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.

Causality 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.

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.

Sports Analytics and 66 Days of Data with Ken Jee

Ken Jee — sports analytics leader, originator of the ubiquitous #66daysofdata hashtag, and data-science YouTube superstar (190k subscribers) — is the guest for this week's fun and candid episode ⛳️🏌️

In addition to his YouTube content creation, Ken:
• Is Head of Data Science at Scouts Consulting Group LLC.
• Hosts the "Ken's Nearest Neighbors" podcast.
• Is Adjunct Professor at DePaul University.
• Holds a Masters in Computer Science with an AI/ML concentration.
• Is renowned for starting #66daysofdata, which has helped countless people create the habit of learning and working on data science projects every day.

Today’s episode should be broadly appealing, whether you’re already an expert data scientist or just getting started.

In this episode, Ken details:
• What sports analytics is and specific examples of how he’s made an impact on the performance of athletes and teams with it.
• Where the big opportunities lie in sports analytics in the coming years.
• His four-step process for how someone should get started in data science today.
• His favorite tools for software scripting as well as for production code development.
• How the #66daysofdata can supercharge your capacity as a data scientist whether you’re just getting started or are already an established practitioner.

Thanks to Christina, 🦾 Ben, Serg, Arafath, and Luke for great questions for Ken!

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Automating Data Analytics

Meet the brilliant Dr. Peter Bailis. Inspired by research he carried out as a professor at Stanford University, three years ago he founded Sisu to automate enterprise data analytics and the firm has already raised $128m.

In today’s episode, Peter details:
• The revolutionary work being carried out by Sisu: generating automated, actionable reports in minutes that might otherwise take a team of data analysts days
• His guidance for people looking to succeed at growing a tech start-up, particularly if they come from an academic or technical background
• What he looks for in the data scientists and software engineers he hires
• His most important daily tools for developing software productively
• The academic research he carried out at Stanford that’s behind Sisu’s innovative capabilities

Peter:
• Is CEO of Sisu, the firm he founded in San Francisco
• Has raised $128m in venture capital from some of the most prestigious VC firms out there such as Andreessen Horowitz
• Was an assistant professor of computer science at Stanford University, where he’s still an adjunct faculty member today
• Holds several computer science degrees: an undergrad from Harvard University and a PhD from the University of California, Berkeley

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Hurdling Over Data Career Obstacles

This week's guest is the sensational Karen Jean-François: mathematician, award-winning data analyst, podcast host, and French national champion in the 400m hurdle. She details how to hurdle over data career obstacles.

More specifically, in today’s episode Karen fills us in on:
• How to overcome Imposter Syndrome in the data science industry
• Why you might want to consider becoming a data science manager versus remaining a more specialized individual contributor
• The data tools that she uses regularly
• The productivity and prioritization techniques that enable her to juggle her day job, her thriving podcast, and her world-class athletic pursuits

Karen:
• Manages banking analytics at publicly-listed Cardlytics
• Is the producer and host of the "Women in Data" podcast
• Was recognized last year as one of the "Twenty in Data and Technology"
• Holds degrees in mathematics and computing from Paris-Sud University (Paris XI)
• Was French national champion in the 400m hurdle and bronze medalist in the 100m hurdle

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Data Meshes and Data Reliability

The fun and brilliant Barr Moses joins me this week to detail for us what organization-transforming Data Meshes are, as well as how to track and improve the "Data Uptime" (reliability) of your production systems.

Barr is co-founder and CEO of Monte Carlo, a venture capital-backed start-up that has grown in head count by a remarkable 10x in the past year. Monte Carlo specializes in data reliability, making sure that the data pipelines used for decision-making or production models are available 24/7 and that the data are high quality.

In this SuperDataScience episode, Barr covers:
• What data reliability is, including how we can monitor for the "good pipelines, bad data" problem
• How reliable data enables the creation of a Data Mesh that empowers data-driven decision-makers across all of the departments of a company to independently create and analyze data
• How to build a data science team
• How to get a data-focused start-up off the ground, generating revenue and rapidly scaled up

In addition, Barr took time to answer questions from listeners, including those from Svetlana, Bernard, and A Ramesh. Thanks to Scott Hirleman for suggesting Barr as a guest on the show and thanks to Molly Vorwerck for ensuring everything ran perfectly.

Listen or watch here.

R in Production

Dutch national-podium-level powerlifter Veerle van Leemput joins me this week to detail how R is not only an option for production, but may in fact be the *best* production option if data models are central to your application.

Over the course of the episode, Veerle runs down for us her favorite R tools for:
• Data gathering
• Model development
• Deployment into production systems

Veerle has held a number of data-science leadership roles at Dutch companies. She now serves as Managing Director and Head of Data Science at Analytic Health, a London-based firm that builds data-centric software for the healthcare industry. And she was silver medalist in the 57kg class of the 2021 Dutch national powerlifting championships with a total of 335kg (~739 pounds) across the back squat, bench press, and deadlift.

Listen or watch 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.