Why I’m ending this show. Death, afterlife, Chinese philosophy, and what’s next? Daliana Liu ft. Tina Huang #92

I had a fun “funeral” of this show with Tina Huang, we talked about death, afterlife, and the various transitions as content creators. Get coached by Daliana:• DS/ML Career Accelerator• Personal Branding Accelerator• Join the newsletter with 20k readers to read my career stories ⁠here⁠.

➡️ Learn more about the DS Career Coaching Course.I went on Super Data Science with Jon Krohn to discuss how to accelerate your career in data science, why I paused the podcast, and how to navigate the industry's change.Join 20k subscribers to read more about career influence and personal branding. Subscribe here.

Ben Rogojan, aka, Seattle data guy is an ex-Meta data engineer turned data engineering consultant and YouTuber. He has 100k followers each on YouTube and LinkedIn. We talked about how he become an independent consultant and the current state of data engineering.- Ben’s LinkedIn: ⁠https://www.linkedin.com/in/benjaminrogojan/⁠- Daliana’s newsletter on career growth and personal branding: ⁠https://dalianaliu.kit.com/profile⁠⁠Hi! I'm Daliana Liu⁠ Join 20,000+ subscribers to read the career lessons from my 7-year experience at Amazon and my solo-founder journey.

Daliana interviewed 6 data scientists from her meetup in New York City. It's a unique episode where you get to hear the real frustrations of data scientists. We talked about struggles working in healthcare, finance, data quality and AI, how to advocate for yourself, and align with your managers. Subscribe to Daliana's newsletter on ⁠www.dalianaliu.com⁠ for more on data science and career. Daliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠ Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/

Most experimentations fail, Kristi Angel shares her expertise on scaling experimentation and avoiding common A/B testing pitfalls. Learn five things that can help boost test velocity, designing impactful experiments, and leveraging knowledge repos. (Chapters below) Kristi Angel’s LinkedIn: ⁠https://www.linkedin.com/in/kristiangel/ Subscribe to Daliana's newsletter on ⁠www.dalianaliu.com⁠ for more on data science and career. Daliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠ Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/⁠ (00:00:00) Intro (00:01:26) Why do most experimentations fail? (00:07:05) Mistakes in choosing metrics (00:10:05) Is revenue a good metric? (00:13:18) Split metrics in three ways (00:15:10) Daliana's story with too many category breakdowns (00:16:59) What makes the best data science team? (00:19:24) Data scientist work in silo vs in a data science team (00:21:15) Building a knowledge center (00:23:40) Example of knowledge center; nuance of experimentations (00:26:09) How many metrics and variants? (00:30:56) How to reduce noise - CUPED (00:33:01) Future of A/B testing (00:38:33) Q&A: Low statistical power

Julia Silge is an engineering manager at Posit PBC, formerly know as R-studio, where she leads a team of developers building open source software MLOps. Before Posit, she finished a PhD in astrophysics, worked for several years in the nonprofit space, and was a data scientist at Stack Overflow where some of her most public work involved the annual developer survey. We talked about MLOps tools, challenges in survey data, text analysis, and balancing her interests in data science and engineering. Subscribe to Daliana's newsletter on ⁠www.dalianaliu.com⁠ for more on data science and career. Daliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠ Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/⁠ (00:00:00) Introduction (00:00:56) Getting into data science (00:04:50) Transition from data centers to engineering manager (00:14:04) Common challenges in tool development (00:17:38) Challenges with survey data (00:26:47) Engineering skills for data scientists (00:28:59) Balancing roles (00:34:49) Developing skills in Exploratory Data Analysis (EDA) (00:39:19) Python vs. R for data analysis (00:44:40) Exciting aspects in career and personal life

