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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