How To Build A Career in Data Science with Jacqueline Nolis and Emily Robinson

<p>How To Build A Career in Data Science with <strong>Jacqueline Nolis</strong> and <strong>Emily Robinson</strong></p><p><br></p><p><u>Episode Show Notes:</u></p><ul><li>Jacqueline engages in industrial consulting such as T-Mobile, Expedia and Emily has background in R programming experience and having been at DataCamp, Etsy, Metis and now Warby Parker. </li><li>As all science fields converge into data science, is now the best time to build a career in data science? There is growth in AI and data science and with many conferences happening; this is the best scale up advice from Jr. to Sr. data scientists. </li><li>The book ‘<a href="https://www.manning.com/books/build-a-career-in-data-science" rel="nofollow">Build a Career in Data Science</a>’ explores topics including stakeholders, deploying models in production and community conferences such as Metis Data Science Bootcamp. More includes moving up the ladder and leaving the job gracefully. </li><li>COVID-19 has been a hot topic since 2020 and data science teams need to adjust to the new circumstances. Data science organizations must consider short term versus long term staffing decisions. The remote culture is picking up because of the pandemic and we should consider phases such as great recovery, slow recovery and communities going remote. </li><li>Prioritizing work and communicating effectively is critical when working with different stakeholders such as managers, product managers, C-Suite, data engineers and analysts. Most people think of data science as business work, but should be thought of as software engineering, sprints/scrums/kanbans. </li><li>Deploying a model in production is essential for handling failed products. But the big question becomes: Why do projects fail? And when it comes to deployment, is analytics easier to deploy. The last question is: How can we better manage the risk of failure?</li><li>Receive 40% off your purchase of a Manning Book with code <strong><em>podhumAIn19</em></strong></li></ul><p><br></p><p><u>On HumAIn:</u></p><ul><li>