A traditional business problem customized here to data science.

1. Identify the problem

• Identify metrics used to measure success over baseline (doing nothing)
• Identify type of problem: prototyping, proof of concept, root cause analysis, predictive analytics, prescriptive analytics, machine-to-machine implementation
• Identify key people within your organization and outside
• Get specifications, requirements, priorities, budgets
• How accurate the solution needs to be?
• Do we need all the data?
• Built internally versus using a vendor solution
• Vendor comparison, benchmarking

2. Identify available data sources

• Extract (or obtain) and check sample data (use sound sampling techniques); discuss fields to make sure data is understood by you
• Perform EDA (exploratory analysis, data dictionary)
• Assess quality of data, and value available in data
• Identify data glitches, find work-around
• Is quality and fields populated consistent over time?
• Are some fields a blend of different stuff (example: keyword field, sometimes equal to user query, sometimes to advertiser keyword, with no way to know except via statistical analyses or by talking to business people)
• How to improve data quality moving forward
• Do I need to create mini summary tables / database to
• Which tool do I need (R, Excel, Tableau, Python, Perl, Tableau, SAS and so on)

3. Identify if additional data sources are needed

• What fields should be capture
• How granular
• How much historical data
• Do we need real time data
• How to store or access the data (NoSQL? Map-Reduce?)
• Do we need experimental design?

4. Statistical Analyses

• Use imputation methods as needed
• Detect / remove outliers
• Selecting variables (variables reduction)
• Is the data censored (hidden data, as in survival analysis or time-to-crime statistics)
• Cross-correlation analysis
• Model selection (as needed, favor simple models)
• Sensitivity analysis
• Cross-validation, model fitting
• Measure accuracy, provide confidence intervals

5. Implementation, development

• FSSRR: Fast, simple, scalable, robust, re-usable
• How frequently do I need to update lookup tables, white lists, data uploads, and so on
• Debugging
• Need to create an API to communicate with other apps?

6. Communicate results

• Need to integrate results in dashboard? Need to create an email alert system?
• Decide on dashboard architecture, with business people
• Visualization
• Discuss potential improvements (with cost estimates)
• Provide training
• Commenting code, writing a technical report, explaining how your solution should be used, parameters fine-tuned, and results interpreted

7. Maintenance

• Test the model or implementation; stress tests
• Regular updates
• Final outsourcing to engineering and business people in your company, once solutions is stable
• Help move solution to new platform or vendor