Category: Analytics

I am really liking the Rodeo IDE, it has a surprisingly comfortable feel to it, even though these are early days.

One of the reasons it is easy to use is it uses the Ace Editor as the underlying layer, which is the same editor that powers RStudio. It is thus very easy for a R User to use. Add in Pandas package and for a newbie Python User,  switching from R to Python is made quite easy.

Why do it? Well Python is surprisingly powerful for big-ger data. I am talking of the 4 gb -15 gb data range that is predominantly used in the data analytics world and which SAS rules in enterprises. R finds it tough to navigate this and training pure SAS users to intermediate R programming takes an expense as well as business disruption. Python could thus be an alternative arrow in the open source enterprise software arsenal.

RODEO is created by Yhat the startup which is makign waves by using BOTH R and Python.

Start Date	Duration	Stipend	Posted On	Application Deadline
16 Aug, 2015	2 Months	Rs.5000 /Month	5 Aug, 2015	26 Aug, 2015

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http://internshala.com/internship/detail/data-scientists-internship-in-delhi-at-decisionstats1438766609

Previous Interns Project Reports

1. Chandan Routray  IIT Kharagpur http://www.slideshare.net/ajayohri/decisionstatscom-data-science-virtual-internship and http://www.slideshare.net/ajayohri/python-for-r-users and blog  http://www.crackstats.in/ 2014 (summer and winter)
2. Farheen Nilofer Jamia Milia – CS Engg https://github.com/Decision-Stats/reports_15 amd blog https://dataorchid.wordpress.com/ 2015 (summer)
3. Sarah Masud Jamia Milia – CS Engg https://github.com/Decision-Stats/ppts  and Blog themessier.wordpress.com 2015 (summer)

Just like the 4 decade long detente when an Iron Curtain shut down most of East Europe for the rest of humanity, an Iron Firewall extends over the largest citizenry of a nation on this planet. This has led to a situation where China is a free member of the WTO and can take part in the bricks and mortar commercial space of everybody else but shutting down its own online businesses to its own control and preferences.

On the other hand, the increasing skirmishes between USA and China in Cyber Conflict could boil down to simple immigration. The USA has been the leading center of global innovation simply because it got the best cherry picked immigrants of Europe and Asia, beating Germany , Japan and even Russia at global supremacy. China’s lack of efforts to attract diversity of minds and perspectives to its own borders means it is simply going to lack of hacktivists, cyber warriors and allies in the cyber cold war.

Allies win wars. Just like catches win matches.

http://www.newsweek.com/2015/05/29/us-china-cold-war-333948.html

Michael Berry whom we interviewed here at https://decisionstats.com/2010/10/05/interview-michael-j-a-berry-data-miners-inc/ is giving a session at PAW on Modeling Techniques

This is a featured post by our sponsor-

Spotlighted PAW Session: Michael Berry on Modeling Techniques

A long-term veteran expert, consultant, and instructor – who is normally found in a keynote session – TripAdvisor’s Michael Berry will serve PAW’s audience with invaluable insights by way of his highly rated, captivating speaking style. Mr. Berry is also the founder of the consultancy Data Miners and co-author of popular books, including Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management.

Witness this and 30 other sessions at Predictive Analytics World for Business,

September 27- October 1, 2015 in Boston.

Michael Berry Analytics Director

Tripadvisor for Business

SESSION: Picking the Right Modeling Technique for the Problem

Decision Tree? Neural Network? Regression? Naive Bayes? Support Vector Machine? It is said that when your only tool is a hammer, every problem looks like a thumb. Modern data mining toolkits are full of tools, but how do you pick the right tool for a particular predictive analytics task? Presenter: Michael Berry

I have delivered trainings by now to hundreds of students and professionals by both online as well as offline means.  Mostly I get great reviews. But twice in a decade I have bombed too.

Here are some thoughts on this-

1) Prior preparation is very necessary. day before class , whole code should be run one day before and tested.
Why do trainers not prepare every time- well time bandwidth is an issue.

2) Enterprise  clients spend on invoice, infrastructure, transport but also on per employee time. So errors during a training session with retail clients it is ok, but one has to be be very prepared with corporate clients. Corporate clients do not tolerate errors of even ten minutes.

3) You can lose the flow if you interrupt your training to questions and answers. Sometimes it is best to park questions.

4) Self discipline is always necessary in case a few assertive students don’t corner or hijack the agenda to what they need . The needs of the many outweigh the needs of the few in analytics training.

5) Even after numerous prior meetings, client requirements and intentions can remain unclear on what they want from training. Keep some flexibility but maintain pedagogy discipline.

6) Always ask prior requirements to read or join the training.

7) Homework rarely works with adults taking training in both online or offline classes. Classroom quizzes and live tasks work better.

8) R and Python are very dynamic in analytics technology. So keep updating those ppts. Sharing 10% of your content for free online can bring you a lot of leads for repeat business free.

9) Always ask for due time to create, rehearse and present the training regardless of client urgency. Training in analytics are long term investments and hurry can compromise your quality and reputation.

10) Always be proactive. If client is very unhappy, be the first to offer discounts. If the client is happy ask for a LinkedIn testimonial.

I spoke at this conference this Sunday July 26, 2015 http://www.iimnetwork.com/event-delhi-26-jul-15-pan-iim-alumni

• Install MySQL

sudo apt-get install mysql-server

• Check if Server is Running

sudo netstat -tap | grep mysql

I use the MySQL command line to check it

To connect

mysql -h localhost -u root -p

To see databases

mysql>show databases;

To see tables

mysql> show tables from mysql;

To quit mysql

mysql> \q

• Install and load RMySQL from within R

install.packages(“RMySQL”)

library(RMySQL)

• I connect using this

mydb = dbConnect(MySQL(),
user=’root’,
password=’XXX’,
host=’localhost’,
port = 8018,
dbname=’mysql’)

• I write sql queries using this

> dbGetQuery(mydb, "select * from  servers")
[1] Server_name Host        Db          Username    Password   
[6] Port        Socket      Wrapper     Owner      
<0 rows> (or 0-length row.names)
> dbGetQuery(mydb, "select * from  db")
 [1] Host                  Db                   
 [3] User                  Select_priv          
 [5] Insert_priv           Update_priv          
 [7] Delete_priv           Create_priv          
 [9] Drop_priv             Grant_priv           
[11] References_priv       Index_priv           
[13] Alter_priv            Create_tmp_table_priv
[15] Lock_tables_priv      Create_view_priv     
[17] Show_view_priv        Create_routine_priv  
[19] Alter_routine_priv    Execute_priv         
[21] Event_priv            Trigger_priv         
<0 rows> (or 0-length row.names)

Source-

https://help.ubuntu.com/12.04/serverguide/mysql.html

RMySQL 0.10.0

https://mkmanu.wordpress.com/2014/07/24/r-and-mysql-a-tutorial-for-beginners/