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.
Filtering by Category: Professional Development
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.
Data 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.
TEDx Talk: How Neuroscience Inspires A.I. Breakthroughs that will Change the World
My first TED-format talk is live! In it, I use (A.I.-generated!) visuals to color how A.I. will transform the world in our lifetimes, with particular emphases on climate change, food security, and healthcare innovations.
Thanks to Christina, Banu, and everyone at TEDxDrexelU for inviting me to speak, organizing a slick event, and masterfully editing the footage of my talk.
Thanks to Ed, Andrew, and Shaan at Nebula.io for providing invaluable feedback on drafts of my talk. It's only due to your constructive criticism that the final version turned out as well as it did. Thanks as well to Steven and Alex at Wynden Stark for kindly covering the travel costs of any employees that came down to Philadelphia to see the talk in-person.
Finally, thanks to Taya and Hannah at OpenAI for providing me with early access to custom images from their DALL-E 2 model. These were critical to me being able to tell the effectively convey the narrative I yearned to.
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.
Who Dares Wins
Even if we don’t achieve what we originally set out to achieve, by having dared to achieve it, by having taken action in the direction of the achievement, we learn from the experience and we gain invaluable information about ourselves and the world. Having dared, we find ourselves at a new, enriched vantage point that we otherwise would never have ventured to. From there, whether we achieved the original goal or not, we can iterate — dare again — perhaps to achieve success at the original objective or perhaps we identify some entirely new objective that would have otherwise been inconceivable without having dared.
Read MoreDaily Habit #11: Assigning Deliverables
To ensure that deliverables are assigned, if you’re running the meeting you can formally set the final meeting agenda item to be something like “assign deliverables”. If you’re not running the meeting, you can suggest having this final agenda item to the meeting organizer at the meeting’s outset or even as the meeting begins to wrap up. By assigning deliverables in this way, we not only make the best use of everyone’s time going forward, but we also maximize the probability that all of the essential action items are actually delivered upon.
Read MoreInferring Causality with Jennifer Hill
Inferring causal direction — as opposed to merely identifying correlations — is central to all real-world data science applications. World-leading expert and author on causality, Prof. Jennifer Hill, is our guest this week.
Jennifer:
• Is Professor of Applied Statistics at New York University, where she researches causality and practical applications of causal research, such as those that are vital to scientific development and government policies.
• Co-directs the NYU Masters in Applied Statistics and directs PRIISM (a center focused on impactful social applications of data science).
• With the renowned statistician Andrew Gelman, wrote the book "Data analysis using regression and multilevel/hierarchical models", an iconic textbook that has been cited over 15k times.
• Holds a PhD in Statistics from Harvard University.
Intended audience:
• Today’s episode largely contains content that will be of interest to anyone who’s keen to better understand the critical concept of causality.
• It also contains technical parts that will appeal primarily to practicing data scientists.
In this episode, Jennifer details:
• How causality is central to all applications of data science.
• How correlation does not imply causation.
• How to design research in order to confidently infer causality from the results.
• Her favorite Bayesian and machine learning tools for making causal inferences within code.
• ThinkCausal, her new graphical user interface for making causal inferences without the need to write code.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Geospatial Data and Unconventional Routes into Data Careers
This week, the remarkably well-read Christina Stathopoulos, details open-source software for working with geospatial data... as well as how you can navigate your data-career path, no matter what your background.
Christina:
• Has worked at Google for nearly five years in several data-centric roles.
• For the past year, she’s worked as an Analytical Lead for Waze, the popular crowdsourced navigation app owned by Google.
• Is also an adjunct professor at IE Business School School in Madrid, where she teaches courses on business analytics, machine learning, data visualization, and data ethics.
• Previously worked as a data engineer at media analytics giant Nielsen.
• Holds a Master’s in Business Analytics and Big Data from IE Business School and a Bachelor’s in Science, Tech, and Society from North Carolina State University.
