R Programming for Data Scientist

Course Description:

This course serves as an introduction to the interdisciplinary and emerging field of data science. The course introduces the programming skill that essential to big data analytic using R language. Students will learn to combine tools and techniques from statistics, computer science, data visualization and machine learning to solve data analytic problems. The course covers many essential knowledge regarding important aspects of R language ranging from basic syntaxes, data handling/acquisition, vital tools and packages to more advanced topic such as machine learning techniques.

Duration : 3 Days (9:00 - 16:30)

Fee : 9,900 Baht; Early Bird 9,500 Baht (with your own notebook)

( Fee includes Course Material + Coffee Break and Lunch )

Instructor:  Mr. Dendej Sawarnkatat   (See Profile >> Here)

○ELearning Association of Thailand (ELAT) official committee / Instructor

○Software Park (Thailand) Instructor

○ACIS Training Center Instructor / Specialist

Training Schedule:

●     20 - 22 January 2016

• 1 - 3 June 2016

• 2 - 4  November 2016

Training Venue: The Connecion (Near MRT Ladprao Exit 4)>> See Map

Who Should Attend:

●      Scientist and Researchers

●      Data Scientists and Data Engineers

●      Business Intelligence Analysts

●      Chief Information Officers, eBusiness Marketing Managers

Prerequisite:

●      Basic computing Skill with adequate mathematical knowledges

Benefits:

●      Understanding the programming concept of one of the most popular languages among data science community.

●      Gaining hand-on experience with simple yet powerful methods and tools that vital to any big data analytics.

Course Outline:

Day 1:

●      Introduction to R Programming Language

○        Basic syntaxes

○        Variables and Operators

○        Expressions

○        Data Types

○        Functions

○        Control Flows Statements

 Day 2

●      Data Manipulation

○        Data source types (online / offline)

■    Data Files (XML, JSON, Excel, Plain Text, etc.)

■    Databases

■    Internet

○        Simple Manipulation Tools

●      Data Visualization

○        Essential plotting & graphing Libraries

○        Chart Types (Box, Bar, Histogram, Pie, etc.)

 Day 3

●      Introduction to Machine Learning with R

○        Cluster Analysis

○        Classification Analysis

• Regression Analysis

Online Registration >>HERE