Superdatascience

Horace Wu joins us to discuss his work on Syntheia, a unique product that helps sift through massive amounts of legal data to augment the capacities and function of law firms. In this episode you will learn: Horace’s life and work in New York City [5:00] Syntheia and Horace’s role there [6:25] Horace’s background [12:07] Nearmap [16:35] Syntheia NLP use cases [21:46] Design, coding, and the team [34:19] What skills does one need for this field? [41:41] What would Horace do differently and what is he excited for? [46:15] Additional materials: www.superdatascience.com/455

In this episode, I continue my discussion about the quick-paced growth of technology and how it impacts different fields. Additional materials: www.superdatascience.com/454

Stephen Welch joins to go over his year-end 2020 list of 10 important questions and pain points that machine learning can improve. In this episode you will learn: Welch Labs on YouTube [4:54] What Stephen’s been up to [7:56] Stephen’s 2020 year-end blog post [10:11] Stephen’s reflections on 10 areas worth focusing on [16:25] Additional materials: www.superdatascience.com/453

In this week’s episode, I discuss how technology propelled the recruitment industry forward and continues to do so today. Additional materials: www.superdatascience.com/452

Dan Shiebler joins us to discuss his category theory Ph.D. program, his full-time job at Twitter, and how the two crossover and combine in his overall data work. In this episode you will learn: Dan’s neuroscience undergrad and MATLAB [4:12] Dan’s Ph.D. timeline and research [14:01] How to start a Ph.D. while working full time [22:45] Dan’s work at TrueMotion and label data [30:39] Dan’s title and role at Twitter [39:15] Specific projects at Twitter [44:09] What skills someone should bring to a Twitter job interview [52:06] What machine learning approaches will be important in the future? [1:00:38] Additional materials: www.superdatascience.com/451

This week, Jon talks with Steve Fazzari about the physical and emotional benefits of practicing Yoga Nidra. Additional materials: www.superdatascience.com/450

Ayodele Odubela joins us to discuss fairness in AI and how we can work towards a more equitable and transparent world of data science and machine learning. In this episode you will learn: Comet ML [3:22] What is a data science evangelist? [7:08] FullyConnected [12:04] Imposter Syndrome and Ayodele’s book [15:57] What Ayodele wished she learned from grad school [20:25] Uncovering Bias in Machine Learning [27:00] Where can we affect this positive change in fairness? [31:08] The potential for a rosy future [49:20] Ayodele’s LinkedIn Learning course [52:24] Additional materials: www.superdatascience.com/449

This week, I answer your questions about how to take yourself from data science practitioner to data science leader. Additional materials: www.superdatascience.com/448

Michael Segala joins us to discuss how machine learning can provide creative and novel solutions to longstanding problems in both the private and public sectors. In this episode you will learn: SFL Scientific [4:20] SFL’s example work [10:55] Public sector vs private sector work [20:28] Michael’s day-to-day [30:18] What is Michael looking for in the people he hires? [33:38] Michael’s career journey [41:39] What is Michael excited about for the future? [48:38] Additional materials: www.superdatascience.com/447

This week I answer your questions about machine learning and how to educate yourself further in the field. Additional materials: www.superdatascience.com/446

Sinan Ozdemir joins us to share his work in conversational AI and what it takes to keep chatbots up to date and functional in an ever-changing world. In this episode you will learn: Kylie.ai under Directly [4:51] Sinan’s day-to-day work and tools [10:45] Use cases [18:27] AutoML’s role in these processes [21:55] What hard or soft skills are needed for this work? [29:32] Sinan’s background in teaching [34:58] Sinan’s history in pure math and applied math [39:44] Sinan’s math tattoos [43:48] Additional materials: www.superdatascience.com/445

In today’s episode, I answer your questions on how to best future-proof your data science career in AI, AutoML, and model interpretability. Additional materials: www.superdatascience.com/444

Jeff Wald joins us to discuss his book and the research he has done into the data and trends around the job market, the decline of the 9-5 office job, and more. In this episode you will learn: The Birthday Rules [3:51] A history of work [7:41] The myth of the lifetime contract [12:15] What the data says about now [21:02] On-demand labor market [25:34] Remote work [32:09] What role will automation play? [46:27] Future of employment from the study lens [48:30] Additional materials: www.superdatascience.com/443

In today’s episode, I discuss how focusing on process and habit building can provide more for you and your professional progress than simply chasing a goal. Additional materials: www.superdatascience.com/442

Kate Strachnyi joins us to discuss her work in data visualization education from conferences to published books as well as her tips for visualization best practices. In this episode you will learn: What does Kate do (from her children’s perspective) [1:56] What kind of tools does Kate employ? [5:19] Kate’s day-to-day [13:03] DATAcated Conference [16:03] How do you amass a big LinkedIn following? [20:39] Kate’s four published books [29:55] The guidelines to follow to succeed in this field [37:00] What’s next for Kate? [41:24] Additional materials: www.superdatascience.com/441

In this episode, I continue my discussion on the leaps we’re making towards AGI, by looking at MuZero. Additional materials: www.superdatascience.com/440

Deblina Bhattacharjee joins us to talk about her amazing work in computer vision and give advice for getting into and excelling in the field. In this episode you will learn: Deblina’s master’s program work [4:03] Deblina’s computer vision research and Ph.D. [11:46] Deblina’s drumming hobby [20:18] The daily work [24:40] What key skills do you need as a data scientist? [33:21] How can a data scientist prepare for the future? [37:03] How does Deblina tackle time management? [40:24] Additional materials: www.superdatascience.com/439

In this episode, I discuss DeepMind’s latest breakthrough towards AGI and the stepping stones that got them there. Additional materials: www.superdatascience.com/438

Claudia Perlich joins us to discuss her work at one of the world’s largest hedge funds and how she got to work there, as well as her history of winning data science competitions. In this episode you will learn: Life and work during the pandemic [2:23] Claudia’s history with horses and riding [8:28] Claudia’s work at Two Sigma [12:00] Claudia’s role on a daily basis [20:51] Tools of the trade [30:27] What Claudia looks for when hiring [36:37] What skills do future hires need? [40:32] Claudia’s history with data science competitions [48:22] Why work in finance and at Two Sigma? [1:00:19] Additional materials: www.superdatascience.com/437

In this episode, I continue my discussion on daily mindfulness practice and how to form a growing habit in it. Additional materials: www.superdatascience.com/436