Today’s episode will appeal to a broad audience of technical and non-technical listeners alike.
In this episode, Christina details:
• Geospatial data and open-source packages for working with it.
• Her tips for getting a foothold in a data career if you come from an unconventional background.
• Guidance to help women and other underrepresented groups thrive in tech.
• The hard and soft skills most essential to success in a data role today.
• Her #bookaweekchallenge and her top data book recommendations.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
MLOps: Machine Learning
Analogous to the role DevOps plays for software development, MLOps enables efficient ML training and deployment. MLOps expert Mikiko Bazeley is our guide!
Mikiko:
• Is a Senior Software Engineer responsible for MLOps at Intuit Mailchimp.
• Previously held technical roles at a range of Bay Area startups, with responsibilities including software engineering, MLOps, data engineering, data science, and data analytics.
• Is a prominent content creator on MLOps – across live workshops, her YouTube channel, her personal blog, and the NVIDIA blog.
Today’s episode will appeal primarily to hands-on practitioners such as data scientists and software engineers.
In this episode, Mikiko details:
• What MLOps is.
• Why MLOps is critical for the efficiency of any data science team.
• The three most important MLOps tools.
• The four myths holding people back from MLOps expertise.
• The six most essential MLOps skills for data scientists.
• Her productivity tricks for balancing software engineering, content creation, and her athletic pursuits.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
A.I. Policy at OpenAI
OpenAI released many of the most revolutionary A.I. models of recent years, e.g., DALL-E 2, GPT-3 and Codex. Dr. Miles Brundage was behind the A.I. Policy considerations associated with each transformative release.
Miles:
• Is Head of Policy Research at OpenAI.
• He’s been integral to the rollout of OpenAI’s game-changing models such as the GPT series, DALL-E series, Codex, and CLIP.
• Previously he worked as an A.I. Policy Research Fellow at the University of Oxford’s Future of Humanity Institute.
• He holds a PhD in the Human and Social Dimensions of Science and Technology from Arizona State University.
Today’s episode should be deeply interesting to technical experts and non-technical folks alike.
In this episode, Miles details:
• Considerations you should take into account when rolling out any A.I. model into production.
• Specific considerations OpenAI concerned themselves with when rolling out:
• The GPT-3 natural-language-generation model,
• The mind-blowing DALL-E artistic-creativity models,
• Their software-writing Codex model, and
• Their bewilderingly label-light image-classification model CLIP.
• Differences between the related fields of AI Policy, AI Safety, and AI Alignment.
• His thoughts on the risks of AI displacing versus augmenting humans in the coming decades.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The A.I. Platforms of the Future
Ben Taylor returns for a third consecutive Five-Minute Friday! This week, he helps us look ahead and dig into what we can expect from the A.I. platforms of the future.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Data Engineering 101
Today's episode is all about Data Engineering — particularly the tools and techniques that Data Scientists should know. "Fundamentals of Data Engineering" book co-authors Matthew Housley and Joe Reis are guests!
Matt and Joe:
• Co-authored the brand-new "Fundamentals of Data Engineering" book that was published by O'Reilly Media and is already a bestseller.
• Co-founded the data architecture and data engineering consultancy Ternary Data. Joe is CEO of the firm while Matt is CTO.
In addition, Joe:
• Is an adjunct professor at the University of Utah.
• Previously founded several tech companies and has held both software engineering and data science roles.
• Holds a math degree from the University of Utah.
Matt:
• Holds a PhD in math from the University of Utah.
• Worked as a professor before becoming a data scientist in industry.
Today’s episode will appeal primarily to technical experts like data scientists and data engineers, but will also be of interest to anyone who manages technology projects that involve data flows.
In this episode, Matt and Joe detail:
• Why they identify as “recovering data scientists”.
• What kinds of people tend to become data scientists versus what kinds tend to become data engineers.
• Key components of their book such as latency trade-offs and the six data engineering undercurrents.
