Superdatascience

Sam Hinton joins us to discuss his work since assisting COVID-19 data pipelines, now working in renewable energy and applications of ML and MLOps for the industry. In this episode you will learn: Catching up with Sam [3:05] Updates on the COVID-19 data pipelines [7:07] Sam’s current work at Arenko [10:41] Sam’s stint on Survivor, PhD, and his software engineering background [16:32] Machine learning in renewable energy [35:23] Sam’s day-to-day tools [49:33] How can listeners utilize MLOps [53:08] Sam’s forthcoming novel [59:05] Additional materials: www.superdatascience.com/461

In this episode, I talk about the ancient history of algebra, an important component of data science today. Additional materials: www.superdatascience.com/460

Vince Petaccio joins us to discuss how he sees data science, ML, and AI making positive impacts in the fight against climate change. In this episode you will learn: Where in the world is Vince? [2:08] Vince’s interest in climate science [4:33] The Citizen’s Climate Lobby [9:12] Where data science comes in [13:28] Risks of relying on tools [31:54] How can you make an impact? [37:28] Additional materials: www.superdatascience.com/459

In this week’s episode, I take you behind the scenes of our video tutorial productions to see what goes into making our tutorials. Additional materials: www.superdatascience.com/458

Harpreet Sahota joins us to discuss his data science mentorship work outside his day job and how you can land your dream job. In this episode you will learn: Harpreet’s current life and location [2:25] Data Community Content Creator Awards [8:37] The Artists of Data Science Podcast [14:46] Data Science Dream Job [24:18] Harpreet’s day job at Price Industries [30:48] Coming in data science from a non-data background [40:55] Tools and skills to know [47:57] Additional materials: www.superdatascience.com/457

In this week’s episode, I talk about one of my favorite time management techniques: the Pomodoro technique. Additional materials: www.superdatascience.com/456

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