This week's guest is Doug Eisenstein, an exceptionally clear and content-rich communicator. He fills us in on the complexity of engineering a coherent source of truth for financial models, integrating hundreds of data sources.
Topics covered in the episode include:
• A breakdown of the primary financial sectors and departments
• Why data source integration for finance is wildly complicated
• Specific data engineering approaches that resolve these issues including entity resolution, knowledge graph mapping and tri-temporality.
20 years ago, Doug founded the consulting firm, Advanti and they have since become a critical provider of solutions to complex data engineering problems faced by some of the world's largest banks and asset managers including Morgan Stanley, Bank of America, Citibank and State Street.
Listen or watch here.
Filtering by Category: Data Science
The Product Rule for Derivatives
Today's video is on the Product Rule, a relatively advanced Derivative Rule. Only a couple such rules remain and then we move onward to Automatic Differentiation with PyTorch and TensorFlow.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
machinelearning,datascience,calculus,mathematics,python
Algorithm Aversion
Exercises on Derivative Rules — Topic 56 of Machine Learning Foundations
Today's YouTube video uses five fun exercises to test your understanding of the derivative rules we’ve covered so far: the Constant Rule, Power Rule, Constant-Multiple Rule, and Sum Rule.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
If you’d happen to like a detailed walkthrough of the solutions to all the exercises in this video, you can check out my Udemy course called Mathematical Foundations of Machine Learning. See jonkrohn.com/udemy
Setting Yourself Apart in Data Science Interviews
For this week's guest episode, I interrogated Andrew Jones on his data science interview secrets. If you want to improve your interview performance — especially if you're in a data-related career — this episode's for you.
Andrew has held a number of senior data roles over the past decade, including at the tech giant Amazon. In those roles, Andrew interviewed hundreds upon hundreds of data scientists, leading him to create his Data Science Infinity educational program, a curriculum that provides you with the hard and soft skills you need to set yourself apart from other data scientists during the interview process.
Listen or watch here.
The Sum Rule for Derivatives
Thus far in this set of videos on Differentiation Rules, we’ve covered the Constant, Power, and Constant-Multiple rules. Today's video is on the Sum Rule. On Thursday, we'll have comprehension exercises on all four key rules!
The Constant Multiple Rule for Derivatives
Continuing my short series on Differentiation Rules, today’s video covers the Constant Multiple Rule. This rule is often used in conjunction with the Power Rule, which was covered in the preceding video, released on Monday.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
Data Community Content Creator Awards
I am surprised and utterly delighted to be recognized yesterday with the Data Community Content Creator Award for the "Machine Learning and AI" YouTube category. 🥳
From my perspective, my YouTube channel is still in its early days so while I did not anticipate formal recognition like this perhaps ever, I *certainly* did not so soon after launching the channel. This is a massive, galvanizing signal that I should continue pressing on with this nascent video-creation effort — I absolutely will!
First off, thank you to everyone who voted. This category was apparently one of the tightest races in this "Peoples' Choice"-style awards show, so truly your individual vote may have tipped the award in my favor.
Many thanks are due to Sangbin Lee and Maria Lee, who have edited, produced, branded, and marketed every single video on my channel since day one. My freely-available YouTube content would not exist without them. Thanks as well to Guillaume Rousseau, who recently joined us and dramatically accelerated how quickly we can publish perfectly-edited videos.
Finally, thanks to Harpreet Sahota and Kate Strachnyi who conceived of the DCCCA show and delivered it with the flair, fun, and precision that we'd expect from them!
The entire ceremony is on YouTube here. And a short recap post is here.
Performance Marketing Analytics
My guest this week is Kris Tait, who fills us in on how data and machine learning have transformed — and will continue to transform — marketing, enabling even small firms to effectively target customers and grow their revenue.
In this episode of the SuperDataScience show, we cover:
• What performance marketing is
• The rapidly shifting digital marketing ecosystem, as well as how data and ML can mitigate the risks associated with these changes
• The sweet spot for augmenting human marketers' skills with machines
• How any firm should define metrics to maximize return on marketing investment, thereby ensuring broader commercial success
• The most useful modern data science tools for global digital marketing
Kris is the managing director for the US at Croud - Performance Marketing Agency of the Year, an innovative marketing agency that is driven by data analytics and machine learning algorithms.
Listen or watch here.
The Power Rule for Derivatives
On Thursday, I published a video on the Constant Rule, the first video in a series on Differentiation Rules. Today, we continue the series with the Power Rule, arguably the most common and most important of all the rules.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
The Derivative of a Constant
This and the next several videos will provide you with clear and colorful examples of all of the most important differentiation rules. We kick these rules off with the Constant Rule.
The derivative rules are critical to machine learning as they allow us to find the derivatives of cost functions. These cost-function derivatives are concatenated into the "gradient" that we descend to allow ML models to learn.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
Knowledge Graphs
In this week's guest episode, wildly intelligent and meticulously communicative Maureen Teyssier, Ph.D. explains what Knowledge Graphs are, why they're so powerful, and how to grow a flourishing data science team.