• Their favorite data engineering tools and techniques.
• What the Live Data Stack is and how it’s putting various data professional titles on a collision course.
• The biggest data engineering problems firms face and how to fix them.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The Real-World Impact of Cross-Disciplinary Data Science Collaboration
How to unlock breakthroughs — particularly in medicine — through cross-disciplinary data science is the main topic covered this week with the fascinating, trailblazing Professor Philip Bourne.
Philip:
• Is Founding Dean of the University of Virginia's School of Data Science.
• Is also Professor of Biomedical Engineering at Virginia.
• Is Founding Editor-in-Chief of the open-access journal PLOS Computational Biology.
• Was previously Associate Director for Data Science of The National Institutes of Health
Despite Prof. Bourne being a deep technical expert, he conveys concepts so magnificently that today’s episode should be broadly appealing to practicing data scientists and non-technical listeners alike.
In this episode, Philip details:
• Why he founded a School of Data Science.
• Why such schools are uniquely positioned to bear the fruits of applied data science research within universities.
• What the most important data science skills are.
• How computing and data science have evolved across academic departments in the recent decades.
• Fascinating practical applications of his biomedical data science research into the structure and function of biological proteins.
• The absolutely essential role of open-source software and open-access publishing in data science.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Simulations and Synthetic Data for Machine Learning
Running Simulations and generating Synthetic Data in order to create more-powerful Machine Learning models is this week's topic. Bewilderingly interesting two-time book author Mars Buttfield-Addison is our guest.
Mars:
• Is co-author of two O'Reilly Media books, "Practical Simulations for Machine Learning" and "Practical Artificial Intelligence with Swift".
• Is pursuing a PhD in computer engineering from the University of Tasmania, focused on writing high-performance software to track space objects.
• Teaches courses on A.I. and data science at the University of Tasmania.
• Is a regular speaker at top tech conferences around the world.
• Holds a bachelor’s degree in software development and data modeling.
Today’s episode should be equally fascinating to technical and non-technical folks alike.
In this episode, Mars details:
• What simulations and synthetic data are, and why they can be invaluable for real-life applications.
• How simulated bots can solve any problem by representing the problem as a 3D visualization.
• Why the mobile operating system language Swift is interesting for A.I.
• How much junk there is in space and why it’s critical we track it.
• What it’s like creating video games in a “secret” Tasmanian games lab.
• Whether programming or statistical skills are more important in data science.
• Why you might want to do a data science internship in industry if you’re thinking of having a career in academia.
Thanks to Suzanne Huston for introducing me to Mars :)
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Narrative A.I. with Hilary Mason
Hilary Mason, one of the world's best-known data scientists, fills us in on A.I. systems that generate interactive story narratives and on building a thriving early-stage A.I. company. This episode was filmed live on stage — so fun!
Hilary:
• Is Co-Founder and CEO of Hidden Door, a start-up that leverages narrative A.I. to generate unique, customized dialog and graphics in real time, thereby delivering a groundbreakingly immersive video game experience.
• Was previously Founder and CEO of Fast Forward Labs, an emerging-tech research company that was acquired by Cloudera.
• Was Data-Scientist-in-Residence at Accel, a leading venture capital firm.
• Co-founded several iconic tech communities in New York such as DataGotham and HackNY.
• Studied computer science at Brown University and Grinnell College.
• Is known for sharing useful data science knowledge with the public; she has over 120k followers on Twitter and over 160k followers on LinkedIn.
The first half of today’s episode contains some technical elements but by and large the episode should be appealing to anyone who’s keen to be on the cutting edge of machine learning application and commercialization.
In today’s episode, Hilary details:
• How narrative A.I. can assist creativity.
• How to build ML products with no quantitative error function to optimize.
• How to prevent A.I. systems from outputting non-sense or explicit content.
• The emerging ML technique she’s most excited about.
• What it takes to be successful as CEO of an early-stage A.I. company.
