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# R Essentials Primer for Data Science & Analytics

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R Essentials Primer for Data Science is a quick start style, light hands-on course. R is a functional programming environment employed by many data analysts and data scientists, easily accessible to non-programmers and naturally extending a skill set that is common to data analysts and data scientists.

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Last Updated

### March 2, 2021

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### ashar hafeez

Certification

This course is approximately 40% hands-on lab to 60% lecture ratio, combining engaging lecture, demos, group activities and discussions with light machine-based practical programming labs and exercises.  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:

Â· Moving from Excel to R

Â· R Basics

Â· Vectors

Â· Reading and Writing Files

Â· Dates

Â· Multiple Dimensions

Â· Overview of R in Data Science

## Course Curriculum

• Common problems with Excel 00:00:00
• The R Environment 00:00:00
• Hello, R 00:00:00
• CRAN 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 Iterating 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
• Powerful Data through Visualization: Communicating the Message 00:00:00
• R in Spark 00:00:00
• Demo(s) 00:00:00

## Course Reviews

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