General Introduction into the R Ecosystem :
- > Downloading and installing R
- >History of R, R packages, CRAN
- >R community, R-bloggers
- >Stack Overflow, Coursera, DataCamp
- >R User Groups & meetups
Demonstration of a Data Analysis Project in R :
- > Brief Overview on R Coding Tools RStudio
- >git, Github
R Syntax Basics :
- >Constants, operators, functions, variables
- >Random numbers
- > Vectors and vector indexing
- >Simple descriptive stats
- > Loops
- >Conditional expressions
The Power of R :
- >Applying PCA on an image for outlier-detection
- >Visualizing MDS on a distance matrix
A Systematic Introduction into Data Types :
- >Levels of measurement (nominal, ordinal, interval, ratio scale)
- > Vector types
- >data. frame objects, rows and columns, indexing
- > Characteristics of tidy data
Basic Data Transformations Create new variables in a data. frame :
- > Filter rows and columns
- > Merging datasets
- > Introduction to data. table for More Complex Data Transformations
Filtering and ordering data:
- >Summaries and aggregates
- >New variables
- >Relational data
- >Joins on Keys
- >Introduction into fuzzy joins
- >Transforming wide and long tables
EDA - First Steps with Data Visualization:
- >Why not Use Pie Charts
- > Plots outside of Excel: dot chart and violin plot examples
- > The Grammar of Graphics in R with ggplot2
- > Using labels for variable names
Introduction to Non-tabular Data Types:
- > Time-series
- > Spatial data
- > Network data
- >Big Data Problems: What is Big Data
- > 4V: volume, variety, velocity, veracity
- >Data Transformations: Converting Numeric Variables into Factors
- >Date Operations
- > String Parsing
- > Geo coding
- > Dirty Data Problems: missing values
- > data imputation
- > duplicates
- >4 forms of data dates
- > outliers
- > spelling
Executive Program in R Programming
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