Gödel Research Lab 

Solving Complex Problems

We are launching short term courses in various technologies to share our knowledge with the ever growing community of Entrepreneurs and Product Enthusiasts. We are starting with Data Sciences for all the reasons you can hear in any of the serious tech circles today. Let's not get much into the Why and start Doing it. Please see below for the course details.

                                               Data Science Course

Course Philosophy : We have divided the Course into two Phases. Phase -I is the foundation - aimed at building a strong base to pursue the Advanced topics to be covered in Phase-II. 

Phase - I : This phase is meant for Absolute Beginners and also for people who know the basics but are not hands-on and need to brush-up. This phase is further divided into two parts --

1. R programming for Data Science

2. Statistics with R

Once through these course, the Learner will be well prepared to approach applications of Statistics in Analyzing and finding Meaningful Patterns in Data (Data Science).

Phase - II : In the Second Phase we will go over the applications. Some of the specializations are  --

1. Data Mining 

2. Machine Learning

3. Web Mining

4. Big Data Analytics and Parallel Computing with R

R has already got huge popularity in Industry and also among Researchers. So, R is the base language / tool which will be used in all the advanced courses. 

                                                         I. R Programming for Data Science 


We are Launching the first course in this series - R Programming for Data Science (Part-1 of Phase-I). Registrations are open! 

Instructor: Raju Mishra [LinkedIn - in.linkedin.com/pub/raju-mishra/91/914/278 ]

Course Brief : This course provides hands-on experience of working with R Programming Language. R is the most widely used tool in the Data Science World (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. 

Prerequisites : This course doesn't have any prerequisite requirements and is self-contained.

Detailed Topics : Please see below for the topics to be covered in this course. Each sections is concluded with hands on exercises. 



                                                                  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.]
                                                                    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.