5 Most Common Mistakes Data Scientists Make When Handling Data

Data science blunders that should not be ignored

Statistics, math, machine learning, and data visualization with R, Java, SQL, or Python are all required and vital skills for data scientists. Several online video tutorials and courses do not cover all of the needs of the sector. As a result, there are a few common blunders that rookie data scientists make.

Case study of Microsoft Tay Bot

On Twitter, Microsoft launched a chatbot dubbed “Tay” in March 2016. Tay was supposed to talk like a youngster, but it only lasted a day as it began tweeting bigoted and hateful things on social media. Tay learned how to speak with individuals depending on who she was talking to as an artificial intelligence system.

1) Missing data annotations and using corrupted data

Collecting and cleaning data takes up 60 percent of a data scientist’s effort. This is the least delightful task, but it is a necessary step. All subsequent processes must be carried out on clean data that serves as the foundation for a machine learning task.

2) Analyzing without any plans or questions

Before you begin the analysis, you must first decide on the direction you want to go and the technique you will employ. Any data science should begin with a clearly defined goal. Data scientists sometimes jump right into modeling and analysis without first considering the problems they are seeking to solve.

3) Using identical functions for a variety of issues

Since this would be totally hypothetical, different functions cannot be applied to the same issue. Some rookie data scientists may be tempted to use the same courses, functions, tools, and so on for each challenge.

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

This is something that many data scientists overlook, because more than half of projects never make it to production and remain in the Proof Of Concept (POC) stage.

  • 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

This is perhaps the most common blunder made by data scientists. Solving a data science problem and then communicating it to a non-tech audience is a different skill.

To conclude

Every new problem is an opportunity to learn and grow as a data scientist. When you are starting in your profession, do not be scared by these blunders. They will undoubtedly educate you on how to deal with various machine learning challenges in practice.

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Albert Christopher

Albert Christopher

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