explanation
Statistics with R Specialization This is a statistical science course using the R programming language. This language is especially familiar for performing statistical calculations and scientific data used during training through various modeling and statistical calculations with the help of this language. In this tutorial, you will learn how to analyze and visualize data in R so that you can extract reproducible analytical reports from your data. It will also help you conceptually understand the integrated nature of statistical inference and enable you to implement frequent inference and Bayesian inference.
Other topics in this tutorial include examining and critiquing statistical claims, evaluating decisions based on data, and using R packages to manipulate and explain data. We also model effective events to understand them. This tutorial also covers how to use definitions and simple sentences instead of advanced statistics terms. To help with the process, we can set out the knowledge, skills and tools required for the project, the data analysis and expertise that should be acquired by experts in the field of statistical data analysis, from exploratory analysis to inference and modeling.
What do you learn?
Bayesian Statistics
Linear Regression, Bayesian Linear and Regression Analysis
statistical inference
Program in R and work with RStudio
Heuristic data analysis
Statistical assumption testing
Select model
and…
Statistical specification via R specialization
Publisher: Coursera
Instructors: Mine Cetinkaya-Rundel, David Banks, Colin Rundel, Merlise A Clyde
Language:English
Education level: beginner
Quantity: 5 periods
Course Duration: The suggested duration is 3 hours per week, approximately 7 months.
procedure
- Introduction to probability and data using R
- inferential statistics
- Linear Regression and Modeling
- Bayesian Statistics
- Statistics using R Capstone
precondition
- No basic math or programming experience required. A genuine interest in data analysis is a plus!
- You will need R and RStudio. Both are free and publicly available. Installing this software requires administrator access to your computer.
- It is assumed that later courses in the specialization will use the same knowledge and skills acquired in previous courses (e.g., knowledge of frequentist statistics and R if you decide to take Bayesian Statistics course 4 without taking the previous three courses). is identical to what is taught in the first three courses.)
image
sample video
installation manual
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Subtitles: English
Quality: 720p
download link
Password file: free download software
file size
2.68GB