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Introduction to R for Data Scientists (TTCR01-DS3)

Essential R for Scientific Computing | Basics, Vectors, Dates, Using MadLib, Data Visualization & More

GK# 8450

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Course Details

Course Overview

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R is a functional programming environment for business analysts and data scientists. It's a language that many non-programmers can easily work with, naturally extending a skill set that is common to high-end Excel users. It's the perfect tool for when the analyst has a statistical, numerical, or probabilities-based problem based on real data and they've pushed Excel past its limits.

Geared for data scientists or engineers with potentially lighttechnical background or experience, R for Data Scientists is a hands-on R course that explores common scenarios that are encountered in analysis, and presents practical solutions to those challenges. Throughout the course, special attention is paid to data science theory including AI grouping theory. A discussion of using R with AI libraries like Madlib is also included. Students who want additional topics and extended hands-on exposure might consider the 5-day extended version of this course, Mastering R for Data Scientists.

Schedule

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Outline

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Virtual Classroom Live Outline

1. From Excel to R

  • Common Problems with Excel
  • The R Environment
  • Hello, R

2. R Basics

  • Simple Math with R
  • Working with Vectors
  • Functions
  • Comments and Code Structure
  • Using Packages

3. Vectors

  • Vector Properties
  • Creating, Combining, and Iterating
  • Passing and Returning Vectors in Functions
  • Logical Vectors

4. Reading and Writing

  • Text Manipulation
  • Factors

5. Dates

  • Working with Dates
  • Date Formats and Formatting
  • Time Manipulation and Operations

6. Multiple Dimensions

  • Adding a Second Dimension
  • Indices and Named Rows and Columns in a Matrix
  • Matrix Calculation
  • n-Dimensional Arrays
  • Data Frames
  • Lists

7. R in Data Science

  • AI Grouping Theory
  • K-means
  • Linear Regression
  • Logistic Regression
  • Elastic Net

8. R with MadLib

  • Importing and Exporting Static Data (CSV, Excel)
  • Using Libraries with CRAN
  • K-means with Madlib
  • Regression with Madlib
  • Other Libraries

9. Data Visualization

  • Powerful Data through Visualization: Communicating the Message
  • Techniques in Data Visualization
  • Data Visualization Tools
  • Examples

Labs

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Virtual Classroom Live Labs

This workshop is about 40% hands-on lab and 60% lecture.

Course Delivery

This course is available in the following formats:

Virtual Classroom Live

Experience expert-led online training from the convenience of your home, office or anywhere with an internet connection.

Duration: 3 day

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