{"id":15685,"date":"2021-05-24T12:24:00","date_gmt":"2021-05-24T19:24:00","guid":{"rendered":"https:\/\/devwww.3cloudsolutions.com\/post\/better-together-blog-series-a-day-as-a-data-scientist-3\/"},"modified":"2023-12-22T13:59:25","modified_gmt":"2023-12-22T21:59:25","slug":"better-together-blog-series-a-day-as-a-data-scientist","status":"publish","type":"post","link":"https:\/\/3cloudsolutions.com\/resources\/better-together-blog-series-a-day-as-a-data-scientist\/","title":{"rendered":"Better Together Blog Series – A Day as a Data Scientist"},"content":{"rendered":"

In the final post of our “Better Together” blog series<\/span><\/a>, I will recap my presentation from a series of BlueGranite Tech and Career Talks conducted in partnership with BDPA<\/a><\/span> (formerly known as ‘Black Data Processing Associates’\u200b), in which I talked about \u201cA Day in the Life of a Data Scientist\u201d. Data Scientists<\/a> are in high demand right now for our ability to provide predictions and insights to real-world business problems. The job is also very new, as shown in the figure below, so there is plenty of confusion about what we actually do. My twenty-minute talk at the end of this post sheds light on what my life as a Data Scientist is truly like.\"Day<\/p>\n

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Trends in Data Science<\/span><\/h2>\n

In my presentation, I discussed current trends and which skills are considered most important to master. One significant trend is automated machine learning<\/a><\/span> that takes away the tedious task of individually training, testing, and comparing different models. This is a welcome change that leaves more time for Data Scientists to deploy machine learning models.<\/p>\n

Another trend in Data Science is using artificial intelligence tools that are interpretative and fair. The concept of interpretability is important to understand not just what<\/em> a model predicts but why <\/em>it came to that prediction. Fairness goes hand in hand with interpretability to ensure AI models do not produce unfair outcomes for sensitive groups.<\/p>\n

Data Science Skills<\/span><\/h2>\n

Data Science is commonly defined as the overlap between the fields of statistics, computer science, and business\/communication. Anybody who succeeds in this role will be strong in each of these domains. Additionally, the specific skills required to succeed in the role are in constant flux. To succeed in my job, I need to be able to work with the following:<\/p>\n