I am going to speak for the first time in SQLRally 2015.
Talking about how data mining approach proposed by Microsoft can help industry to identify their potential customers.
http://www.sqlpass.org/sqlrally/2015/nordic/Sessions/Speakers.aspx
SQL Bits 2015 (XIV) is close to happen in March from 4th to 7th in London.
I have submitted some sessions in upcoming SQLBits, and I hope to get one or more accepted. SQLBits usually picks sessions based on community’s voice. so please go to link below and vote for any session you like to see in this conference;
https://sqlbits.com/information/PublicSessions.aspx
some time there is need to convert a variable to specific data type
in Microsoft SQL server 2008 R2
Cast : select CAST(2000 as varchar); you indicate the target type
Covert, beside the type you can choose the style of the conversion
select convert (DATE,’11/17/2014′,101); (date is data type, 101 is the date american style )
it always matter to choose suitable data type for the columns in table.
1- choosing a data type is equivalent to define a constraint :
e.g : employee_hiredate : DATE–> if the user enter the string value such as “November” database engine report an error to user.
2- if using an inappropriate data type we will miss the some operation. e.g. “+” for numeric data stand for add and sum whilst for character and string employ for concatenation
3. waste the memory: choosing appropriate type for your data.
e.g choosing an integer type for a field “Flag” which just hold “Y” or “N” is not an appropriate choice. imagine that there are around 100000 rows . So instead of allocating 200000 bit to “Flag” field, by choosing Integer type, system will allocate around 1200000 bit which about 800000 are useless.
4- if your data need to present small or very large number with out and they don’t need accuracy then using Float and Real data type is a wise decision.
5-Fixed type or dynamic one. (varchar vs char or Nchar vs Nvarchar) , when data need to be updated frequently it is good to choose a fixed type such as char or Nchar, however dynamic one (nvarchar) are good for saving storage .
imagine
Finished presentation on Data mining approach in SQLSAT352 in Sydney, 25th October
https://www.sqlsaturday.com/viewsession.aspx?sat=352&sessionid=24344
In the most marketing departments, the tactical question is about who are going to buy our products. It is more cost effective to identify and spend money on highly potential customers (than those who are not likely to purchase). This also affects the advertisement strategy. Potential customers and their traits can be identified by analysing previous purchasing information. Management experts can predict who is going to be their new customers by analysing their current customer purchase information. There are many data mining algorithms which can help with this task. Microsoft Business Intelligence employs data mining algorithms that are deployed in an easy to use environment. This demonstration based session will show how to use previous customer purchase information to predict potential customers. We will discuss how to set data sets and use different data mining algorithms to get predictive results and then demonstrate how to find the best predictions.
Thanks for attending my session, I just finished my presentation in Oregan SQLSAT 337 in Oregan
please find the below link to access the presentation power point
https://www.sqlsaturday.com/viewsession.aspx?sat=337&sessionid=25169
In the most marketing departments, the tactical question is about who are going to buy our products. It is more cost effective to identify and spend money on highly potential customers (than those who are not likely to purchase). This also affects the advertisement strategy. Potential customers and their traits can be identified by analysing previous purchasing information. Management experts can predict who is going to be their new customers by analysing their current customer purchase information. There are many data mining algorithms which can help with this task. Microsoft Business Intelligence employs data mining algorithms that are deployed in an easy to use environment. This demonstration based session will show how to use previous customer purchase information to predict potential customers. We will discuss how to set data sets and use different data mining algorithms to get predictive results and then demonstrate how to find the best predictions.