R Programming for Data Science (Instructor-led Online Course)

R Programming for Data Science (Instructor-led Online Course)

 

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About The Event

Introduction : This Course is first in the series of our Data Science courses. It's aimed at providing a strong foundation in R-programming for Data Science enthusiasts. R-Statistics is the most widely used tool in the area of Data Analysis (used extensively in both Industry and Research) and so, is the base for all of our courses. This course is meant for people planning to start a career in the Data Science field and also for those who know the basics, but lack practice and grip over the subject.

 

For Any queries reach out to info@godelresearch.in  / +91- 8123617991

 

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Company  : Godel Research Lab

 

Instructor : Raju Mishra (Linkedin)

 

 

Schedule : 27th & 28th Dec 2014 and 3rd Jan 2015

 

 

Mode of Delivery : WebEx Session (Instructor-led Online Course)

 

Detailed Course Content :

This 3-day course is divided into 2 sections -

        Section-A : Theory and Hands-on
        Section-B : Project on Real Data

 

 

Section-A (Day- 1&2) :

 

- Introduction to R
      [What is R.  Why R.  Scope and Applications. Drawbacks of using R]
- Getting Help
      [help(), mailing list, R webpage, ? & ?? Operators]
- Structure of Program in R
      [Using R console. Scripting in R]
- Packages Overview
      [Types of Packages. R base-Package. User Created Package. Installation]
- Data-types
      [Basic - Integer, Numeric, Character, Logical, Complex, Special Data-type. Advanced - Vector, List, Matrices, Table, Data Frame, Array]
- Loops and Conditional
      [Basic - usages of if-else, while, repeat and for loops in the context of R. Advanced - apply(), sapply(), l apply, t apply(), by(), plyr packages (xxply functions) etc.]
- String Manipulation in R
      [Introduction & Techniques. Regular expressions in R - sub(), gsub(), grep(), substr(), strsplit(), regexpr(), gregexpr()]
- Functions in R
      [Structure and usages of functions. User defined functions. Using built in functions - Logarithmic, Exponential, Operation on Sets, pmin() & pmax(), round() etc. ]
- Graphics in R
      [Use of graph and charts. Basic elements of graph generation. Graphics in R base-Package - par(), plot(). Grammar of graphics. ggplot2 package, basic elements of ggplot2 - qplot(), ggplot(), layered structure of ggplot2. Creating various charts using R base-Package & ggplot2- Pie, Bar, Histogram, Bubble etc.]
- R Connection with Database
      [working with MySql and R. R Packages for database connection. Analyzing Data from Database. 
- Debugging in R
     [Introduction. Example functions to debug. sbrowser(), debug(), undebug(), trace(), untrace(), setBreakPoint() etc.]

 NOTE : Each section is concluded with hands-on exercises (on inbuilt dataset and also by reading data from a file/database)

 

 

 

Section-B (Day-3):                                                                                                                

On day-3, we do a complete Data Analysis Project using R and try to figure out useful patterns for a real case (we have got data from a Clothing Store Chain, which has multiple stores across the city). So, towards the end, participants will do certain analysis on this data set and see the patterns using R graphics and charting tools.