with GitHub Desktop, a GUI Git client. Learn how to use version control, create repositories, branching, merging, and using GitHub Desktop to collaborate on software in your research workflows.

    dlab-berkeley/GitHub-Fundamentals’s past year of commit activity
    2 0 0 0 Updated Mar 11, 2025
  • R-Census-Data Public
    dlab-berkeley/R-Census-Data’s past year of commit activity
    Jupyter Notebook 4 0 0 0 Updated Mar 5, 2025
  • Python-Deep-Learning Public

    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/Python-Deep-Learning’s past year of commit activity
    Jupyter Notebook 2 1 0 0 Updated Mar 3, 2025
  • Python-Machine-Learning Public

    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-Machine-Learning’s past year of commit activity
    Jupyter Notebook 82 69 25 4 Updated Mar 1, 2025
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