The boom in Data Scientists is purely due to the large amounts of data being able to provide us with solutions to our real-life problems. However, when it comes to Data Science, the theory that is being put into practice is not always the same as reality.
As a Data Scientist, it is very normal to receive large amounts of data that have issues with it and require heavy data cleaning, model design, and model execution. The problems are purely due to the complexity and scope of the data that is being used to answer a question. Problems in the data can be the number of features, the errors, the characteristics, and more.
When handling problems with your data, it is vital that the issues are handled correctly and efficiently.
So let’s have a look into some of the common problems with data and the solutions for them.
Not enough Data
The main component for a Data Scientist is Data; it’s part of their title. Without data, the movement of Data Science is limited, which would be a problem for a world that’s heavily dependent on data now.
If there is not enough data, it becomes a problem as it is an important element for training algorithms. If the data is limited, it can lead to inaccurate and inefficient outputs, costing the company a lot of time and resources. However, there are solutions to generating more data to train your model.