However, have you ever thought about what kind of problems data scientists can work on?
Posted: Tue Jan 21, 2025 5:11 am
The growth of data seems indisputable. In 2020, each person generated 1.7 megabytes of data per second , and today it would take a person more than 180 million years to download all the data on the Internet .
As you know, technology allows us to use this data to make decisions, which is why 94% of companies say that data is important for their business growth and digital transformation .
In this context, the field of data science emerges with the mission of making the most of the data generated. But how is this amount of data managed to provide information and recommendations to the business?
To answer this fundamental question, data scientists are hired: The U.S. Bureau of Labor Statistics predicts that the number of jobs in the data science field will grow by approximately 28% through 2026 .
Here at Rock Content, we use data to predict when a nepal phone number list customer is going to cancel a contract, so we can use retention techniques before they make that decision.
When we detect this possibility, other teams proactively engage with customers to salvage this revenue.
This is not the only application of data science . From challenges related to acquiring new customers to cross-selling opportunities in business, data scientists focus on consuming data to solve problems .
It is natural for data scientists to approach those business problems with different strategies. While it is healthy, especially when a team is full of professionals from different backgrounds, there is one characteristic among the most successful ones that I would like to discuss.
Real-life data science projects are not exactly the same as those found in learning environments or on data competition websites, such as Kaggle .
As you know, technology allows us to use this data to make decisions, which is why 94% of companies say that data is important for their business growth and digital transformation .
In this context, the field of data science emerges with the mission of making the most of the data generated. But how is this amount of data managed to provide information and recommendations to the business?
To answer this fundamental question, data scientists are hired: The U.S. Bureau of Labor Statistics predicts that the number of jobs in the data science field will grow by approximately 28% through 2026 .
Here at Rock Content, we use data to predict when a nepal phone number list customer is going to cancel a contract, so we can use retention techniques before they make that decision.
When we detect this possibility, other teams proactively engage with customers to salvage this revenue.
This is not the only application of data science . From challenges related to acquiring new customers to cross-selling opportunities in business, data scientists focus on consuming data to solve problems .
It is natural for data scientists to approach those business problems with different strategies. While it is healthy, especially when a team is full of professionals from different backgrounds, there is one characteristic among the most successful ones that I would like to discuss.
Real-life data science projects are not exactly the same as those found in learning environments or on data competition websites, such as Kaggle .