Give an overview of the analytics life-cycle in an organization, over view of data mining – its position in an organization’s business intelligence efforts, and the need to find the right data mining technique for the specific application in order to achieve accurate results.

Then, I’d like to list out the data mining techniques (only applicable to predictive analytics) and explain each one of those in detail( Based on my brief literature review, I identified the broad categories: regression, classification, time series analysis etc., I’d like to see the specific techniques listed under each category as applicable). This includes explanation of the variables/constants in the technique, and explain how those variables impact the prediction outcome (gives the reader an idea as to what parameters can be tweaked to improve the accuracy of the algorithm) and the pros and cons of each technique (theoretically stating them)

Then I would like to qualitatively/quantitatively compare (using some open source data mining tools) which technique works better and report my findings on why a technique worked better than others (verifying the above said pros and cons).

If you could develop the content, I’ll probably edit it to accommodate my testing of the algorithms where applicable. If not, I should still be able to use it as a qualitative research.

Give an overview of the analytics life-cycle in an organization, over view of data mining – its position in an organization’s business intelligence efforts, and the need to find the right data mining technique for the specific application in order to achieve accurate results.

Then, I’d like to list out the data mining techniques (only applicable to predictive analytics) and explain each one of those in detail( Based on my brief literature review, I identified the broad categories: regression, classification, time series analysis etc., I’d like to see the specific techniques listed under each category as applicable). This includes explanation of the variables/constants in the technique, and explain how those variables impact the prediction outcome (gives the reader an idea as to what parameters can be tweaked to improve the accuracy of the algorithm) and the pros and cons of each technique (theoretically stating them)

Then I would like to qualitatively/quantitatively compare (using some open source data mining tools) which technique works better and report my findings on why a technique worked better than others (verifying the above said pros and cons).

If you could develop the content, I’ll probably edit it to accommodate my testing of the algorithms where applicable. If not, I should still be able to use it as a qualitative research.

Give an overview of the analytics life-cycle in an organization, over view of data mining – its position in an organization’s business intelligence efforts, and the need to find the right data mining technique for the specific application in order to achieve accurate results.

Then, I’d like to list out the data mining techniques (only applicable to predictive analytics) and explain each one of those in detail( Based on my brief literature review, I identified the broad categories: regression, classification, time series analysis etc., I’d like to see the specific techniques listed under each category as applicable). This includes explanation of the variables/constants in the technique, and explain how those variables impact the prediction outcome (gives the reader an idea as to what parameters can be tweaked to improve the accuracy of the algorithm) and the pros and cons of each technique (theoretically stating them)

Then I would like to qualitatively/quantitatively compare (using some open source data mining tools) which technique works better and report my findings on why a technique worked better than others (verifying the above said pros and cons).

If you could develop the content, I’ll probably edit it to accommodate my testing of the algorithms where applicable. If not, I should still be able to use it as a qualitative research.