803: How to Thrive in Your (Data Science) Career, with Daliana Liu

Daliana Liu is a big name in data science teaching, and she has always been generous in sharing everything she knows about getting a job in data science. In this episode, she continues to extend her generosity, helping listeners define their approach to achieving a fulfilling career in data science and tech. This episode is brought to you by AWS Inferentia and AWS Trainium, by Babbel, the science-backed language-learning platform, and by Gurobi, the Decision Intelligence Leader. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: • Common career challenges for data scientists [34:57] • Advice for people who don’t know where to go in their career [48:05] • How to build resilience and protect against Imposter Syndrome [1:06:23] • Skills that data scientists should develop today [1:39:17] • The future of the data science and AI job market [1:46:55] Additional materials: www.superdatascience.com/803

Om Podcasten

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact. Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy. We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.