5 step approach to Data Analytics problems

Data Profiling – or simply data discovery. Based on your business objectives and what information you would like to extract we understand your data with a variety of statistical measurements. This could be from simpler methods like mean, median to more complex methods like variance or standard deviation or quartile analysis

Outcome: a sense of data ranges and fields to build next steps in analysis

Find systematic patterns using Regression – we apply regression techniques to identify significant relationships and strength of impact of independent variables to dependent variables.

Outcome: key variables impacting our outcome, and their strength which can help us prioritize in case of conflicts. Ex: living in San Francisco vs having a family is more important factor for buying a car.

Correlation analysis – Looking to see if there are unique relationships between variables that are not immediately obvious. Ex: Is credit score and monthly income correlated? If yes then how does this impact my outcome?

Outcome: set of parameters/variables highly correlated which will impact our business decisions.

Outlier analysis – are the outliers showing us new emerging trends or are they just outliers? We need to check this in our data to capture trends early.

Outcome: set of outliers with their future impact on outcome. What’s the onset of those outlier?

Cohort/cluster analysis – data segmentation. Then combine the outcome of previous steps with coming up with cohorts or segmentation to target with business objectives. This is where we discover consumer preferences, segment our data, and analyze micro data for improving our decision making

Outcome: set of variables and their values which result in targeted cohorts

All in all combined you can see a full picture of your data with results and outcomes expressed as visuals for better understanding of your data.

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