Life Cycle of a Data Science Project

It is best to use CRISP -DM, SEMMA and/or KDD for a systematic approach

1) Understanding Business Requirements from Client

2) Converting Business Problem to a Statistical Problem

• what data to collect

• what is the cost of data

• how can I enhance the data

• data quality issues

3) Solving Statistical Problem with Tools (R, SAS, Excel)

• import

• data quality

• outlier and missing value treatment

• exploratory analysis

• data visualization

• hypothesis and problem framing

• data mining and pattern identification

• create success parameters for statistical solution

4) Converting Statistical Solution to Business Solution

• project report template

• assumptions and caveats

• feedback from stakeholders

5) Communicating Business Solution to Client

• presentation

• report

• customer satisfaction

• monitoring of results

Author: Ajay Ohri

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