Superdatascience

Jared Lander joins us to discuss his work as an R meetup organizer, the upcoming virtual R Conference, and his work as a consultant for a variety of companies from metal workers to professional football teams. In this episode you will learn: Jared’s R meetups and our professional history [3:27] NYHackR [6:42] The R Conference [13:25] R for Everyone [18:55] Lander Analytics [22:10] Job openings at Lander Analytics [25:04] R vs. Python [29:15] The importance of pizza in Jared’s life [32:19] Additional materials: www.superdatascience.com/501

In this very special episode, we delve into a live yoga Nidra practice with Jes Allen and go over how you can open up to consciousness through yoga practice. In this episode you will learn: [3:40] What Yoga means [10:00] Jes’s current work as a yoga practitioner [22:31] How to find Jes online [27:09] The Yoga Nidra practice [54:50] Coming out of the practice Additional materials: www.superdatascience.com/500

Barr Moses joins us to discuss the importance of data reliability for pipelines and how companies can achieve data mesh. In this episode you will learn: Data meshes [4:25] Self-serve data reliability [15:36] How Monte Carlo helps data up time [21:13] How to build an effective data science team [26:50] LinkedIn Q&A [31:50] Additional materials: www.superdatascience.com/499

In this episode, I dive into a reoccurring pattern I’ve noticed where beginners, myself included, think they’re more skilled and experienced than they really are. Additional materials: www.superdatascience.com/498

Benjamin Todd joins us to discuss his work helping professionals maximize their career capital, the top skills to learn across professions, and more. In this episode you will learn: How Benjamin helped me become a data scientist [6:56] How did 80,000 Hours come about? [9:39] The impact of 80,000 Hours [14:46] Funding [17:23] Where does the name come from? [23:32] What kind of advice does Benjamin give to people? [25:21] How data scientists can make an impact [42:04] How can someone strategize about their career? [1:02:53] Top skills that everyone should learn [1:05:49] Additional materials: www.superdatascience.com/497

In this episode, you’ll enjoy a fictional narrative I’ve titled “2040: A Brain-Computer Interface Story”. Additional materials: www.superdatascience.com/496

Greg Coquillo joins us to discuss his work on ROI for startups and the best ways to make the most of your company’s AI investment. In this episode you will learn: Our connection through Harpreet’s happy hours and DSGO [4:48] Greg’s content on LinkedIn [6:40] The scope of Greg’s work [9:25] Making the most out of AI [16:05] LinkedIn Q&A [20:00] Quantum machine learning [32:06] Additional materials: www.superdatascience.com/495

In this episode, I talk about an interesting thought experiment that helps you appreciate your existence. Additional materials: www.superdatascience.com/494

Anjali Shrivastava joins us to discuss her data science degree and her content creation efforts to bring data science to the people. In this episode you will learn: Anjali’s studies [2:00] Anjali’s YouTube channel [11:57] The content creation process [17:58] Yoga during the pandemic [21:34] Anjali as a writer [24:38] Anjali’s dual degrees [31:28] Anjali’s previous data science roles [43:04] Anjali’s first full-time data job [51:12] Anjali’s hopes for the future [55:29] Additional materials: www.superdatascience.com/493

In this episode, I discuss the changing child mortality rate as evidence of how much better the world is and how much better it could be. Additional materials: www.superdatascience.com/492

Veerle van Leemput joins us to make the case for why you should be using R for production. In this episode you will learn: Our shared powerlifting passion [2:47] The stigma of using R [12:02] What does Analytic Health do? [13:55] How Analytic Health uses R [19:08] Tidyverse [34:44] Tools for API creation [37:09] Additional materials: www.superdatascience.com/491

In this episode, I discuss why you should avoid the visually pleasing but flawed pie chart. Additional materials: www.superdatascience.com/490

Vin Vashishta joins us to discuss his AI consulting work and his philosophy on AI strategy for monetization. In this episode you will learn: V-Squared [4:59] Vin’s online content [17:18] Low-code/no-code in data science [25:33] Top five gap skills [35:19] Data sets for insights on consumers and targeting [40:26] Are there socially beneficial data science and machine learning applications? [43:16] The most difficult data science problem Vin ever faced [50:39] Additional materials: www.superdatascience.com/489

In this episode, I discuss the simple and cheap ways you can buy yourself more time during the day. Additional materials: www.superdatascience.com/488

Susan Walsh joins us to discuss the importance of data cleaning and normalization and how clean procurement data can save companies money. In this episode you will learn: Susan’s “COAT” system [7:16] The Classification Guru [15:39] Case studies [22:46] Susan’s book [30:26] Additional materials: www.superdatascience.com/487

In this episode, I go over the world history of calculus and how we still use these techniques today. Additional materials: www.superdatascience.com/486

Doug Eisenstein joins us for a great and in-depth conversation on data engineering in the financial sector. In this episode you will learn: • The founding of Advanti [4:37] • Aristos and solution products [16:45] • The kinds of financial industries and how Doug helps [26:25] • Entity Extraction [34:27] • Temporality data [44:27] • How to work with Doug [58:19] Additional materials: www.superdatascience.com/485

In this episode, I discuss interesting research on why humans are so quick to lose faith in algorithms. Additional materials: www.superdatascience.com/484

Andrew Jones joins us to discuss data science interviews and how you can maximize your chances on interview time, resume, and more! In this episode you will learn: Data Science Infinity [5:40] “The Essential AI and Data Science Handbook for Recruitment” [17:40] How can aspiring data scientists set themselves apart? [21:30] What skillset should data scientists have? [34:36] Should data science be trying to be data engineers? [41:14] How can organizations ensure data science projects are a success? [50:50] Additional materials: www.superdatascience.com/483

In this episode, I talk about the advantages of using a continuous calendar. Additional materials: www.superdatascience.com/482