Citizen Data Scientist Vs. Academic Data Scientist: Case Study SalesForce Einstein Discovery

Citizen Data Scientist is a relatively new term, used for people using latest advanced capabilities to general ML models, but are not from a core Data Science or Statistics background.

In heading here, have specifically used a prefix ‘Academic’, reasons being:

  • One or single time Analytics only >A single table non-complex dataset
  • Standard process followed

Also to set context, a bit about Automated Machine Learning (Auto ML), specific the one developed by SalesForce here. Its important to note here that even in R/ Python, we do have now open source AutoML libraries available; but again they do require substantial coding knowledge & efforts to utilize and finally deploy.

About Data & Business Problem:

Company: Mobile Network provider, Dataset: Customer details/ consumption, Business Problem: Customer Attrition

Steps followed in an Academic Process:

  1. Data Exploration & Visualization
  2. Data Preparing (Training & Testing datasets)
  3. Initial model (M1) building with all variables includes
  4. Model M1: Variables importance & Model performance analysis
  5. Model refining (M2, M3,..): By changing hyperparameters, Variables selection
  6. Models (M1, M2, ..): Performance Comparison & final model selection
  7. Final selected Model: Communicating Results

Data Exploration & Visualization:

Data Preparation (Division):

Initial Model Building, with all variables available:

Model Refining:

Models Comparison:

Final Interpretatio:

Final Thoughts…

Modelling by the Einstein Discovery is coding-free, highly time-saving & intuitive for non-Data Science experts also.

However, this does not rule out importance of Domain knowledge & interpreting results of a ML model statistically.

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