Today, Mariya Sha — host of the wildly popular "Python Simplified" YouTube channel (140k subscribers!) — taps her breadth of A.I. expertise to provide a fun and fascinating finale to SuperDataScience guest episodes for 2022.
Mariya:
• Is the mind behind the "Python Simplified" YouTube channel that makes advanced concepts (e.g., ML, neural nets) simple to understand.
• Her videos cover Python-related topics as diverse as data science, web scraping, automation, deep learning, GUI development, and OOP.
• Is renowned for taking complex concepts such as gradient descent or unsupervised learning and explaining them in a straightforward manner that leverages hands-on, real-life examples.
• Is pursuing a bachelor's in Computer Science (with a specialization in A.I. and Machine Learning) from the University of London.
Today’s episode should appeal to anyone who’s interested in or involved with data science, machine learning, or A.I.
In this episode, Mariya details:
• How the incredible potential of ML in our lifetimes inspired her to shift her focus from web-development languages like JavaScript to Python.
• Why automation and web scraping are critical skills for data scientists.
• How to make learning any apparently complex data science concept straightforward to comprehend.
• Her favorite Python libraries and software tools.
• One rarely-mentioned topic that every data scientist would benefit from.
• The pros and cons of pursuing a 100% remote degree in computer science.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Filtering by Category: SuperDataScience
How to Influence Others with Your Data
If you ever use data to make decisions or to persuade those around you to make data-driven decisions, today’s episode is jam-packed with relevant, practical tips from data presentation guru Ann K. Emery.
Ann:
• Is an internationally-acclaimed speaker who delivers 100+ keynotes, workshops, and webinars each year to enable people to share data-driven insights more effectively.
• She has consulted on data visualization, data reporting, and data presentation with over 200 organizations — the likes of the United Nations, the US Centers for Disease Control, and Harvard University.
• She holds a BA in Psychology and Spanish from the University of Virginia and a Masters in Educational Psychology Evaluation, Assessment, and Testing from George Mason University.
I rarely say that everyone should listen to an episode, but this is one of those rare cases.
In this episode, Ann details:
• What data storytelling is.
• Best practices for data visualization.
• Surprising tricks you can pull off with spreadsheet software.
• How to report on data effectively.
• Her top tips for presenting data in a slideshow.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The Equality Machine
Many recent books and articles spread fear about data collection and A.I. Today's guest, Prof. Orly Lobel, offers the antidote with her book "The Equality Machine" — an optimistic take on the future of data science.
Liquid Neural Networks
Liquid Neural Networks are a new, biology-inspired deep learning approach that could be transformative. I think they're super cool and Adrian Kosowski, PhD introduced them to me for today's Five-Minute Friday episode.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Data Analytics Career Orientation
Considering a Data Analytics career? Today's episode with YouTube icon Luke Barousse (273k subscribers) will be particularly appealing to you, but the terrifically interesting guest makes for an episode that anyone will love.
Luke:
• Is a full-time YouTuber, creating highly educational — but nevertheless hilarious — videos focused on Data Analytics.
• Previously worked as a Lead Data Analyst and Data Engineer at BASF.
• Worked for seven years in the US Navy on nuclear-powered submarines.
• Holds a degree in mechanical engineering, a graduate qualification in nuclear engineering, and an MBA in business analytics.
In this episode, Luke details:
• The must-have skills for entry-level data analyst roles.
• The data analyst skills mistakenly and erroneously pursued by many folks considering the career.
• How his submariner experience prepared him well for a data career.
• His favorite tools for creating interactive data dashboards.
• His favorite scraping libraries for collecting data from the web.
• The skills to learn now to be prepared for the data careers of the future.
• The benefits of CrossFit beyond just the fitness improvements.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Resilient Machine Learning
Machine learning is often fragile in production. For today's Five-Minute Friday episode, Dr. Dan Shiebler details how we can make ML more resilient.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Software for Efficient Data Science
In today's episode, Dr. Jodie Burchell details a broad range of tools for working efficiently with data, including data cleaning, reproducibility, visualization, and natural language processing.
Jodie:
• Is the Data Science Developer Advocate for JetBrains, the developer-tools company behind PyCharm (one of the most widely-used Python IDEs) and DataLore (their new cloud platform for collaborative data science).
• Previously was Data Scientist or Lead Data Scientist at several tech companies, developing specializations in search, recommender systems, and NLP.
• Co-authored two books on data visualization libraries: "The Hitchhiker's Guide to ggplot2" and "The Hitchhiker's Guide to Plotnine".
• Prior to entering industry, was a postdoctoral fellow in biostatistics at the University of Melbourne.
• Holds a PhD in Psychology from the Australian National University.
Today’s episode is primarily intended for a technical audience as it's packed with practical tips and software for data scientists.
In this episode, Jodie details:
• What a data science developer advocate is and why you might want to consider it as a career option.
• How to work effectively, efficiently, and confidently with real-world data.
• Her favorite Python libraries, such as ones for data viz and NLP.
• How to have reproducible data science workflows.
