Data Analysis Using 'R'
- Offered byNimble Tech
Data Analysis Using 'R' at Nimble Tech Overview
Data Analysis Using 'R'
at Nimble Tech
Gain a comprehensive overview of Data Analysis Using 'R'
Mode of learning | Online |
Official Website | Go to Website  |
Credential | Certificate |
Data Analysis Using 'R'
Table of content
- Overview
- Highlights
- Course Details
- Curriculum
Data Analysis Using 'R' at Nimble Tech Highlights
Data Analysis Using 'R'
at Nimble Tech
- Earn a certificate after completion of the course
Data Analysis Using 'R' at Nimble Tech Course details
Data Analysis Using 'R'
at Nimble Tech
Skills you will learn
Who should do this course?
- Professionals who analyze data to gain insights, make informed decisions, and solve problems in various industries such as finance, marketing, healthcare
What are the course deliverables?
- Gain proficiency in the fundamentals of R programming language, including data types, functions, and control structures
- Learn how to import, clean, and preprocess data using R packages such as dplyr and tidyr to prepare it for analysis
- Master techniques for exploring and summarizing data visually and numerically, including descriptive statistics, data visualization with ggplot2, and identifying patterns and trends
- Acquire skills in conducting various statistical analyses, including hypothesis testing, regression analysis, ANOVA, and non-parametric tests using R's built-in functions and packages like stats
More about this course
- R is a programming language for statistical computing and graphics. Its libraries include linear and non-linear modelling, classical statistical tests, time-series analysis, classification, clustering and others
- It can link and call C, C++ and fortran code for computationally intensive tasks if required
Data Analysis Using 'R' at Nimble Tech Curriculum
Data Analysis Using 'R'
at Nimble Tech
Introduction to Data Analysis Introduction to R programming Data Manipulation using R
Data import techinques using R Exploatory data analysis
Data visualisation using R
Data Mining
Linear and logistic regression Anova and predictive analysis
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