In this episode of DataFramed, we speak with Vishnu V Ram, VP of Data Science and Engineering at Credit Karma about how data science is being leveraged to increase financial inclusion.

Throughout the episode, Vishnu discusses his background, Credit Karma’s mission, how data science is being used at Credit Karma to lower the barrier to entry for financial products, how he managed a data team through rapid growth, transitioning to Google Cloud, exciting trends in data science, and more. 

Relevant links from the interview:

• You can now learn data science with your team for free—try out DataCamp Professional with our 14-day free trial. 
• Data roles at Credit Karma
• Credit Karma’s mission

In this episode of DataFramed, we speak with Andy Cotgreave, Technical Evangelist at Tableau about the role of data storytelling when driving change with analytics, and the importance of the analyst role within a data-driven organization.

Throughout the episode, Andy discusses his background, the skills every analyst should know to equip organizations with better data-driven decision making, his best practices for data storytelling, how he thinks about data literacy and ways to spread it within the organization, the importance of community when creating a data-driven organization, and more.

Relevant links from the interview:

• We’d love your feedback! Let us know which topics you’d like us to cover and what you think of DataFramed by answering this 30-second survey
• Check out our upcoming webinar with Andy
• Check out Andy's book
• Become a Tableau expert

In this episode of DataFramed, we speak with Brian Campbell, Engineering Manager at Lucid Software about managing data science projects effectively and harnessing the power of collaboration. 

Throughout the episode, Brian discusses his background, how data leaders can become better collaborators, data science project management best practices, the type of collaborators data teams should seek out, the latest innovations in the data engineering tooling space, and more.

Relevant links from the interview:

• We’d love your feedback! Let us know which topics you’d like us to cover and what you think of DataFramed by answering this 30-second survey
• Lucid’s Tech Blog

In this episode of DataFramed, we speak with Shameek Kundu, former group CDO at Standard Chartered Bank, and Chief Strategy Officer & Head of Financial Services at TruEra Inc about Scaling AI Adoption throughout financial services.

Throughout the episode, Shameek discusses his background, the state of data transformation in financial services, the depth vs breadth of machine learning operationalization in financial services today, the challenges standing in the way of scalable AI adoption in the industry, the importance of data literacy, the trust and responsibility challenge of AI, the future of data science in financial services, and more.

Relevant links from the interview:

• We’d love your feedback! Let us know which topics you’d like us to cover and what you think of DataFramed by answering this 30-second survey
• Check out TruEra in action
• Bank of England Report: The impact of Covid on machine learning and data science in UK Banking
• MIT Tech Review — Hundreds of AI tools have been built to catch covid. None of them helped

In this episode of DataFramed, we speak with Syafri Bahar, VP of Data Science at Gojek about building high-performing data teams, and how data science is central to Gojek’s success. 

Throughout the episode, Syafri discusses his background, the hallmarks of a high-performance data team, how he measures the ROI on data activities, the skills needed in every successful data team, what is the best organizational model for data mature organizations, how Covid-19 affected Gojek’s data teams, his thoughts on data literacy and governance, future trends in data science and AI, and why data scientists should sharpen their maths and machine learning skills in an age of increasing automation. 

Relevant links from the interview:

• We’d love your feedback! Let us know which topics you’d like us to cover and what you think of DataFramed by answering this 30-second survey
• Gojek’s Data Blog

In this episode of DataFramed, we speak with Noah Gift, founder of Pragmatic AI Labs and prolific author about operationalizing machine learning in organizations and his new book Practical MLOPs. 

Throughout the episode, Noah discusses his background, his philosophy around pragmatic AI, the differences between data science in academia and the real world, how data scientists can become more action-oriented by creating solutions that solve real-world problems, the importance of dev-ops, his most recent book on the practical guide to MLOps, how data science can be compared to Brazilian jiu-jitsu, what data scientists should learn to scale the amount of value they deliver, his thoughts on auto-ml and automation, and more. 

Relevant links from the interview:

• We’d love your feedback! Let us know which topics you’d like us to cover and what you think of DataFramed by answering this 30-second survey
• Unsettled: What Climate Science Tells Us, What It Doesn't, and Why It Matters
• Check out Noah's books
• Check out Noah's course on DataCamp
• Connect with Noah on LinkedIn
• Gain access to DataCamp's full course library at a discount!

In this episode of DataFramed, we speak with Rick Scavetta and Boyan Angelov about their new book, Python and R for the Modern Data Scientist: The Best of Both Worlds, and how it dawns the start of a new bilingual data science community.  

Throughout the episode, Rick and Boyan discuss the history of Python and R, what led them to write the book, how Python and R can be interoperable, the advantages of each language and where to use it, how beginner data scientists should think about learning programming languages, how experienced data scientists can take it to the next level by learning a language they’re not necessarily comfortable with, and more. 

Relevant links from the interview:

• We’d love your feedback! Let us know which topics you’d like us to cover and what you think of DataFramed by answering this 30-second survey
• Check out Rick and Boyan’s book
• Check out Rick’s courses on DataCamp
• Check out Boyan's other books
• Connect with Rick on LinkedIn
• Connect with Boyan on LinkedIn

In this episode of DataFramed, we speak with Brent Dykes, Senior Director of Insights & Data Storytelling at Blast Analytics and author of Effective Data Storytelling: How to Turn Insights into Action on how data storytelling is shaping the analytics space. 

Throughout the episode, Brent talks about his background, what made him write a book on effective data storytelling, how data storytelling is often misinterpreted and misused, the psychology of storytelling and how humans are shaped to resonate with it, the role of empathy when creating data stories, the blueprint of a successful data story, what data scientists can do to become better data storytellers, the future of augmented analytics and data storytelling, and more. 

Relevant links from the interview:

• Connect with Brent on LinkedIn
• Register for Brent's Webinar on DataCamp
• Check out Brent's Book

In this episode of DataFramed, Adel speaks with Maria Luciana Axente, Responsible AI and AI for Good Lead at PwC UK on the state and future of responsible AI.

Throughout the episode, Maria talks about her background, the differences & intersections between "AI ethics" and "Responsible AI", the state of responsible AI adoption within organizations, the link between responsible AI and organizational culture, what data scientists can do today to ensure they're part of their organization's responsible AI journey, and more.

Relevant links from the interview:

• Connect with Maria on LinkedIn
• Kate Crawford's Atlas of AI
• 9 Ethical AI Principles for Organizations to Follow
• PwC's Responsible AI Toolkit
• Read our Data Literacy for Responsible AI White Paper

In this episode of DataFramed, Adel speaks with Alessya Visnjic, CEO and co-founder of WhyLabs,  an AI Observability company on a mission to build the interface between AI and human operators. 

Throughout the episode, Alessya talks about the unique challenges data teams face when operationalizing machine learning that spurred the need for MLOps, how MLOps intersects and diverges with different terms such as DataOps, ModelOps, and AIOps, how and when organizations should get started on their MLOps journey, the most important components of a successful MLOps practice, and more. 

Relevant links from the interview:

• Connect with Alessya on LinkedIn
• Andrew Ng on the important of being data-centric
• Joe Reis on the data culture and all things data
• whylogs: the standard for data logging — please send you feedback, contribute, help us build integrations into your favorite data tools and extend the concept of logging to new data types. Join the effort of building a new open standard for data logging!
• Try the WhyLabs platform