- Probability And Statistics
- Correlation And Dependence
- Data Analysis
- Linear Regression
- Statistical Inference
- Statistical Hypothesis Testing
- Data Visualization
- R Programming
- Rstudio
- Regression Analysis
- Exploratory Data Analysis
- General Statistics
Data Analysis with R
Completed by José Santiago Calderón
March 19, 2017
Approximately 4 months at 10 hours a week to completeJosé Santiago Calderón's account is verified. Coursera certifies their successful completion of Duke University Data Analysis with R Specialization.
Course Certificates Completed
Inferential Statistics
Introduction to Probability and Data with R
Statistics with R Capstone
Linear Regression and Modeling
Bayesian Statistics
What you will learn
Analyze and visualize data
Perform hypothesis tests, interpret statistical results (e.g., p-values), and report the results of your analysis to clients
Fit, examine, and utilize regression models to examine relationships between multiple variables
Install and use R and RStudio
Skills you will gain
Course Certificates
Earned after completing each course in the Specialization
Inferential Statistics
Duke University
Taught by: Mine Çetinkaya-Rundel
Completed by: José Santiago Calderón by August 22, 2016
5 weeks of study, 5-7 hours/week
Introduction to Probability and Data with R
Duke University
Taught by: Mine Çetinkaya-Rundel
Completed by: José Santiago Calderón by August 9, 2016
5 weeks of study, 5-7 hours/week
Statistics with R Capstone
Duke University
Taught by: Merlise A Clyde, Colin Rundel , David Banks & Mine Çetinkaya-Rundel
Completed by: José Santiago Calderón by March 19, 2017
5-10 hours/week
Linear Regression and Modeling
Duke University
Taught by: Mine Çetinkaya-Rundel
Completed by: José Santiago Calderón by August 8, 2016
4 weeks of study, 5-7 hours/week
Bayesian Statistics
Duke University
Taught by: Mine Çetinkaya-Rundel, David Banks, Colin Rundel & Merlise A Clyde
Completed by: José Santiago Calderón by August 29, 2016
5 weeks of study, 5-7 hours/week