• How she’s hopeful A.I. will transform our lives for the better in the future.
Thank you to Jared Lander and Nicole DelGiudice of the New York R Conference for providing us with an amazing live forum to host a live SDS episode and for the exceptional footage. And thanks to Claudia Perlich for introducing me to Hilary!
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Data Engineering for Data Scientists
Prolific data science content creator 🎯 Mark Freeman details what Data Engineering is and why it's a critically useful subject area for data scientists to be proficient in. Hear all about it in this week's episode.
Mark:
• Is a Senior Data Scientist, with a Data Engineering specialization, at Humu (startup that has raised $100m in venture capital).
• Posts data science and software engineering tips daily on LinkedIn.
• Previously was data scientist at Verana Health and data analyst at the Stanford University School of Medicine.
• Also holds a Master’s in Community Health and Prevention Research from the Stanford medical school.
Today’s episode is geared toward listeners who are already in a technical role such as data scientists, data engineers, ML engineers, or software engineers — as well as to folks who’d like to grow into these kinds of roles.
In today’s episode, Mark details:
• The differences between junior, senior, and staff data scientists.
• What it takes to get promoted into more senior data science roles.
• How data engineering differs from data science.
• His top tools for data extraction, modeling, and pipeline engineering.
• His top tip for getting hired at a fast-growing VC-backed startup.
• How behavioral nudges can drastically improve workplace experiences.
• Why all data scientists should be interested in web3.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
PyMC for Bayesian Statistics in Python
Learn how Bayesian Statistics can be more powerful and interpretable than any other data modeling approach from Dr. Thomas Wiecki, a Core Developer of PyMC — the leading Bayesian software library for Python.
Thomas:
• Has been a Core Developer of PyMC for over eight years.
• Is Co-Founder and CEO of PyMC Labs, which solves commercial problems with Bayesian data models.
• Previously, he worked as VP Data Science at Quantopian Inc.
• Holds a PhD in Computational Neuroscience from Brown University.
Today’s episode is more on the technical side so will appeal primarily to practicing data scientists.
In this episode, Thomas details:
• What Bayesian statistics is.
• Why Bayesian statistics can be more powerful and interpretable than any other data modeling approach.
• How PyMC was developed and how it trains models so efficiently.
• Case studies of large-scale Bayesian stats applied commercially.
• The extra flexibility of *hierarchical* Bayesian models.
• His top resources for learning Bayesian stats yourself.
• How to build a successful company culture.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
OpenAI Codex
OpenAI's Codex model is derived from the famed GPT-3 and allows humans to generate working code with natural language alone. It's flexibility and capability are quite remarkable! Hear all about it in today's episode.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The State of Natural Language Processing
As the LaMDA "sentience" hubbub highlights, Natural Language Processing is perhaps the most exciting and rapidly accelerating area of Machine Learning. Hear all about NLP from the deep expert Rongyao HUANG.
(LaMDA is definitely not sentient, by the way... but it is an impressive display of state-of-the-art conversational machine capabilities.)
Rongyao:
• Is Lead Data Scientist at CB Insights, a marketing intelligence platform.
• Previously she worked as a data scientist at a number of other New York start-ups and as a quantitative research assistant at Columbia University.
• She holds a masters in research methodology and quantitative methods from Columbia University in the City of New York.
Today’s episode is more on the technical side so will appeal primarily to practicing data scientists, however the second half of the episode does contain general sage guidance for anyone seeking to navigate career options as well as to balance personal and professional obligations.
In today’s episode, Rongyao details:
• The evolution of NLP techniques over the past decade through to the large transformer models of today.
• The practical implications of this dramatic NLP evolution.
• How the “scaling law” will impact NLP model capabilities over the coming decade.
• The major limitations of today’s NLP approaches and how we might overcome them.
• Her Bauhaus-inspired model for effective data science.
• Her pathfinding model for making effective career choices.
• Her top tips for staying sane while juggling career and family.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.