Data science is an exciting and pivotal sector. Its practitioners are increasingly in demand in a wide variety of fields, from business to healthcare to the public sphere. A data scientist’s role is to help collect, process, elucidate, and draw conclusions from data, helping organizations and individuals solve problems and make the best decisions possible - therefore, they must be savvy across diverse disciplines. This includes being fluent in numerous coding languages (such as R, Python, SAS, etc.) and having a comprehensive understanding of statistics and analytics, while knowing how to adeptly model and visualize data, in order to come up with the best solution possible in context of the field they work in. Solutions are often derived through writing algorithms, designing A/B tests, and devising ways to ensure data accuracy. Effective communication skills are crucial, as the data scientist must often persuade others - who may not be as familiar with data science principles - of their ideas. The role is often a collaborative one, as data scientists often work in tandem with product development or other departments, and may later hand their prototyped models off to a machine learning engineer, who is responsible for adapting and scaling the models by writing production-level code.

When examining the differences between data scientists and the classic statistician, one notes the approach that each takes to a problem and the way that they relay information to others. While statisticians appear to work in a more amorphous manner, data scientists present information in a more digestible and relatable manner. Data scientists examine and compare numerous machine learning algorithms and possible models, trying to learn as much as possible from the data, while a statistician tries to build the single, simplest model possible. A data scientist is more likely to do predictive modeling to try to gain insights on the future, while crafting solutions that are real-time (such as the Facebook newsfeed algorithm) to create the optimal user experience. In my learning pursuits thus far, I have identified more readily with the role of the statistician, being primarily familiar with ways to clean and visualize data; but I am excited to dive more deeply into the data science field, to discover how to apply my statistics knowledge in a way that can create positive change in an organization’s strategic approach. In my professional life, I work in government affairs and advocacy, and I hope to one day work for an association or legislator as a policy director, helping shape the office’s legislative priorities while monitoring the latest developments in particular areas of interest, such as the environment, public health, housing, and income inequality. It is essential that we keep abreast of the latest patterns and trends on this issues; and I believe that having the skills of a data scientist will help ensure that we are making positive, impactful decisions while supporting the most beneficial solutions possible.

I’m looking forward to this semester’s journey, as well as to go forth and apply the skills that we will learn in my chosen vocation.

Sources referenced:

• https://medium.com/odscjournal/data-scientists-versus-statisticians-8ea146b7a47f
• https://mixpanel.com/blog/2016/03/30/this-is-the-difference-between-statistics-and-data-science/
• https://www.simplilearn.com/data-science-vs-data-analytics-vs-machine-learning-article
• https://www.springboard.com/blog/machine-learning-engineer-vs-data-scientist/