5 Most Common Mistakes Data Scientists Make When Handling Data

Data science blunders that should not be ignored

Case study of Microsoft Tay Bot

1) Missing data annotations and using corrupted data

2) Analyzing without any plans or questions

3) Using identical functions for a variety of issues

4) Not considering a model as a component of a life-cycle

  • Training an ML algorithm
  • Evaluating and testing algorithms with the proper metrics
  • Deploying them with minimum performance standards (latency) is followed by model monitoring, training, and feedback.

5) Paying little to no attention to communication skills

To conclude

--

--

--

AI Researcher, Writer, Tech Geek. Contributing to Data Science & Deep Learning Projects. #coding #algorithms #machinelearning

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Basic Intro of Text Summarization

Starbucks Capstone Challenge

Underground Version 2.0

How to Hire Data Scientists

Notable maps visualizing COVID-19 and surrounding impacts

Mavrx Makes Precision Soil Sampling Easier

Essential OpenCV Functions to Get You Started into Computer Vision

Resources for Learning Sports Analytics Coding

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Albert Christopher

Albert Christopher

AI Researcher, Writer, Tech Geek. Contributing to Data Science & Deep Learning Projects. #coding #algorithms #machinelearning

More from Medium

PYTHON FUNDAS FOR DATA SCIENCE

Deep dive of Regularizations techniques

How To Approximate the Results of Your Sample Set (Empirical Rule vs. Chebyshev’s Formula)

Want to become a Data Scientist?