What are the challenges faced by a Data Scientist?

Data Science, AI and ML
#1

In the day-to-day existence of a data scientist, there are a few problems. Here are a few examples:

  1. Every other day, you will be presented with a new challenge. You frequently find it difficult to solve problems you have never encountered before. Likely, the customer won’t understand the scope of the problem you’re dealing with therefore they’ll strive to stick to the project deadlines. If you can’t get yourself out of every circumstance, this might lead to worry and anxiety.

  2. We frequently mistakenly believe that data science is all about creating clever models, but this is not the case. Documentation and post-deployment validations are the tasks that irritate me the most.

  3. Data might vary over time, so let’s say you built a model based on one set of data. The company has introduced a few more products, and statistics on those products are now available.

  4. Often, the data you require is not easily available, and you must produce it. You must be perceptive enough to consider the optimal method for generating a useful dataset.