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qss

Supplementary Materials for ``Quantitative Social Science: An Introduction''

quanteda

Text Analysis in R with Quanteda

quanteda-1

quanteda.extras

r-course-material

A collection of R tutorials

r-for-data-science-lunchbreak-lessons

Source files for the LinkedIn Learning Course

r-for-hr

This repository contains the files necessary to build my free ebook called "R for HR: An Introduction to Human Resource Analytics Using R".

r-tutorial-data-files

Contains data files that are referenced in R tutorials from the following YouTube playlist: https://www.youtube.com/playlist?list=PLKkRkURCtPjCJOZHskCoyJCPb8wMDs2CW

r_and_python

Demo of seamless coding in Rmd with both R and Python

rmd-tr

R Ladies Ankara, Eskişehir ve İstanbul için R Markdown sunumu

rtrainintroduction

Series of short introduction to R training courses, built for online delivery with RMarkdown and RStudio

scholarly

Retrieve author and publication information from Google Scholar in a friendly, Pythonic way

seaborn-data

Data repository for seaborn examples

sentiment-analysis-topic-modeling-for-hotel-reviews

Web Scraping, Sentiment Analysi, Latent Dirichlet Allocation (LDA) topic modelling

sicss-python-crash-course

The Python crash course of the Summer Institute in Computational Social Science 2022!

sifirdan-ileri-seviyeye-python-programlama

Udemy üzerindeki Python kurslarında kullanılan materyaller

soc360-css

SOC360 Computational Social Science

socialsciencedatatools

Tools for Social Science Data

spring2022

Repository for the Spring 2022 Computational Social Science Workshop

starbucks_people_analytics

Starbucks project shows how I utilized the ETL process using python pandas and matplotlib.

statistical-analysis

Repository focused on statistical analysis and exploration used on various data sets for academic, personal and professional projects.

statistics-for-data-science

Learning Statistics is one of the most Important step to get into the World of Data Science and Machine Learning. Statistics helps us to know data in a much better way and explains the behavior of the data based upon certain factors. It has many Elements which help us to understand the data better that includes Probability, Distributions, Descriptive Analysis, Inferential Analysis, Comparative Analysis, Chi-Square Test, T Test, Z test, AB Testing etc.

statistics-using-r

Implemented some statistical methods using R.

stgdpg7

streamlit-demo

streamlit-example

Example Streamlit app that you can fork to test out share.streamlit.io

streamlit-folium

Streamlit Component for rendering Folium maps

streamlit-hr-analytics

Create Web Apps Using Streamlit

streamlit_app_gallery

This is a multi-page streamlit app to showcase Sharone's streamlit app examples

summer-institute

Summer Institutes in Computational Social Science