From the course: Introduction to Career Skills in Data Analytics

Data scientists

From the course: Introduction to Career Skills in Data Analytics

Data scientists

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- People often pursue data with the hopes of becoming a data scientist. And I believe it's important to know that not all data professionals grow into data scientists, nor do we need all analysts or engineers to turn into data scientists. Data scientists will likely have all the skills of the analyst engineer and they will have likely worked in those roles. However, a data scientist will have a heavier requirement for skills in coding, mathematics, and statistics. A data scientist will be instrumental in developing tools and instruments that provide valuable insight to the organization, but they can't do it alone without all the other roles, or well, maybe they can perform the task, but when you don't have all the other roles, the data scientists must perform them. Data scientists or data science teams comprised of all the disciplines will interpret large sets. They'll likely build machine learning models. They'll present outcomes and make suggestions as a portion of what they do. They'll likely be leaders in the data science team. They'll provide support and strategy to the overall data governance plan. If you want to further your skills in this area, you should consider gaining a better understanding of programmatic thinking. You'll want to dive deeper into learning code and maybe start with something like Python. If you have some stats experience, or not, you will definitely want to grow in this area. Remember, one of the key differences between data scientists and all other roles is heavier math, coding, and stats. It's also important to remember that for most organizations, having a data scientist and not having all the other roles means that that data scientist is having to perform all those roles before they get to the data science. This is where having a team of multi-discipline people serving all the roles might just be your next play.

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