Outside Event #3
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Feb 20, 2024
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Uploaded by dinoman639
Outside Event #3
I chose to watch the 2021 Data Science Career Q&A for Undergrads. The three speakers in this event are Mallory LaRusso, Glen Wright Colopy and Richard Franzese. The video was uploaded on February 25
th
, 2021; however, I watched the video on April 16
th
, 2021. Mallory started the Q&A by asking Glen and Richard about their career path. Glen’s path started with a Math and Economics undergraduate and got a Masters in Operations research and a minor in Statistics. He then went to Oxford to get a Masters in Statistics and a Doctorate in Probabilistic Machine Learning. Richard went from Physics to Engineering Science and Biomedical Engineering and eventually found himself in the Pharmaceutical industry doing mathematical modeling. Mallory then asked what the typical workday is like. Richard said that his workload could range from severe coding, lots of emails, meetings, but the key message was that each day is very different. Glen had a different answer to this. He said that he would usually only work on one thing and try
and finish that. As a Data Science undergraduate, this might be a lot of what I do in the future. It is good to know before hand so I can know if this is something that I would like to continue doing or stop beforehand. Something that came as a shock to me was that Mallory and Glen both
said that reading and writing are very important in Data Science. I am not an avid reader and writer but that might have to change if I want to become a data scientist. I will have to pick up a knack for reading and writing as I am being paid to read, write, and analyze data. It is part of the job description. Glen and Richard brought up a good point that as a Data Scientist, we will usually not talk to each other and working alone so our documentation, and work needs to be very good. Glen then goes on to say that the harder Data Science jobs are available while the easy ones are not. If I am to get a job, I am going to have to be better than the average Data Scientist. Later in the video, Glen said the difference between Data Science and Data Analysts is
the coding required. Analysts usually use SQL, and the data is already curated while Data Scientist can do the same work as Analysts but primarily focus on statistical modeling and coding. This is a big distinction that I did not know and something I need to be wary of when I am applying for jobs and in interviews. If I could talk to them later, I would like to know why they chose to gravitate towards Data Science with their degrees. I would also like to ask them for
tips during the hiring and interview process for internships and careers in the future.
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