• The subject she would have majored in if she could go back in time.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The Critical Human Element of Successful A.I. Deployments
For today's episode, I sat down with the prolific data-science instructor, author and practitioner Keith McCormick to discuss how critical user considerations are for developing a successful A.I. application.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
AutoML: Automated Machine Learning
AutoML with Erin LeDell — it rhymes! In today's episode, H2O.ai's Chief ML Scientist guides us through what Automated Machine Learning is and why it's an advantageous technique for data scientists to adopt.
Dr. LeDell:
• Has been working at H2O.ai — the cloud A.I. firm that has raised over $250m in venture capital and is renowned for its open-source AutoML library — for eight years.
• Founded (WiMLDS) Women in Machine Learning & Data Science (100+ chapters worldwide).
• Co-founded R-Ladies Global, a community for genders currently underrepresented amongst R users.
• Is celebrated for her talks at leading A.I. conferences.
• Previously was Principal Data Scientist at two acquired A.I. startups.
• Holds a Ph.D. from the Berkeley focused on ML and computational stats.
Today’s episode is relatively technical so will primarily appeal to technical listeners, but it would also provide context to anyone who’s interested to understand how key aspects of data science work are becoming increasingly automated.
In this episode, Erin details:
• What AutoML — automated machine learning — is and why it’s an advantageous technique for data scientists to adopt.
• How the open-source H2O AutoML platform works.
• What the “No Free Lunch Theorem” is.
• What Admissible Machine Learning is and how it can reduce the biases present in many data science models.
• The new software tools she’s most excited about.
• How data scientists can prepare for the increasingly automated data science field of the future.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Subword Tokenization with Byte-Pair Encoding
When working with written natural language data as we do with many natural language processing models, a step we typically carry out while preprocessing the data is tokenization. In a nutshell, tokenization is the conversion of a long string of characters into smaller units that we call tokens.
Read MoreAnalyzing Blockchain Data and Cryptocurrencies
As real-time, publicly-available ledgers of transactions, blockchains provide exciting new data analytics opportunities. Kimberly Grauer leads us through the tools and approaches for blockchain analytics.
Kim:
• Is Director of Research at Chainalysis Inc., the world’s leading crypto analytics firm.
• Previously worked in an economic research and analysis group for NYC.
• Holds a Masters in Political Theory from the University of Oxford, a Master of Public Administration from the London School of Economics, and she completed the General Assembly Data Science bootcamp.
Today’s episode will appeal primarily to folks who are interested in blockchains and cryptocurrencies, particularly those keen to perform data analysis on blockchain data.
In this episode, Kim details:
• The unique real-time economic-data analytics opportunities that blockchains provide.
• Examples of her own research on blockchain data, such as analyses of illegal activity and global crypto adoption.
• The tools and approaches she uses daily to analyze and report on blockchain data.
• Where the evolutions of crypto, blockchains, and data science are going together.
• Why a data science bootcamp could be exactly the right thing for you if you’re looking to break into the field.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Imagen Video: Incredible Text-to-Video Generation
For today’s Five-Minute Friday episode, it’s my pleasure to introduce you to the Imagen Video model published upon just a few weeks ago by researchers from Google.
Read MoreData Analyst, Data Scientist, and Data Engineer Career Paths
Keen to become a Data Analyst? Get promoted to Sr Data Analyst? Or explore Data Engineer/Scientist options? Shashank, a YouTube expert on these questions (>100k subscribers!) tackles them in today's episode.
Shashank:
• Has an exceptional YouTube channel focused on helping people break into a data analyst career.
• Works as a Senior Data Engineer at digital sports platform Fanatics, Inc.
• Was previously Data Analyst at luxury retailer Nordstrom and other firms.
• Holds a degree in chemistry from Emory University in Atlanta.
Today’s episode will appeal primarily to folks who are interested in becoming a data analyst, or who are interested in transitioning from a data analyst role into a data science or data engineering role.
In this episode, Shashank details:
• How you can land an entry-level data analyst role in just a few weeks, regardless of your educational and professional background.
• The hard and soft skills you need to progress from a junior data analyst to a senior data analyst position.
• What it takes to transition from data analyst to a typically more lucrative role as a data scientist or data engineer.
• His favorite resources for learning the essential skills for data scientists.
What he looks for when he’s interviewing candidates.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Burnout: Causes and Solutions
What really is Burnout? What causes it? And how can you prevent or treat it? Prof. Christina Maslach — world-leading researcher and author on Burnout — joins me for today's episode to unpack these questions.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Blockchains and Cryptocurrencies: Analytics and Data Applications
Today's episode introduces what Blockchains are, what Crypto is, and Data Science applications of these technologies. Philip Gradwell of globally-renowned Chainalysis Inc. is our brilliant guide.
Philip:
• Is Chief Economist at Chainalysis, the world’s leading crypto analytics firm — their analysis is regularly featured by major news outlets.
• Previously worked as Principal at Vivid Economics, where he helped grow the consulting firm to 40 people, eventually culminating in its acquisition by consulting giant McKinsey & Company.
