Why understanding domain matters for a data scientist

Data science is one field which deals with different domains. “Where ever you find data, you can apply science" – Data Science.

Now with advent of hadoop and bigdata, data storage and processing is smooth and easily done. Now the question is what can be done with the data?

  • Finding loyal customer
  • Bank Defaulters
  • Bank loan takers
  • Predict demand of a product
  • Customer segmentation prior to marketing
  • Fuel savings in logistics
  • Warehouse planning
  • Server outage predictions in telecom industry.
  • Stars labelling in Astronomy.
  • Provide better health care decisions
  • Predict who will leave company
  • Measure emotion in a Resume
  • HR analytics to measure performance of a team
  • On-time delivery in SCM.
  •  Targeting right customers
  • Churn prediction in telecom.
  • Epilepsy prediction in health care.
  • Cancer cells growth rate.
  • Better traffic management.
  • Understanding the flow of rivers to predict the movement of soils.
  • Better energy management.
  • Targeting the right customer
  • Optimization in every sector especially manufacturing
  • Cost cuttings in every sector
  • Personalized offers in ecommerce.
  • Recommendations
  • Digital Marketing
  • Fraud management
  • Stocks forecasting
  • Better Air Traffic Control
  • And many more........

Yes there are many more applications and future of the coming years for better living is Data Science. But the first and foremost step for data scientist is to understand enough domain for following reasons:
1.       To look accessible in client meetings.
2.       To understand client’s business for building better models.
3.       To extract domain related features in Feature Engineering.
4.       To build a data product, we have to understand what business problem we are addressing.
5.        To help customer in achieving the targets.
6.       For better feature selection.
7.       Your solutions will be implemented in business, so build quick, easy and better solutions.
8.       For out of box thinking.
9.       Creative solutions come up when we understand what domain we are dealing with.
10.   Accuracy measures vary from domain to domain, so before validating model, decide what accuracy measures would make sense in domain you are dealing with.
11.   What visualizations make sense.

 Finally, by understanding domain customers can be provided exceptional help, acceptable solutions. 


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