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Now displaying: Category: Data Science
Apr 24, 2018

Ivy Nguyen shares insights on: 

  1. Going beyond data - why data is NOT the new oil
  2. Model-Market Fit vs. Product-Market Fit
  3. Data Moat - a term she coined at Zetta Ventures - What does that mean and how can you build one.
  4. AI product development strategy. A trifecta of:
    1. Performance Plateau
    2. Minimum Algorithm Performance
    3. Stability Threshold

About Ivy Nguyen:

  • Investor @ Zetta Ventures Partners, VC firm that exclusively invests in startups leveraging Data and AI.
  • Prior to Zetta, Ivy sourced and led investments in several companies including Constructor, NuCypher, Assembly, and Agridata.
  • Ivy graduated with a chemical engineering degree from Stanford
  • Fun fact about Ivy: Ivy loves to knit and is planning to make a hat from a neural net generated knitting pattern.

Follow Ivy on Twitter or LinkedIn to keep up with AI Product Development and her new AI Metrics Benchmark program. 

Nov 9, 2017

In this episode, Tom shares his advice on:

  • Building Data Startups
  • Company culture and growth 
  • Future of AI

About Tom Reilly:

  • Tom Reilly is the CEO of Cloudera.
  • Tom has a distinguished 30-year career in the enterprise software market.
  • Tom has led several tech startups through a successful initial public offering.
  • Prior to Cloudera, Tom served as CEO of ArcSight, an enterprise security company acquired by HP in 2010.
  • Fun fact: Tom is one of things in the Internet of Thing (IoT). More on that in the podcast. 
Jul 14, 2017

I had a chance to sit down with co-founders of Monicat Data.  It's the first time I chatted with experts at the intersection of data science and art, so I took a lot of notes. 

What I learned:

  • How to quantify art... This is the first time I heard some articulate it so well. A must listen for anyone who thinks Art cannot be quantified.
  • How creative supporting organizations approach analytics 
  • Data Entrepreneurship learning lessons!
  • Product Management - How is it done in Art Organizations!
  • Covered tons of other topics - including my favorite - round of rapid fire questions! 

About Monicat Data: 

Jasmine, Cassie, and Kurt are co-founders of Monicat Data, a Data Management & Research agency specifically focused on bringing strategic data to Artists and Creative Supporting Organizations.

Since launching September of 2016, Monicat Data has aided creative organizations with data solutions ranging from notable names such as Springboard for the Arts, Rhymesayers Entertainment, Forecast Public Art. Fashion Week MN, The Bush Foundation and Mu Performing Arts--to name a few."

Monicat Data also offers 'Data for Art' workshops to the Minneapolis/St. Paul in the efforts of educating artists and creative supporting organizations on the power of data organization and strategy implementation.

Jasmine has a background in marketing analytics and an MBA.

Cassie has a background in music, computer science, and art.

Both Jasmine and Cassie love working on data that makes people dream.

Fun fact about Jasmine & Cassie: Aside from running their data startup, Jasmine is an avid yogi and Cassie is an opera singer.

Jun 29, 2017

In this episode, Jarah Euston discusses:

  1. How is Data Operations being redefined – 2015 Vs 2017 & Beyond
  2. Top challenges most data-driven executives face when it comes to gaining insights from their data.
  3. An opportunity that most data-driven organization are unaware of – this is going to be blow up in next couple of years
  4. Entrepreneur / Executive Special Edition:
    1. How to achieve unstoppable momentum for your startup (whether you work in a scrappy startup environment or a large org, this applies to you!)
    2. How to get better at making presentations
    3. Women Tech Entrepreneurship: Challenges and Path to Success
  5. Top stories she uses when communicating with C-Suite about Data Science

About Jarah Euston:

Jarah Euston is a growth and analytics leader who has propelled several Silicon Valley Startups to the top.

Jarah is one of the Business Insider’s “28 Most Powerful Women in Mobile Advertising.”

Jarah served as growth and analytics leader at Flurry, Yahoo, and most recently Nexla, an early stage next gen data ops startup, winner of several startup awards, including Strata Hadoop and TechCrunch Disrupt.

Jarah has a background in Economics and an MBA from Wharton

To learn more about Jarah and Nexla, please visit or reach out to Jarah “at”

About Nexla:

Nexla automates DataOps so companies can quickly derive value from their data, with minimal engineering required.

Nexla’s secure platform runs in the cloud or on-premise. It allows business users to send, receive, transform, and monitor data in their preferred format via an easy to use web interface.



May 4, 2017

In this episode, Rumman Chowdhury shares her insights on:

  1. Enterprise AI - What exactly is it and the right way to think of it
  2. Two key advantages Enterprise AI can provide to your organization 
  3. Biggest barrier executive leaders face when it comes to Entreprise AI
  4. Humanity and AI - Current AI models and flawed reality
  5. How to create AI with Active Inclusion
  6. Education as it stands today - how it's about to be revolutionized
  7. And tons of other valuable insights...

