Home About Courses Schedule Services Webinars Contact Search

Data Science with R

SEE SCHEDULE

Duration: 3 Days

Method: Instructor led, Hands-on workshops

Price: $1650.00

Course Code: DS1010


Audience

Developers who want to learn Data Science using R programming language.

Description

Data science is the study of the extraction of knowledge from data. It builds on techniques and theories from mathematics, probability models, machine learning, computer programming, statistics, data engineering, pattern recognition and learning, visualization, uncertainty modeling, data warehousing, and high performance computing with the goal of extracting meaning from data and creating data products. Data Science need not always used for big data applications, however, big data is an important aspect of data science today. Data scientists are able to work with these technical elements and utilize strong business acumen to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge. This course provides a foundation for applying these principles to challenges within their organization.

Objectives

Upon successful completion of this course, the student will be able to:

  • Explain what Data Science is and how it relates to Big Data
  • Describe R language and use it to create meaning from data
  • Use R to produce graphs and charts
  • Use R to perform linear and logical regression
  • Describe what Text Analytics is and how it to use it
  • Describe and implement collaborative filtering

Prerequisites

Solid foundation in computer science, applications, modeling, statistics, analytics and math.

Topics

  • I.Data Science Basics
  • II.R language basics : Scalars, Vectors, and Functions
  • III.Intermediate R: Matrices, Factors, and Data Frames
  • IV.Charting and Graphing in R
  • V.Statistical processing in R: Linear and Logistic Regression
  • VI.Introduction to Text Analytics and the 'tm' package
  • VII.Introduction to Collaborative Filtering
  • VIII.Implementing a recommendation engine using collaborative filtering