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Introduction to R Programming for Data Science & Analytics

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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.

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Unlimited Duration

Last Updated

March 2, 2021

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Posted by

ashar hafeez

Certification

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

    • Common challenges with Excel / SAS 00:00:00
    • The R Environment 00:00:00
    • Hello, R 00:00:00
    • 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
    • Vector Properties 00:00:00
    • Creating, Combining, and Iteratorating 00:00:00
    • Passing and Returning Vectors in Functions 00:00:00
    • Logical Vectors 00:00:00
    • Text Manipulation 00:00:00
    • Factors 00:00:00
    • Working with Dates 00:00:00
    • Date Formats and formatting 00:00:00
    • Time Manipulation and Operations 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
    • AI Grouping Theory 00:00:00
    • K-means 00:00:00
    • Linear Regression 00:00:00
    • Logistic Regression 00:00:00
    • Elastic Net 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
    • Powerful Data through Visualization: Communicating the Message 00:00:00
    • Techniques in Data Visualization 00:00:00
    • Data Visualization Tools 00:00:00
    • Examples 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
    • Overview of Hadoop 00:00:00
    • Overview of Distributed Databases 00:00:00
    • Overview of Pig 00:00:00
    • Overview of Mahout 00:00:00
    • Exploiting Hadoop clusters with R 00:00:00
    • Hadoop, Mahout, and R 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
    • Best practices for working with AWS (completely outside of R and Python) 00:00:00

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