We have heard about the difficulty of managing big data. We have heard of parallel computing, which means Hadoop and Spark.
Little-known aspect of this work
Less well known is the aggregation and labeling aspects of data scientist work. Surprisingly, this is one of the company's most important things because you are trying to tell the company how to handle your product. This means that Google Analytics will tell you what kind of insights you can give me with the data, such as what happened to my users. Metrics are important because they tell you what happened to the product. These metrics will tell you if you are successful. In addition, A/B testing and experimentation will let you know which product versions are best. These things are very important, but they are not well reported in the media. The media reports on artificial intelligence and deep learning. We have already heard of it. But when you think about it, it's actually not a top priority for companies and industries. Or at least it is not something that produces the most results with the least amount of effort.
What did the data scientist really do?
It depends on the size of the company. At startups, you lack resources. Therefore, they may have only one data scientist. That data scientist will do all the work related to various data science roles. He may not do artificial intelligence and deep learning, as this may not be a priority now. He will have to build the entire data structure. He may even need to write some software code to add logging and then analyze it himself. Then he will have to build his own indicators. He even has to do the A/B test himself.
For medium-sized companies, they have more resources. They can separate data engineers from data scientists. Therefore, the collection will be handled by software engineering, and the move/storage and exploration/conversion operations may be handled by the data engineer. The data scientist will be responsible for the rest of the work. The role of data scientists can become very technical, which is why most companies hire PhD or master's degree holders to take on this role because they want you to do more complex things.
Let us talk about the situation of a big company now. They tend to have more money and can spend more staff. So you can get more employees to work in different areas. This way, employees don't have to think about what they don't want to do. They can focus on what they do best.
Therefore, data science is all this, and your work depends on your company.
Orignal From: Learn more about data science work
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