DataFramed

In this episode of DataFramed, Adel speaks with Sudaman Thoppan Mohanchandralal, Regional Chief Data, and Analytics Officer at Allianz Benelux, on the importance of building data cultures and his experiences operationalizing data culture transformation programs.

Throughout the episode, Sudaman talks about his background, the Chief Data Officer’s mandate and how it has evolved over the years, how organizations should prioritize building data cultures, the science behind culture change, the importance of executive data literacy when scaling value from data, and more.

Relevant links from the interview:

• Connect with Sudaman on LinkedIn
• Check out Sudaman’s Webinar on DataCamp
• Why Data Culture Matters

In this episode of DataFramed, Adel speaks with Elad Cohen, VP of Data Science and Research at Riskified on how data science is being used to combat fraud in eCommerce.

Throughout the episode, Elad talks about his background, the plethora of data science use-cases in eCommerce, how Riskified builds state-of-the-art fraud detection models, common pitfalls data teams face, his best practices gaining organizational buy-in for data projects, how data scientists should focus on value, whether they should have engineering skills, and more.

Relevant links from the interview:

• Connect with Elad on LinkedIn
• Register for our upcoming webinars
• How Riskified chooses what to research

In this episode of DataFramed, Adel speaks with Barr Moses, CEO, and co-founder of Monte Carlo on the importance of data quality and how data observability creates trust in data throughout the organization. 

Throughout the episode, Barr talks about her background, the state of data-driven organizations and what it means to be data-driven, the data maturity of organizations, the importance of data quality, what data observability is, and why we’ll hear about it more often in the future. She also covers the state of data infrastructure, data meshes, and more. 

Relevant links from the interview:

• Connect with Barr on LinkedIn
• Learn more about data meshes
• Check out the Monte Carlo blog
• DataCamp's Guide to Organizational Data Maturity

In this episode of DataFramed, Adel speaks with Sergey Fogelson, Vice President of Data Science and Modeling at Viacom on how data science has evolved over the past decade, and the remaining large-scale challenges facing data teams today.

Throughout the episode, Sergey deep-dives into his background, the various projects he’s been involved with throughout his career, the most exciting advances he’s seen in the data science space, the largest challenges facing data teams today, best practices democratizing data, the importance of learning SQL, and more. 

Relevant links from the interview:

• Connect with Sergey on LinkedIn
• Check out Sergey’s course on DataCamp
• Learn more about Airflow
• Learn more about PySpark
• Learn more about SQL

More resources from DataCamp

• Upskill your team with DataCamp
• Our Guide on Open Source Software in Data Science
• Your Organization’s Guide to Data Maturity

In this episode of DataFramed, Adel speaks with Dan Becker, CEO of decision.ai and founder of Kaggle Learn on the intersection of decision sciences and AI, and best practices when aligning machine learning to business value.

Throughout the episode, Dan deep-dives into his background, how he reached the top of a Kaggle competition, the difference between machine learning in a Kaggle competition and the real world, the role of empathy when aligning machine learning to business value, the importance of decisions sciences when maximizing the value of machine learning in production, and more. 

Links:

• Follow Dan on Twitter
• Follow Dan on LinkedIn
• What 70% of data science learners do wrong
• Check out Dan’s course on DataCamp
• decision.ai
• Dan’s climate dashboard

In this episode of DataFramed, Adel speaks with Amen Ra Mashariki, principal scientist at Nvidia and the former Chief Analytics Officer of the City of New York on how data science is done in government agencies, and how it's driving smarter cities all around us. 

Throughout the episode, Amen deep-dives into the use-cases he worked on to make the city of New York smarter, how data science allows cities to become more reactive and proactive, the unique challenges of scaling data science in a government setting, the friction between providing value and data privacy and ethics, the state of data literacy in government, and more. 

Links from the interview:

• Follow Amen on LinkedIn
• Follow Amen on Twitter
• The New York City Business Atlas
• Hurricane Sandy FEMA After-Action Report
• Data Drills

We are super excited to be relaunching the DataFramed podcast. In this iteration of DataFramed, Adel Nehme, a data science educator at DataCamp, will uncover the latest thinking on all things data and how it’s impacting organizations through biweekly (once every two weeks) interviews and conversations with data experts from across the world. 

Check out this snippet for a preview of what’s to come and for a short chat with DataCamp’s CEO Jonathan Cornelissen on where he thinks data science is headed and the major challenges facing data teams today. 