I hope you enjoy listening to it as much as I enjoyed created it!


Rumman's Blog:

Rumman's Twitter: 

AI Education Startup

Studies Show Hosted by Rumman Chowdhury and Imran Siddiquee.

1000 women in data science:
About Rumman Chowdhury

Rumman Chowdhury is an AI Authority working on cutting edge applications of Artificial Intelligence at Accenture. She also serves on the Board of Directors for several AI startups.

Rumman holds two undergraduate degrees from MIT, and a Masters in Quantitative Methods of Social Sciences from Columbia University. Rumman also has a PhD in Political Science from the University of California, San Diego.

Rumman has given several talks and tutorials, some of them include Intel Analytics Conference, Open Data Science Conference, Machine Learning Conference, Women Catalysts, PyBay, and Demystifying AI Conference.

In mainstream media, Rumman has been interviewed for the PHDivas podcast, German Public Television, and fashion line MM LaFleur. In 2017, Rumman has upcoming talks at the Global Artificial Intelligence Conference, at Strata + Hadoop San Jose, Southern Data Science Conference, and Strata + Hadoop London

Nov 14, 2016

In this episode, David Herman shared his insights on:

  • How can financial service companies create value using the emerging integration between behavioral neuroscience and financial data science (the right way and the wrong way)
  • How the consumers win when these two fields interact
  • How to win consumer trust when building advanced AI products or campaigns, especially catering to finance industry.

David Herman packed some powerful concepts and insights in quick 30 min conversation and it is definitely worth a listen or two :)

Dave's been busy at Payoff, but his youtube channel has some great videos as well. 

For additional questions, you may reach out to David on his linked or his email dh.herman "at"


Aug 12, 2016

Richard Demsyn-Jones has an extensive experience building predictive models. Currently, Richard is at Google Trust and Safety Analytics, Prior to that Richard helped Capital One with their data science opportunities as a principal data scientist. Richard has an academic background in economics.

I bumped into Richard when he was presenting on sports analytics, and despite zero interest or prior experience in watching in Hockey, I found myself deeply immersed in Richard’s presentation where he discussed data analytics around goalie quality...This goes to show Richard’s compelling storytelling abilities.

Couple of fun facts about Richard: He thinks cereal is good for any meal, also he’s from Canada, but doesn’t want you to hold that against other Canadians.

In this podcast, Richard shares his thoughts on:

  1. When it comes to betting, is natural talent more important than quantitative skills
  2. Nate Silvers story on why he left the poker world...
  3. A key area for a sports analyst to focus on (even if you are a hobbyist, you may want to follow the advice here)
  4. How can data be applied when selecting lineups
  5. Going beyond sports, how to apply data when building new teams and increase team performance…where you can play off the way different strengths interact with each other.
  6. Round of rapid fire questions on data…my favorite part.

Richard’s Blog:

Richard’s email: [at]

Disclaimer: Opinions expressed herein are in my own, and do not represent my employer’s viewpoints on sports analytics or other projects.

Jul 2, 2016

Sam Abrahams is a TensorFlow evangelist and a kickass storyteller, programmer, and a statistician! In this podcast episode, Sam discusses his thoughts on TensorFlow coming from a hard-core practitioner point of view. We chat about:

  • TensorFlow and how it compares to other machine learning frameworks
  • Mastering TensorFlow: How to go from being zero to well-sought out TensorFlow expert
  • Journey of a Data Scientist. What does it take to become one...
  • And a round of intriguing rapid fire questions guaranteed to give you some golden nuggets!

Show References:

TensorFlow White Paper

Sam's Github With TensorFlow Whitepaper Notes

Sam's Upcoming Book on TensorFlow For Machine Intelligence

Sam's Blog:



Jun 7, 2016

Sean is the director of data science at Space-Time Insight, a leading provider of advanced analytics software for organizations looking to leverage machine learning for their business applications. Having worked across diverse industries, and alongside many talented professionals, Sean has seen the blend of approaches required to successfully convert raw data into real world value.

Sean holds his doctorate in scientific computing, where he used advanced mathematics, parallel computing and optimization to solve challenges in nanotechnology, chemistry and renewable energy. After completing his Ph.D. Sean started his own data science consulting practice, helping companies automate decision-making and uncover patterns in large amounts of data. Sean has since joined a major technology consulting firm working with cross-discipline teams to build the next generation of adaptive, data-driven applications. 

Sean’s upcoming book is focusing on building products with data and will be published by O'Reilly.

To connect with Sean McClure, reach out to him on LinkedIn or Twitter

In this conversation, we touch on Return of Investment on Data Science and other things like:

  • Real World Value of Data Science
  • Pitfalls of Data Science
  • Building Data Science Teams and Culture
  • Is Data Science a Boy's Club?
  • How to Develop Storytelling skills as a Data Scientist?