Wes McKinney is the co-creator of pandas library and he is the cofounder of Voltron data. Currently he is a principal Architect at Posit and an investor in data systems. Daliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠ Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/⁠ Wes' LinkedIn: https://www.linkedin.com/in/wesmckinn/ (00:00:00) Introduction (00:00:44) How Pandas Started (00:06:40) Voltron Data (00:10:03) Benefits of Easy-to-Use Data Tools (00:13:20) The Rise of New Data Tools (00:18:07) Choosing Tools: Vertical or Flexible? (00:23:01) Big Models and Data Tools (00:29:29) Challenges in Building a Product (00:31:28) Becoming a Top Architect (00:34:55) Missed Aspects of Previous Roles (00:39:04) A Busy Week: Advising, Designing, Investing (00:43:42) Improving Open Source (00:45:24) How to Decide What to Work On (00:46:28) What he’s learning now (00:47:56) Excitement in Career and Life (00:48:29) Using ChatGPT for Learning (00:50:27) Future Impact Goals

Christopher Fricker is a senior director in analytics and BI at Renaissance Learning. He started his career in finance and later became a data science consultant working with Meta, Netflix, and pre-IPO tech companies doing analytics. We talked about the mental models that helped him grow from a finance analyst to an analytics leader. Subscribe to Daliana's newsletter on ⁠www.dalianaliu.com⁠ for more on data science and career. Chris’ LinkedIn: https://www.linkedin.com/in/christopherfricker/ Daliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠ Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/⁠ (00:00:00) Introduction (00:01:46) How to get promoted quickly (00:08:40) Power vs authority (00:11:21) First principal thinking (00:32:34) ROI of a data team (00:40:29) How to be persuasive (00:54:52) All Data is wrong (00:56:22) How he audits the data (01:00:52) How to make someone help you at work

I interviewed Geoffery Angus, ML team lead @Predibase to talk about why adapter-based training is a game changer. We started with an overview of fine-tuning and then discussed five reasons why adapters are the future of LLMs. Later we also shared a demo and answered questions from the live audience. Try fine-tuning for free: https://pbase.ai/GetStarted Geoffrey’s LinkedIn:https://www.linkedin.com/in/geoffreyangus Daliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠ Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/⁠ Daliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠ Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/ Geoffrey’s LinkedIn: https://www.linkedin.com/in/geoffreyangus Try finetuning for free: https://pbase.ai/GetStarted (00:00:00) Intro (00:01:19) What is Fine-tuning? (00:08:18) Utilizing Adapters for Finetuning Enhancement (00:09:50) 5 reasons why adapters are the future of LLMs (00:26:34) Common Mistakes in Adapters Usage (00:28:34) Training Your Own Adapter (00:32:23) Behind the Scenes of the Adapter Training Process (00:37:51) Config File Guidance for Fine-Tuning (00:39:41) Debugging Strategies for Suboptimal Fine-Tuning Results (00:42:23) User Queries: Creating a LoRa Adapter and Future Support (00:51:06) Key Takeaways and Recap

Jay Feng created a viral project using Seattle crime data and later got into data science. He later founded "Interview Query" helping data scientists get jobs. We'll talk about how he landed his data science job through his blog, and his journey from data scientist to founder. Subscribe to Daliana's newsletter on ⁠www.dalianaliu.com⁠ for more on data science and career. Daliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠ Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/⁠ Jay Feng's LinkedIn: ⁠https://www.linkedin.com/in/jay-feng-ab66b049/⁠ Jay Feng's YouTube: ⁠https://www.youtube.com/c/DataScienceJay⁠ (00:00:00) Introduction (00:01:11) From engineer to data scientist (00:03:10) Got a job through a project (00:05:35) Daliana's portfolio project with Zillow (00:09:13) From data scientist to entreprenuer (00:13:19) "Tinder" for job (00:15:01) How he chose companies to work for (00:15:56) Why he became an entreprenuer (00:17:37) How many hours does he work (00:18:54) Challenges when building "interview query" (00:20:18) Speed vs scale (00:22:11) Growth hacks he used (00:24:22) YouTube vs newsletter (00:27:21) Lessons he learned as a CEO (00:29:16) How to grow from tech employee to founder (00:31:59) How he defines success (00:34:38) If you have a business idea for Jay