Month: May 2015

KDnuggets Poll -Is Rapid Miner 3 times more used as SAS

16th annual KDnuggets Software Poll continued to get huge attention from analytics and data mining community and vendors, attracting about 2,800 voters, who chose from a record number of 93 different tools.

from

http://www.kdnuggets.com/2015/05/poll-r-rapidminer-python-big-data-spark.html

What seems a rather disquieting sampling error-

RapidMiner remains the most popular suite for data mining/data science, but it got fewer votes than last year

 

The top 10 tools by share of users were

  1. R, 46.9% share ( 38.5% in 2014)

  2. RapidMiner, 31.5% ( 44.2% in 2014)

  3. SQL, 30.9% ( 25.3% in 2014)

  4. Python, 30.3% ( 19.5% in 2014)

  5. Excel, 22.9% ( 25.8% in 2014)

  6. KNIME, 20.0% ( 15.0% in 2014)

  7. Hadoop, 18.4% ( 12.7% in 2014)

  8. Tableau, 12.4% ( 9.1% in 2014)

  9. SAS, 11.3 (10.9% in 2014)

 

I really dont think Rapid Miner has three times SAS users. I have no doubts on the credibility of the poll but there seems either sampling bias or something plain wrong here

!!!!

and 44.2 % of users used Rapid Miner last year ( I dont think one in two data miners uses Rapid Miner)

So there is some error here- or maybe different ways of counting a user or not!!

Moobhi Review- Piku Emotion in Motion

Shoojit Sircar has written a love poem to the saga of probashi Bongalis, Kolkatta longing and the fine yet quixotic and sometimes insular Bong culture. He has relied on shortcuts and stereotypes to finish the story in the time alloted. Deepika looks great with Kajal laced Bengali Eyes, but someone needs to tell her to get accent training. Irrfan can act better with his eyes and mouth closed, than Karan Johar can act with his entire body.

Amitabh Bachchan just disappears into his role as Bhaskar Da. Moushmi Chatterjee lifts occasional sag into the story pace. What a nice story? If only non Bengalis knew more about their culture than just Bengali sweets.

piku-mos_650_103114032933

Dealing with zip files in R #rstats

> setwd("/home/ajay/Downloads")
> a=dir()
> class(a)
[1] "character"
> grep(".zip",a)
[1]  37  38  41  43  88  96 133
> b=grep(".zip",a)
> a[b]
[1] "alissa-coming-soon-v2-0(1).zip"            
[2] "alissa-coming-soon-v2-0.zip"               
[3] "CAX_EMC_Journalist_Data.zip"               
[4] "CAX_EMC_Racer_Data.zip"                    
[5] "matlab_R2015a_glnxa64.zip"                 
[6] "Photos.zip"                                
[7] "unvbasicvapp__9411003__vmx__en__sp0__1.zip"
> unzip("CAX_EMC_Racer_Data.zip")
> c=dir() 
library(Hmisc)

> c[c %nin% a] 
[1] "CAX_EMC_Racer_Garmin_Camera.csv" 
[2] "CAX_EMC_Racer_Garmin_Watch_Data.csv"
[3] "CAX_EMC_Racer_Motorcycle_Data.csv"
  ps- I know Hadley's convenient wrappR  packages are all the rage now, but nothing, i repeat
 nothing beats Frank Harell and Ripley's cool packages

	


	







	

		
		
Mad Max Movie Review : Why Fury Road is so awesome
	


	
	
	

		
    

  1. They didnot use computer generated effects, they used human generated effects.
    2. 

  2. They made sure 3D was actually realistic and not just a rip off to charge more money.
    3. 

  3. Tom Hardy and Theron are awesome actors. So are Hoult. No the splendid wifey was not a splendid actress again.
    4. 

  4. The same director and the same villain as 1979! movie
    5. 

  5. Design ! Design !
    6. 

  6. Witness the V8 riding off to Valhalla.
    7. 

  7. The sound track was actually appropriate to the context and delivered!
    8. 

  8. Double guitar throwing flames from a guitarist suspended in air in a truck full of speakers and drum players!
    9. 

  9. Mechanical Engineering is much more cool than Computer Science engineering in terms of visual effects :-p
    10. 

  10. I wish Mel Gibson had made a cameo. or Tina Turner!
    11. 




	


	





	

		
		
DecisionStats  Summer School in Delhi 2015 #rstats
	


	
	
	

		
This summer get a foothold in the world of data science. These are in classroom trainings for Delhi India and all prices are in INR only.


If you are interested apply here-


bit.ly/decisionstats


Screenshot from 2015-05-20 20:01:25




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  • Bring your own device. Hardware – with >2GB RAM and >20 GB Hard Disk Free
    • 

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17- 18 June
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Course Name
Introduction to


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Introduction to Analytics


using Python

Introduction to


Analytics using R

SAS Language


Fundamentals






Hours
10
10
20
10





Classes
4
4
8
4





Days
2
2
4
2





Cost
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10000










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Basics of Data Science
Introduction to Python
Introduction to R
Introduction to Interface




Basics of Analytics
Introduction to iPython
Introduction to R Studio
Introduction to SAS language




LTV Analysis
Introduction to Pandas
Introduction to R
Data Step




LTV Analysis Quiz
Introduction to iPython Notebook
Introduction to Rattle
Proc Print




RFM Analysis
IDE- IDLE and Spyder
Deducer
Proc Means and Proc Freq




RFM Analysis Quiz
Python 1 Quiz
R Quiz 1
SAS Quiz 1




Basic Stats
Data Input
Data Input
Proc Univariate




Introduction to Modeling
Data Analysis
Data Analysis
Do loops





Data Summarization
Data Summarization
Proc sgplot




Introduction to Google Analytics
Data Visualization
Data Visualization
Proc SQL




Blogging
Data Output
Data Output
SAS Macro Language




Web Analytics Quiz
Ipython 2 Quiz
R Quiz 2
menu driven options






data.table
ODS Output






ggplot







sports analytics
SAS Quiz 2






regression model







data mining







R Quiz 3







social network analysis







text mining







time series forecasting







Using apis







association analysis







R Quiz 4







RODBC







sqldf







spatial analytics







RMarkDown







Using Github







R Quiz 5











If you are interested apply here-


bit.ly/decisionstats



	


	





	

		
		
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