Discover dlab-berkeley/intro-maxqda Open Source project

This repository has been archived on 10/Nov/2022
Stars
2
Language
Created over 4 years ago
Updated almost 4 years ago

dlab-berkeley

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

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

Machine-Learning-in-R

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

CSS

187

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

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.

139

Bash-Git

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

131

R-Deep-Learning

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

120

git-fundamentals

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

Shell

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

python-for-everything

Materials for teaching the Python for Everything workshop at UC Berkeley's D-lab

Jupyter Notebook

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

MachineLearningWG

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

HTML

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

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

R-Geospatial-Fundamentals-Legacy

This is the repository for D-Lab's Geospatial Fundamentals in R with sf workshop.

Jupyter Notebook

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

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.

Unsupervised-Learning-in-R

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

python-berkeley

python resources of berkeley curated at a place

Jupyter Notebook

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

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.

python-data-from-web

API and web scraping workshops

Jupyter Notebook

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.

R-Functional-Programming

The joy and power of functional programming in R

python-text-analysis-legacy

Text Analysis Workshops for UC Berkeley's D-Lab

Jupyter Notebook

programming-fundamentals

Introduction to Programming for UC Berkeley's D-Lab

Python

ANN-Fundamentals

Jupyter Notebook

DIGHUM101-2020

Jupyter Notebook

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

sql-for-r-users

SQL for R Users, Workshop

HTML

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

awesome-dlab

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

advanced-data-wrangling-in-R-legacy

Advanced-data-wrangling-in-R, Workshop

HTML

R-Census-Data-Legacy

Workshop on fetching and mapping census data with tidycensus

HTML

Geospatial-Fundamentals-in-QGIS

regular-expressions-in-python

Jupyter Notebook

Qualtrics-Fundamentals

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

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

Geocoding-in-R

HTML

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

efficient-reproducible-project-management-in-R

Efficient and Reproducible Project Management in R

HTML

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.

fairML

Bias and Fairness in ML workshop

Jupyter Notebook

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

regex-intro

Shell

Geospatial-Fundamentals-in-R-sp

HTML

Leaflet-Maps-in-R

A 3-hour intensive workshop to introduce the R Leaflet package

HTML

javascript-viz

A D-Lab intro to JavaScript visualization using the IPython notebook.

HTML

DIGHUM101-2023

Practicing the Digital Humanities, UC Berkeley Summer Session 2023

Jupyter Notebook

LaTeX-Fundamentals

TeX

DIGHUM101-2021

Jupyter Notebook

cloud-computing-working-group

data-security-fundamentals

Data Security Fundamentals

HTML

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

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

quick-consulting-examples

Collection of quick pandas, python, and other coding examples based on real consulting requests.

Jupyter Notebook

dlab-berkeley.github.io

Tech overview site showcasing D-Lab's online offerings

CSS

visualization-in-Excel

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

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

DIGHUM101-2022

Practicing the Digital Humanities, UC Berkeley Summer Session 2022

Jupyter Notebook

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

Basics-of-Excel

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

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.

IRB-Fundamentals

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

RStudio-Project-Management

Resources to help you start managing data science projects.

HTML

git-for-project-management

Using Git and GitHub for Project Management

R-package-development

R package development workshop

HTML

Git-Playground

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

sas-intro

Introduction to SAS

TeX

R-Push-Ins

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

DEVP229-Spring2021

HTML

MAXQDA-Fundamentals

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

sas-analysis

Data Analysis with SAS

SAS

R-Research-Design

ArcGIS-Online-Fundamentals

dlab-methods

CSS

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

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

HAAS-Python-Workshop

Jupyter Notebook

dlab-berkeley/intro-maxqda

dlab-berkeley

Reviews

Repository Details

More Repositories