ratings
Introduction to R Programming for Data Science & Analytics is a comprehensive hands-on course that presents common scenarios encountered in analysis and present practical solutions.
Unlimited Duration
March 2, 2021
This course provides indoctrination in the practical use of the umbrella of technologies that are on the leading edge of data science development focused on R and related tools. In this course you will learn about:
· R Language and Mathematics
· How to work with R Vectors
· How to read and write data from files, and how to categorize data in factors
· How to work with Dates and perform Date math
· How to work with multiple dimensions and DataFrame essentials
· Essential Data Science and how to use R with it
· Visualization in R
· How R can be used in Spark (Optional / Overview)
Course Curriculum
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- Common challenges with Excel / SAS 00:00:00
- The R Environment 00:00:00
- Hello, R 00:00:00
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- Rshiny 00:00:00
- Rpresentations 00:00:00
- Rmarkdown 00:00:00
- Simple Math with R 00:00:00
- Working with Vectors 00:00:00
- Functions 00:00:00
- Comments and Code Structure 00:00:00
- Using Packages 00:00:00
- Text Manipulation 00:00:00
- Factors 00:00:00
- Adding a second dimension 00:00:00
- Indices and named rows and columns in a Matrix 00:00:00
- Matrix calculation 00:00:00
- n-Dimensional Arrays 00:00:00
- Data Frames 00:00:00
- Lists 00:00:00
- Importing and Exporting static Data (CSV, Excel) 00:00:00
- Using Libraries with CRAN 00:00:00
- K-means with Madlib 00:00:00
- Regression with Madlib 00:00:00
- Other libraries 00:00:00
- Building connections to Databases and Data lakes, for both Python and R (using Hive server) 00:00:00
- Methods to “query” data from database and data lakes, for both Python and R 00:00:00
- Creating and passing macro variables. Specifically, R sprint, paste, paste0, and paste3 (not sure of the equivalent in Python). 00:00:00
- Rule Systems in the Enterprise 00:00:00
- Enterprise Service Busses 00:00:00
- Drools & Using R with Drools 00:00:00
Course Reviews
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