From Pro Athlete to Data Engineer: Colleen Fotsch’s Inspiring Journey

Colleen Fotsch won national swimming championships and was a pro athlete in both CrossFit and bobsledding. Now she's excelling at data analytics and engineering! Today, hear her fun, inspiring and practical story.

More on Colleen:

• As a collegiate swimmer, she won national championships and set an American record in the relay.

• As a pro CrossFit athlete, she twice competed at the “Games”, which is the highest echelon of the sport.

• And then she simultaneously pursued a degree in data analytics while training with the US Bobsled team.

• An injury ended her Olympic Bobsled team dream, but luckily she’d been pursuing that analytics career in parallel!

• She began working full-time as a data analyst four years ago and has now grown into a data-engineering leadership role at a healthcare-staffing firm called CHG Healthcare in Utah, where she serves as Senior Technical Manager of their Data Platform.

• Inspires her 280,000 Instagram followers on a daily basis.

Today’s episode essentially has two separate parts:

• The first half focuses on Colleen’s exciting journey to the highest levels of three sports: swimming, CrossFit and bobsledding. That part should be fascinating to just about anyone.

• The second half covers Colleen’s transition into data analytics and data engineering; that part will appeal to technically-minded listeners, particularly ones considering a career in or early on in a career in analytics or engineering.

In today’s episode, Colleen details:

• The connection between a competitive sports mindset and data-career success.

• Proven strategies for being hired into your first data role later in your career.

• Why being "not smart enough" for coding was a mental block she had to overcome.

• How analytics engineering bridges the gap between data engineering and analysis.

• The huge benefits deskbound professionals can enjoy by including regular exercise in their week, and tips and tricks for developing or growing an exercise habit.

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.

Upskilling in Data Science and Machine Learning

This week, iconic Stanford University Deep Learning instructor and entrepreneur Kian Katanforoosh details how ML powers his EdTech platform Workera, enabling you to systematically fill gaps in your data science skills.

Kian:
• Is Co-Founder and CEO of Workera, a Bay Area education technology company that has raised $21m in venture capital to upskill workers, with a particular early focus on upskilling technologists like data scientists, software developers, and machine learning specialists.
• Is a lecturer of computer science at Stanford University (specifically, he teaches the extremely popular CS230 Deep Learning course alongside Prof. Andrew Ng, one of the world’s best-known data scientists).
• Was awarded Stanford’s highest teaching award.
• Is also a founding member of DeepLearning.AI, a platform through which he’s taught over three million students deep learning.
• Holds a Masters in Math and Computer Science from CentraleSupélec.
• Holds a Masters in Management Science and Engineering from Stanford.

By and large, today’s episode will appeal to any listener who’s keen to understand the latest in education technology, but there are parts here and there that will specifically appeal to practicing technologists like data scientists and software developers.

In this episode, Kian details:
• What a skills intelligence platform is.
• Four ways that machine learning drives his skills intelligence platform.
• What frameworks and software languages they selected for building their platform and why.
• What he looks for in the data scientists and software engineers 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.

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.

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.

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.