Links:

• For the rest of April, get free access to DataCamp.
• Get involved with DataCamp Donates

Before the COVID-19 crisis, we were already acutely aware of the need for a broader conversation around data privacy: look no further than the Snowden revelations, Cambridge Analytica, the New York Times Privacy Project, the General Data Protection Regulation (GDPR) in Europe, and the California Consumer Privacy Act (CCPA). In the age of COVID-19, these issues are far more acute. We also know that governments and businesses exploit crises to consolidate and rearrange power, claiming that citizens need to give up privacy for the sake of security. But is this tradeoff a false dichotomy? And what type of tools are being developed to help us through this crisis? In this episode, Katharine Jarmul, Head of Product at Cape Privacy, a company building systems to leverage secure, privacy-preserving machine learning and collaborative data science, will discuss all this and more, in conversation with Dr. Hugo Bowne-Anderson, data scientist and educator at DataCamp.

Links from the show

FROM THE INTERVIEW

• Katharine on Twitter
• Katharine on LinkedIn
• Contact Tracing in the Real World (By Ross Anderson)
• The Price of the Coronavirus Pandemic (By Nick Paumgarten)
• Do We Need to Give Up Privacy to Fight the Coronavirus? (By Julia Angwin)
• Introducing the Principles of Equitable Disaster Response (By Greg Bloom)
• Cybersecurity During COVID-19 ( By Bruce Schneier)

This week, Hugo speaks with Sean Law about data science research and development at TD Ameritrade. Sean’s work on the Exploration team uses cutting edge theories and tools to build proofs of concept. At TD Ameritrade they think about a wide array of questions from conversational agents that can help customers quickly get to information that they need and going beyond chatbots. They use modern time series analysis and more advanced techniques like recurrent neural networks to predict the next time a customer might call and what they might be calling about, as well as helping investors leverage alternative data sets and make more informed decisions.

What does this proof of concept work on the edge of data science look like at TD Ameritrade and how does it differ from building prototypes and products? And How does exploration differ from production? Stick around to find out.

LINKS FROM THE SHOW

DATAFRAMED GUEST SUGGESTIONS

• DataFramed Guest Suggestions (who do you want to hear on DataFramed?)

FROM THE INTERVIEW

• Sean on Twitter
• Sean's Website
• TD Ameritrade Careers Page
• PyData Ann Arbor Meetup
• PyData Ann Arbor YouTube Channel (Videos)
• TDA Github Account (Time Series Pattern Matching repo to be open sourced in the coming months)
• Aura Shows Human Fingerprint on Global Air Quality

FROM THE SEGMENTS

Guidelines for A/B Testing (with Emily Robinson ~19:20)

• Guidelines for A/B Testing (By Emily Robinson)
• 10 Guidelines for A/B Testing Slides (By Emily Robinson)

Data Science Best Practices (with Ben Skrainka ~34:50)

• Debugging (By David J. Agans)
• Basic Debugging With GDB (By Ben Skrainka)
• Sneaky Bugs and How to Find Them (with git bisect) (By Wiktor Czajkowski)
• Good logging practice in Python (By Victor Lin)

Original music and sounds by The Sticks.

This week, Hugo speaks with Debbie Berebichez about the importance of critical thinking in data science. Debbie is a physicist, TV host and data scientist and is currently the Chief Data Scientist at Metis in NY.

In a world and a professional space plagued by buzz terms like AI, big data, deep learning, and neural networks, conversations around skill sets and less than productive programming language wars, what has happened to critical thinking in data science and data thinking in general?

What type of critical thinking skills are even necessary as data science, AI and machine learning become even more present in all of our lives and how spread out do they need to be across organizations and society? Listen to find out!


LINKS FROM THE SHOW

DATAFRAMED GUEST SUGGESTIONS

• DataFramed Guest Suggestions (who do you want to hear on DataFramed?)

FROM THE INTERVIEW

• Debbie on Twitter
• Debbie's Website
• Debbie Berebichez- Media Reel (Video)
• Deborah Berebichez' Keynote at Grace Hopper Celebration 2017 (Video)
• Debbie Berebichez on Perseverance and Paying it Forward (Video)
• Things about the Future and the Future of Things (By Debbie Berebichez, Video)

FROM THE SEGMENTS

Data Science tools for getting stuff done and giving it to the world (with Jared Lander ~21:55)

• Lander Analytics Website
• Docker Website
• plumber Website

Statistical Distributions and their Stories (with Justin Bois ~39:30)

• Probability distributions and their stories (By Justin Bois)
• The History of Statistics (By Stephen M. Stigler)
• The Evolution of the Normal Distribution (By Saul Stahl)

Original music and sounds by The Sticks.