Data Mining & Analytics with R : Introduction to R, RStudio and Rattle

Data Mining & Analytics with R : Introduction to R, RStudio and Rattle - Day 1

This is a blog post on a workshop I attended : a three-day hands-on Workshop on Data Mining & Analytics with R at Technopark, Trivandrum on 5th May 2015

Taught by :

Graham Williams - Senior Director and Data Scientist , Australia

Graham.Williams@togaware.com

http://datamining.togaware.com

Read About

- Literate Programming

- Literate Data Mining

Philosophy

We are not writing program for the computer, it is written to share with other people.

Everything that we write should be written for others.

We should control the computer not vice versa.

Introducing Data Science

Data Mining

Started in around 1989.

Lego house - model - > not real but we get an idea. Data mining is all about building such models.

Analytics

Descriptive Analytics - what happens , suggestions in Amazon

Diagnostic Analytics - explain why the above happened

Correlation and Causation - Find up people who ended up in hospital after taking a particular drug

Predictive Analytics - machine learning and statistics models - predicts when it happen in the future.

Prescriptive Analytics - decide on what to do and how to decide the best interaction

Philosophy

Science brings knowledge, philosophy brings wisdom

Everything begins as science and ends as an ard.

The Data Roles

Data Technician -> Data Analyst (Add value to data) -> Data Miner (Computer Scientist/Statistician, Machine Learning etc) -> Data Scientist (Ability to follow one’s intuitions to draw it all together)

Continued.. Read Day 2 Here.

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