Be sure to check out the end of the podcast where Sean reveals his favorite data science product that had the most positive impact on his life. 

May 8, 2016

Ben Spooner has several years of experience as an Army Officer at a battle post in Iraq, both in Intelligence as well as Executive capacities. I sat down with him to discuss his thoughts on activating a data driven work culture using data visualization and dashboards as primary weapons. 

Here's what we discussed:

  • A complete dashboard lifecycle - from inception to decision-making
  • How to build dashboards that drive decision making
  • Up close and personal look at the data scientist who did data research and exploration at the front line trenches of  insurgency in a war zone
  • When is raw data the best form of reporting (instead of a fancy-schmancy dashboard)
  • Two critical factors that fuels dashboard building process…They are C-words (hint: Curiosity and Collaboration)
  • Data Driven culture has two key pursuits: truth and discovery. How to make a dashboard that builds this type of a data driven culture...
  • How to deal with stakeholders who don’t appreciate the exploratory process of dashboard building… and how to set their expectations and empower them.
  • Bonus: Self-correcting semi-autonomous system and its application to insurance claims processing...

Reach out to Ben's through LinkedIn or Email to learn more about his exciting data science projects.


Mar 8, 2016
Peter Nelson, CEO of, and I sat to discuss Adaptive Design and it’s impact on Conversion Optimization. Peter brings a breath of experience to Marketing Technology space and it was fun sitting down with him to discuss everything from his entrepreneurial philosophy to his home in Norway...
Interesting gems of knowledge I picked up:
  • How to get unicorn conversion rate for mobile devices...
  • What is Adaptive Design and how does it differ from responsive design?
  • Mobile specific calls to actions and navigation features...
  • How to effectively use sticky header and footer in mobile design?
  • How to cut testing time by more than half so you can fit more tests in your testing calendar!
Wordstream Study on Mobile Click Distribution
Link to Peter’s Company:
Jan 28, 2016

For the past 2 years, Zach has been working as a data scientist at an industry leading data consulting firm.  He works in fraud analytics space where he and his team has saved hundreds of millions of dollars of federal dollars using sophisticated data science techniques. He is also a recent graduate of data science program at UC Berkeley.

When I met him, I was really impressed with your ability to speak “real world” data science and later I found out that he has a professional background in teaching complex topics like physics and calculus, which is what makes you such a good communicator in this field.

I sat down with him on a sunny Saturday afternoon to discuss one of the most exciting projects he has worked on in his data science career. 

Here's a quick recap of what we discussed:

  • Can we predict what pitch is going to be thrown next in major league baseball? Implications for Hitters (batters) equipped with this data is $10M to $15M per season. 
  • A wave of In-game analytics about to hit the sports industry. This in-game analytics may eat ‘Moneyball’ style static analytics for breakfast
  • Are better pitchers tough to predict? Or are they just as easy to predict as others?
  • What’s the correlation between a pitcher’s ERA and his predictability? ERA is a baseball metric - earned runs average - its used to gauge how well a pitcher is doing in a season.
  • Is it better to be 90% accurate 30% of the times or 30% accurate 90% of the times? 
  • What has Ashton Kutcher to do with Data Science and Social Good?
  • How to cultivate the presence of mind when communicating about data? 
Link to the Project:
Link to Zach's LinkedIn:


Nov 20, 2015

Lisa Kirch has over 20 years of broad business experience and deep understanding of data science. As she was recently celebrating her graduation from Berkeley's data science program, I got together with her to chat about a very interesting project she has been working on for the last few weeks. 

Listen to this quick (less than 15 mins) discussion to learn more about:

- Future of smart home (connected home) and conversation

- Data science can help create a sustainable ways of living

- Compelling Visuals Dashboards 

Connect with Lisa at on LinkedIn or @lkonthego on twitter. For email fans, reach out to Lisa and learn more about her data science projects via email at

Jul 12, 2015

Sharon Lin has big aspirations when it comes to big data. Sharon wants to apply data science to solve California’s mega drought problem by integrating real-time weather data into crop irrigation systems. Additionally, she wants to use advanced image recognition algorithms to predict, track, and monitor endangered wildlife migration patterns to help endangered species from being hunted.

I sat down with her this week to talk about applications of data science in the field of mobile behavioral analytics. Over the last 5 years, Sharon has worked in the emerging field of mobile behavioral analytics and she shared some golden nuggets with me. 

Here’s what I picked up:

  • Mobile analytics and its relationship with evolution and emergence of IoT (internet of things)
  • Privacy, Personalization, and IoT: Sharon shared her brief thoughts on the intersection of these areas. 
  • Two main problems most companies face when it comes to fully maximize their data potential, and what to do about it!
  • Difference between data processing engines Spark and Hadoop 

Connect with Sharon on her LinkedIn: Sharon’s LinkedIn or email her at “sharonxlin” at “”