Chapter 4: Building a Portfolio
55 min

Perhaps the most common piece of advice for aspiring data scientists is to make a project portfolio. Despite this, so few data scientists do so! In this episode, we discuss what exactly a portfolio is, the benefits, and the common reasons people don’t do it and how to overcome them. Spoiler: it's just as much psychological as it is about time and skills.

The Artists of Data Science
The Artists of Data Science
Harpreet Sahota
Beware of Black Friday Deals | Jeff Kreisler
Jeff Kreisler uses behavioral science, real life, and humor to understand, explain, and change the world. He’s pretty much your friendly, typical Princeton educated lawyer turned award-winning comedian, best-selling author, and champion for behavioral economics . WHAT YOU'LL LEARN What money is, why we won't make good money decisions, the common cognitive biases we have related to money, how to not let the retailers dupe you during this holiday shopping season, and more! QUOTES [11:31] "Traditional economics says everything's cost benefit analysis. Reality is that that's not how it works. We are busy people. We have a lot of stress and we don't always make the rational choice." [16:47] "The there's no clear right or wrong choice with money that we all always know. There's always some uncertainty. And when uncertainty is in any decision, that gap gets filled by the emotional needs that we have. The need to feel like we're making the right choice. The need to feel like we've done the right thing. The need to feel good. And that's when we can be prone to make irrational decisions, because we go by our feelings and emotions. " [18:08] "I feel like marrying the data science with the people science is going to be like an incredible combination. To not just know where they're going and what buttons to push, but why why are people doing this?" [20:30] "When we pay for something, it stimulates the same region of our brain as physical pain. And that pain should serve a purpose. It should make us stop and think about what we're doing." [34:01] "You can't pay your rent with the money you save shopping." FIND JEFF ONLINE Website: LinkedIn: SHOW NOTES [00:01:32] Introduction for our guest [00:02:42] We talk about Jeff’s journey [00:08:06] How Jeff teamed up with Dan Ariely [00:13:54] The concept of money [00:18:46] Mental shortcuts and money [00:22:24] What would you do with $30,000 [00:25:24] Relativity, money, and why we suck at comparing things [00:29:38] System 1 and System 2 [00:31:33] Don’t fall for the sale price! [00:33:22] How retailers will trick you with discounts [00:35:52] The anchoring effect [00:41:16] Mental accounting [00:45:06] Extreme examples of mental accounting [00:46:38] Do we have the same cognitive biases for other people's money as we do for our own? [00:48:39] Herding and self-herding [00:52:28] Jeff shares his top three favorite tips for fighting our flawed financial thinking [00:56:37] Some other cognitive biases to watch out for this holiday shopping season [00:58:27] It's one hundred years in the future. What do you want to be remembered for? [00:59:23] The random round Special Guest: Jeff Kreisler.
1 hr 5 min
Machine Learning Street Talk
Machine Learning Street Talk
Machine Learning Street Talk
#030 Multi-Armed Bandits and Pure-Exploration (Wouter M. Koolen)
This week Dr. Tim Scarfe, Dr. Keith Duggar and Yannic Kilcher discuss multi-arm bandits and pure exploration with Dr. Wouter M. Koolen, Senior Researcher, Machine Learning group, Centrum Wiskunde & Informatica. Wouter specialises in machine learning theory, game theory, information theory, statistics and optimisation. Wouter is currently interested in pure exploration in multi-armed bandit models, game tree search, and accelerated learning in sequential decision problems. His research has been cited 1000 times, and he has been published in NeurIPS, the number 1 ML conference 14 times as well as lots of other exciting publications. Today we are going to talk about two of the most studied settings in control, decision theory, and learning in unknown environment which are the multi-armed bandit (MAB) and reinforcement learning (RL) approaches - when can an agent stop learning and start exploiting using the knowledge it obtained - which strategy leads to minimal learning time 00:00:00 What are multi-arm bandits/show trailer 00:12:55 Show introduction 00:15:50 Bandits  00:18:58 Taxonomy of decision framework approaches  00:25:46 Exploration vs Exploitation  00:31:43 the sharp divide between modes  00:34:12 bandit measures of success  00:36:44 connections to reinforcement learning  00:44:00 when to apply pure exploration in games  00:45:54 bandit lower bounds, a pure exploration renaissance  00:50:21 pure exploration compiler dreams  00:51:56 what would the PX-compiler DSL look like  00:57:13 the long arms of the bandit  01:00:21 causal models behind the curtain of arms  01:02:43 adversarial bandits, arms trying to beat you  01:05:12 bandits as an optimization problem  01:11:39 asymptotic optimality vs practical performance  01:15:38 pitfalls hiding under asymptotic cover  01:18:50 adding features to bandits  01:27:24 moderate confidence regimes   01:30:33 algorithms choice is highly sensitive to bounds  01:46:09 Post script: Keith interesting piece on n quantum #machinelearning
1 hr 48 min
Demetrios Brinkmann
Introducing Data Downtime: From Firefighting to Winning // Barr Moses // MLOps Coffee Sessions #19
Coffee Sessions #19 with Barr Moses of Monte Carlo, Introducing Data Downtime: How to Prevent Broken Data Pipelines with Observability co-hosted by Vishnu Rachakonda //Bio Barr Moses is CEO & Co-Founder of Monte Carlo, a data observability company backed by Accel and other top Silicon Valley investors. Previously, she was VP Customer Operations at customer success company Gainsight, where she helped scale the company 10x in revenue and among other functions, built the data/analytics team. Prior to that, she was a management consultant at Bain & Company and a research assistant at the Statistics Department at Stanford. She also served in the Israeli Air Force as a commander of an intelligence data analyst unit. Barr graduated from Stanford with a B.Sc. in Mathematical and Computational Science. //Talk Takeaways As companies become increasingly data-driven, the technologies underlying these rich insights have grown more and more nuanced and complex. While our ability to collect, store, aggregate, and visualize this data has largely kept up with the needs of modern data teams (think: domain-oriented data meshes, cloud warehouses, data visualization tools, and data modelling solutions), the mechanics behind data quality and integrity has lagged. To keep pace with data’s clock speed of innovation, data engineers need to invest not only in the latest modelling and analytics tools but also technologies that can increase data accuracy and prevent broken pipelines. The solution? Data observability, the next frontier of data engineering and a pillar of the emerging Data Reliability category and the fix for eliminating data downtime. During this talk, listeners will learn about: * The rise (and threat) of data downtime * The relationship between DevOps Observability and Data Observability * Data Observability and it's five key pillars * How the best data teams are leveraging Data Observability to prevent broken pipelines //About Monte Carlo As businesses increasingly rely on data to drive better decision making, it’s mission-critical that this data is accurate and reliable. Billed by Forbes as the New Relic for data teams and backed by Accel and GGV, Monte Carlo solves the costly problem of broken data through their fully automated, end-to-end data reliability platform. Data teams spend north of 30% of their time tackling data quality issues, distracting data engineers, data scientists, and data analysts from working on revenue-generating projects. Providing full coverage of your data stack – all the way from data lake and warehouse to analytics dashboard – Monte Carlo’s platform empowers companies such as Eventbrite, Compass, Vimeo, and other enterprises to trust their data, saving time and money and unlocking the potential of data. //Other links you can check Barr on Learn more about Monte Carlo: What is data downtime?   What is data observability? How data observability prevents broken data pipelines:
1 hr 1 min
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