• Holds a Master’s in Economics from UCL and a PPE degree — that’s Philosophy, Politics, and Economics — from the University of Oxford.
Today’s episode will appeal to anyone looking for an introduction to the blockchain and cryptocurrencies. It’ll hold special appeal for people keen to do data science with these technologies.
In this episode, Philip details:
• Similarities and differences between analyzing cryptocurrencies and the established fiat currencies.
• His crypto data analytics pipeline.
• How he develops data products for a wide range of users, including businesses, banks, governments, and law enforcement.
• How the blockchain facilitates innovative computing and machine learning technologies.
• What he looks for in the data scientists he hires.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
OpenAI Whisper: General-Purpose Speech Recognition
One of the challenges holding machines back from approaching human-level speech recognition like Whisper has has been acquiring sufficiently large amounts of high-quality, labeled training data. “Labeled” in this case means audio of speech that has a corresponding text associated with it. With enough of these labeled data, a machine learning model can learn to take in speech audio as an input and then output the correct corresponding text.
Read MoreTools for Deploying Data Models into Production
Today's guest is mighty Erik Bernhardsson — creator of Spotify's music recommender, prolific open-source developer, world-leading technical blogger, and now model-deployment-tool entrepreneur via Modal Labs.
Erik:
• Is the Founder and CEO of Modal Labs, a startup building innovative tools and infrastructure for data teams.
• Previously was CTO of the real estate startup Better, where he grew the engineering team from the size of 1 — himself — to 300 people.
• Was also previously an Engineering Manager at Spotify, where he created their now-ubiquitous music-recommendation algorithm.
• Is a prolific open-sourcer, having created the popular Luigi and Annoy libraries, among several others.
• Is an industry-leading blogger with posts that frequently feature on the front page of Hacker News.
Today’s episode gets deep into the weeds at points, so it will be particularly appealing to practicing data scientists, ML engineers, and the like, but much of the fascinating, wide-ranging conversation in this episode will appeal to any curious listener.
In this episode, Erik details:
• How the Spotify music recommender he built works so well at scale.
•The litany of new data science and engineering tools he’s excited about and thinks you should be excited about too.
•What open-source library he would develop next.
•Why he founded his Modal and how their tools empower data teams.
• Having interviewed more than 2000 candidates for engineering roles, his top tips both for succeeding as an interviewer and as an interviewee.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The Joy of Atelic Activities
You might think to yourself “I could be spending this time productively!” But pushing past these inner calls for productivity and leaning into the initial discomfort of atelic activities is likely to be rewarding. When you’re consumed by telic activities, by always pursuing outcomes, you’re missing out on being, on appreciating being alive for the fleeting moments that you have.
Read MoreCausality in Sequential Data
Inferring Causality is uniquely powerful when done with Sequential Data: data unfolding over time. Forecasting guru Dr. Sean Taylor — renowned for Prophet and now Motif Analytics co-founder — leads us through the topic.
Sean:
• Is Co-Founder and Chief Scientist of Motif Analytics, a startup that blends his deep expertise in causal modeling with sequential analytics.
• Previously worked as a Data Science Manager at Lyft.
• Also worked as a Research Scientist Manager at Facebook, where he led the development of the renowned open-source forecasting tool, Prophet.
• Holds a PhD in Information Systems from New York University and a BS in Economics from the University of Pennsylvania.
Today’s episode gets deep into the weeds on occasion, particularly when discussing making causal inferences, but most of the episode will resonate with any curious listener.
In this episode, Sean:
• Publicly unveils his new venture, filling us in on why now was the right time for him to co-found and lead data science at an ML startup.
• Details what causal modeling is, why every data scientist should be familiar with it, and how it can make a real-world impact, with many illustrative examples from his time at Lyft.
• Fills us in on the infrastructure and teams required for large-scale causal experimentation.
• Covers how causal modeling and forecasting can’t be fully automated today as it requires humans to make assumptions, but also how humans can make these assumptions in a more informed manner thanks to data visualizations.
• Explains what the field of Information Systems is and, having conducted several hundred interviews, what he looks for in the data scientists he hires.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Data Science Interviews with Nick Singh
For an episode all about tips for crushing interviews for Data Scientist roles, our guest is Nick Singh — author of the bestselling "Ace the Data Science Interview" book and creator of the DataLemur SQL interview platform.
Nick:
• Co-authored “Ace the Data Science Interview”, an interview-question guide that has sold over 16,000 copies since it was released last year.
• Created the DataLemur platform for interactively practicing interview questions involving SQL queries.
• Worked as a software engineer at Facebook, Google, and Microsoft.
• Holds a BS in engineering from the University of Virginia.
Today's episode is ideal for folks who are looking to land a data science job for the first time, level-up into a more senior data science role, or perhaps land a data science gig at a new firm.
In this episode, Nick details:
• His top tips for success in data science interviews.
• Common misconceptions about data science interviews.
• How to become comfortable with self-promotion and increase your chances of landing your dream job.
• Strategies for when interviewers ask if you have any questions for them.
• The subject areas and skills you should master before heading into a data science interview.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.