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1

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.
Jupyter Notebook
218
star
2

Machine-Learning-in-R

Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles
CSS
187
star
3

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.
Jupyter Notebook
168
star
4

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.
R
139
star
5

Bash-Git

D-Lab's 3 hour introduction to basic Bash commands and using version control with Git and Github.
131
star
6

R-Deep-Learning

Workshop (6 hours): Deep learning in R using Keras. Building & training deep nets, image classification, transfer learning, text analysis, visualization
R
120
star
7

git-fundamentals

A starting point for discovering the wonderful world of Git, GitHub, and Git Annex (Assistant)
Shell
74
star
8

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.
Stata
72
star
9

python-for-everything

Materials for teaching the Python for Everything workshop at UC Berkeley's D-lab
Jupyter Notebook
69
star
10

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.
Jupyter Notebook
66
star
11

MachineLearningWG

D-Lab's Machine Learning Working Group at UC Berkeley, with supervised & unsupervised learning tutorials in R and Python
HTML
65
star
12

Python-Geospatial-Fundamentals-Legacy

D-Lab's 6 hour introduction to working with geospatial data in Python. Learn how to import, visualize, and analyze geospatial data using GeoPandas in Python.
Jupyter Notebook
57
star
13

Python-Data-Visualization-Legacy

D-Lab's 3 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.
Jupyter Notebook
56
star
14

R-Geospatial-Fundamentals-Legacy

This is the repository for D-Lab's Geospatial Fundamentals in R with sf workshop.
Jupyter Notebook
53
star
15

Python-Data-Wrangling-Legacy

D-Lab's 3 hour introduction to data wrangling in Python. Learn how to import and manipulate dataframes using pandas in Python.
Jupyter Notebook
51
star
16

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.
R
47
star
17

Unsupervised-Learning-in-R

Workshop (6 hours): Clustering (Hdbscan, LCA, Hopach), dimension reduction (UMAP, GLRM), and anomaly detection (isolation forests).
R
47
star
18

python-berkeley

python resources of berkeley curated at a place
Jupyter Notebook
44
star
19

Python-Text-Analysis-Fundamentals

D-Lab's 9 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.
Jupyter Notebook
38
star
20

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.
R
37
star
21

python-data-from-web

API and web scraping workshops
Jupyter Notebook
35
star
22

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.
28
star
23

R-Functional-Programming

The joy and power of functional programming in R
27
star
24

python-text-analysis-legacy

Text Analysis Workshops for UC Berkeley's D-Lab
Jupyter Notebook
26
star
25

programming-fundamentals

Introduction to Programming for UC Berkeley's D-Lab
Python
23
star
26

ANN-Fundamentals

Jupyter Notebook
23
star
27

DIGHUM101-2020

Jupyter Notebook
20
star
28

Python-Text-Analysis

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.
Jupyter Notebook
20
star
29

sql-for-r-users

SQL for R Users, Workshop
HTML
19
star
30

Python-Deep-Learning-Legacy

D-Lab's 6 hour introduction to deep learning in Python. Learn how to create and train neural networks using Tensorflow and Keras.
Jupyter Notebook
17
star
31

awesome-dlab

😎 Awesome lists about all kinds of topics and tools interesting to D-Labbers
17
star
32

advanced-data-wrangling-in-R-legacy

Advanced-data-wrangling-in-R, Workshop
HTML
15
star
33

R-Census-Data-Legacy

Workshop on fetching and mapping census data with tidycensus
HTML
14
star
34

Geospatial-Fundamentals-in-QGIS

11
star
35

regular-expressions-in-python

Jupyter Notebook
10
star
36

Qualtrics-Fundamentals

D-Lab's 3 hour introduction to Qualtrics Fundamentals. Learn how to design and manage your own surveys in Qualtrics.
10
star
37

Data-Science-Social-Justice-2022

Materials for D-Lab / UC Berkeley Graduate Division's Data Science + 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.
Jupyter Notebook
10
star
38

Geocoding-in-R

HTML
9
star
39

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.
Jupyter Notebook
9
star
40

efficient-reproducible-project-management-in-R

Efficient and Reproducible Project Management in R
HTML
9
star
41

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.
9
star
42

fairML

Bias and Fairness in ML workshop
Jupyter Notebook
8
star
43

Python-Web-Scraping-Legacy

D-Lab's 3 hour introduction to web scraping in Python. Learn how to use APIs and scrape data from websites using the New York Times API and BeautifulSoup in Python.
Jupyter Notebook
7
star
44

regex-intro

Shell
6
star
45

Geospatial-Fundamentals-in-R-sp

HTML
6
star
46

Leaflet-Maps-in-R

A 3-hour intensive workshop to introduce the R Leaflet package
HTML
6
star
47

javascript-viz

A D-Lab intro to JavaScript visualization using the IPython notebook.
HTML
6
star
48

DIGHUM101-2023

Practicing the Digital Humanities, UC Berkeley Summer Session 2023
Jupyter Notebook
6
star
49

LaTeX-Fundamentals

TeX
6
star
50

DIGHUM101-2021

Jupyter Notebook
5
star
51

cloud-computing-working-group

5
star
52

data-security-fundamentals

Data Security Fundamentals
HTML
5
star
53

Python-Fundamentals

D-Lab's 3-part, 6 hour introduction to Python. Learn how to create variables, distinguish data types, use methods, and work with Pandas, using Python and Jupyter.
Jupyter Notebook
4
star
54

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.
Jupyter Notebook
3
star
55

quick-consulting-examples

Collection of quick pandas, python, and other coding examples based on real consulting requests.
Jupyter Notebook
3
star
56

dlab-berkeley.github.io

Tech overview site showcasing D-Lab's online offerings
CSS
3
star
57

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.
Jupyter Notebook
3
star
58

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.
Jupyter Notebook
3
star
59

DIGHUM101-2022

Practicing the Digital Humanities, UC Berkeley Summer Session 2022
Jupyter Notebook
3
star
60

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.
Jupyter Notebook
2
star
61

Basics-of-Excel

2
star
62

intro-maxqda

2
star
63

Python-Intermediate

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.
Jupyter Notebook
2
star
64

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.
R
2
star
65

IRB-Fundamentals

D-Lab's 3 hour introduction to the fundamentals of navigating Institutional Review Boards (IRB).
2
star
66

RStudio-Project-Management

Resources to help you start managing data science projects.
HTML
1
star
67

git-for-project-management

Using Git and GitHub for Project Management
1
star
68

R-package-development

R package development workshop
HTML
1
star
69

Git-Playground

This repository is for D-Lab workshops that require practicing with Git.
1
star
70

sas-intro

Introduction to SAS
TeX
1
star
71

R-Push-Ins

D-Lab's 4.5 hour "push-in" introduction to R, providing a brief survey of foundational R concepts and operations.
R
1
star
72

DEVP229-Spring2021

HTML
1
star
73

MAXQDA-Fundamentals

D-Lab's 2 hour introduction to MAXQDA. Learn how to conduct qualitative data analysis using MAXQDA.
1
star
74

sas-analysis

Data Analysis with SAS
SAS
1
star
75

R-Research-Design

1
star
76

ArcGIS-Online-Fundamentals

1
star
77

dlab-methods

CSS
1
star
78

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
HTML
1
star
79

Python-Data-Visualization-Pilot

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.
Jupyter Notebook
1
star
80

HAAS-Python-Workshop

Jupyter Notebook
1
star