In more detail, in today’s episode we cover:
• The theory and applications of Knowledge Graphs, a cool and powerful data type at the heart of much of Maureen’s work at Reonomy
• The data science techniques that Reonomy use to flow data through extremely high-volume pipelines, enabling them to efficiently apply models to their massive data sets
• What Maureen looks for in the data scientists that she hires and the tools and approaches she leverages in order to grow a highly effective data science team
• The differences between data scientists, data analysts, data engineers, and machine learning engineers.
• Maureen’s fascinating academic work in which she used gigantic supercomputers to simulate solar systems and galaxies
Maureen is Chief Data Scientist at Reonomy, a very well-funded New York start-up — they’ve raised over 100 million dollars — that is transforming the world of commercial real estate with data and data science. Prior to working in industry, Maureen was an academic working in the field of computational astrophysics; she obtained her PhD from Columbia University in the City of New York and then carried out research at Rutgers University in New Jersey.
Listen here.
Derivative Notation
In today's YouTube video, we detail all of the most common notation for derivatives. This lays the foundation for a fun, immediately forthcoming series of videos covering all of the major differentiation rules. Enjoy!
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
How Derivatives Arise from Limits
In today's video, we use hands-on code demos in Python to find the slopes of curves with the Delta Method. While finding these slopes, we derive together — from first principles — the most important Differential Calculus formula.
This video is part of a thematic segment of videos on Differentiation. In the forthcoming videos, we’ll cover derivative notation and a series of useful rules for differentiation.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
How to Thrive as an Early-Career Data Scientist
Getting started in data science? Today's episode is for you! Sidney Arcidiacono is absolutely crushing her first year in the field; we discuss the options for getting started in the field and top tips for early-career success.
Trained as a phlebotomist (blood-sample collection), Sidney was inspired by the potential for machine learning to revolutionize healthcare, so she jumped feet first into a full-time computer science degree at Make School, specializing in the data science track. From no familiarity with code or models just a year ago, Sidney's immersion has paid off: She's now fluent in the modern data science software stack and landed a summer data science internship at GreenLight Biosciences, Inc., an RNA-molecule therapeutics firm (like the Pfizer/BioNTech/Moderna vaccines).
Sidney is terrifically sharp and engaging; I think you'll enjoy hearing from her as much as I did during filming.
Watch or listen here.
The Delta Method
In today's video, we use a Python code demo to develop a working understanding of the Delta Method, a centuries-old technique that enables us to determine the slope of a curve.
This video is the first from a thematic segment of videos on Differentiation. In Thursday's video, we'll build on what we covered today to derive — and deeply understand — the most common, most important equation in differential calculus.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
Derivatives and Differentiation — Segment 2 of Subject 3, "Limits & Derivative
Today marks the beginning of a new thematic segment of videos in my ML Foundations series. This segment builds on the Limits content already covered to clearly illustrate how Differentiation works and how we find Derivatives.
Through a combination of color-coded equations, paper-and-pencil exercises, and hands-on Python code demos, the videos in this segment instill a deep understanding of how differentiation allows us to find derivatives.
More specifically, the videos cover:
• The Delta Method
• The Differentiation Equation
• Differentiation Notation
• Rules that enable us to quickly calculate the derivatives of a wide range of functions, including those found throughout machine learning
New videos are published every Monday and Thursday. The playlist for my Calculus for ML course is here.
More detail about my broader ML Foundations series and all of the associated open-source code is available in GitHub here.
The 20% of Analytics Driving 80% of ROI
Today’s episode is with freakin' David Langer, people!! (So obviously it's brilliant, witty, and full of laughs.) He fills us in on the most powerful 20% of analytics — the analytics that drive 80% of companies’ return on investment.
Publishing under his Dave on Data brand, Dave's YouTube channel is top-notch, with several videos that have over a million views (and the thumbnails are hilarious; check 'em out). He is an exceptionally accomplished data scientist and software engineer, including spending nearly a decade at Microsoft's Global HQ, where his titles included principal software architect, principal data scientist, and director of analytics.
Topics in the episode include:
Surprisingly powerful modeling approaches in spreadsheet tools like Excel
The SQL databases we'll need if the data sets we're working with are too big for spreadsheets
Why R programming is easy and should be our default language choice for moderate to advanced statistical analysis
How companies can maximize value from machine learning
Listen or watch here.
Exercises on Limits
Final YouTube video from my thematic segment on Limits out today! It's a handful of comprehension exercises. Starting Thursday, we'll begin releasing videos from a new Calculus segment, on derivatives and differentiation.
We release new videos from my "Calculus for Machine Learning" course on YouTube every Monday and Thursday. The playlist is here.
The Machine Learning House
In last week’s Five-Minute Friday, I discussed how, in the data science field, the learning never stops. But there’s one big counterpoint: The foundational subjects that underlie the field barely change at all, decade after decade.
These subjects — linear algebra, calculus, probability, statistics, data structures, and algorithms — build a strong foundation for your “Machine Learning House”. Today's Five-Minute Friday articulates my perspective that investing time in studying these foundational subjects will reap great dividends throughout your data science career.
You can listen or watch here.