Statistical Inference via Data Science: A ModernDive into R and the Tidyverse
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Updated
Sep 11, 2024 - HTML
Statistical Inference via Data Science: A ModernDive into R and the Tidyverse
Main repository for R programming courses @ University of Cincinnati, courses and tutorials that focus on data wrangling, exploration, visualization, and analysis with R.
Teaching material for Summer Institute in Statistics for Big Data Module 1.
Kai Sheng Teh - Udacity Data Analyst Nanodegree
Project work for the Udacity Data Analyst Nanodegree
Data Analysis with R (Remote Course)
Introduction to large scale computing and data wrangling with hands-on tutorials
Workshop: Using R/tidyverse to analyze & visualize gapminder/processed transcriptomics data!
This is a completely open-source repo of interview questions and answers for people preparing for such interviews. This is maintained by you and you can send the questions that you faced during interviews.
Udacity Nanodegree - Data Analyst - Wrangling, Exploring, Analyzing, and Visualizing Data
Python for Business
Udacity Data Analyst Nanodegree - Project IV
🔫 The data comes from the FBI's National Instant Criminal Background Check System. The NICS is used by to determine whether a prospective buyer is eligible to buy firearms or explosives. 🔫
Teaching notes from courses on digital information literacy, data management, and data wrangling.
Revistas de Economia com a nova classificação Qualis 2019: lista extraída a partir daquele PDF de quatrocentas páginas!
Wrangling and analysis of Tweets from WeRateDogs (@dogrates) with Python in Jupyter Notebook. Project focuses on gathering, assessing and cleaning data. Various methods, including Python's Requests and Tweepy packages for performing a GET Request and querying Twitter API, were used to collect Tweets and relevant data available online.
Analysis of datasets from "Gapminder World" to gain insights into countries' environment and economy from their consumption of energy. The analysis covers steps ranging from gathering and wrangling data to EDA and drawing conclusions.
OCS (BP): Examine global patterns of obesity across rural and urban regions
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