Category: Analytics

This is the brochure for Summer School in Analytics- a ten day intensive program in classroom at Delhi, India on R, Python and SAS languages.

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!!

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.

> 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

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

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