• No products in the cart.

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.

PRIVATE
Course Access

Unlimited Duration

Last Updated

March 2, 2021

Students Enrolled

Total Reviews

Posted by
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

    Course Reviews

    Profile Photo
    ashar hafeez
    0
    61

    Students

    About Instructor

    Pak

    Course Events

    [wplms_eventon_events]

    More Courses by Insturctor

    © 2021 Ernesto.  All rights reserved.  
    X