DataFramed

To become a data-driven organization, it takes a major shift in mindset and culture, investments in technology and infrastructure, skills transformation, and clearly evangelizing the usefulness of using data to drive better decision-making. With all of these levers to scale, many organizations get stuck early in their data transformation journey, not knowing what to prioritize and how. In this epi...

During Data Literacy Month, we shared how data journalists curate and distill data stories to the wider public. Since 2020, Data Journalism has risen both in significance and visibility. Throughout the COVID-19 pandemic, data journalists have been instrumental in keeping the public informed by investigating, challenging, interpreting, and explaining complex datasets. In this episode, Betsy Ladyzhe...

Python has dominated data science programming for the last few years, but there’s another rising star programming language seeing increased adoption and popularity—Julia. As the fourth most popular programming language, many data teams and practitioners are turning their attention toward understanding Julia and seeing how it could benefit individual careers, business operations, and drive increase...

While securing the support of senior executives is a major hurdle of implementing a data transformation program, it’s often one of the earliest and easiest hurdles to overcome in comparison to the overall program itself. Leading a data transformation program requires thorough planning, organization-wide collaboration, careful execution, robust testing, and so much more. Vanessa Gonzalez is the Sen...

As data leaders continue to fill their talent gap, how should they approach sourcing, retaining, and upskilling their talent? What strategies should data leaders adopt in order to accomplish their talent goals and become data-driven? Kyle Winterbottom joins the show to talk about the key differentiators between data teams that build talent-dense teams and those that do not. Kyle is the host of Dri...

To improve Data Literacy, organizations need high-quality data training programs that give their employees the most valuable and relevant data skills they need. Many companies fall into the trap of implementing training programs that are poorly designed or not relevant for the needs of their learners. Sharon Castillo is the VP of Global Education at DataRobot , where she developed the DataRobot Un...

We have had many guests on the show to discuss how different industries leverage data science to transform the way they do business, but arguably one of the most important applications of data science is in space research and technology. Justin Fletcher joins the show to talk about how the US Space Force is using deep learning with telescope data to monitor satellites, potentially lethal space deb...

Throughout data literacy month, we’ve shined a light on the importance of data literacy skills and how it impacts individuals and organizations. Equally as important is how to actually approach transformational data literacy programs and ensure they are successful. In this final episode of Data Literacy Month, we are unpacking how CBRE is upskilling over 3,000 of its employees on data literacy ski...

Understanding and interpreting data visualizations are one of the most important aspects of data literacy. When done well, data visualization ensures that stakeholders can quickly take away critical insights from data. Moreover, data visualization is often the best place to start when increasing organizational data literacy, as it’s often titled the “gateway drug” to more advanced data skills. And...

Data Literacy may be an important skill for everyone to have, but the level of need is always unique to each individual. Some may need advanced technical skills in machine learning algorithms, while others may just need to be able to understand the basics. Regardless of where anyone sits on the skills spectrum, the data community can help accelerate their careers. There’s no one who knows that bet...

Data Literacy is increasingly becoming a skill that every role needs to have, regardless of whether their role a data-oriented or not. No one knows this better than Jordan Morrow , who is known as the Godfather of Data Literacy. Jordan is the VP and Head of Data Analytics at Brainstorm, Inc. , and is the author of Be Data Literate: The Skills Everyone Needs to Succeed. Jordan has been a fierce adv...

Taking inspiration from International Literacy Day on September 8, DataCamp is dedicating the whole month of September to raising awareness about Data Literacy. Throughout the month, we are featuring thought leaders and subject matter experts in order to get you Data Literacy, and we can’t wait for you to hear the exceptional guests we have lined up for you right here on DataFramed. Check out the...

Many times, data scientists can fall into the trap of resume-driven development. As in, learning the shiniest, most advanced technique available to them in an attempt to solve a business problem. However, this is not what a learning mindset should look like for data teams. As it turns out, taking a step back and focusing on the fundamentals and step-by-step iteration can be the key to growing as a...

Most companies experience the same pain point when working with data: it takes too long to get the right data to the right people. This creates a huge opportunity for data scientists to find innovative solutions to accelerate that process. One very effective method is to implement real-time data solutions that can increase business revenue and make it easier for anyone relying on the data to acces...

Machine learning models are often thought to be mainly utilized by large tech companies that run large and powerful models to accomplish a wide array of tasks. However, machine learning models are finding an increasing presence in edge devices such as smart watches. ML engineers are learning how to compress models and fit them into smaller and smaller devices while retaining accuracy, effectivenes...

Many machine learning practitioners dedicate most of their attention to creating and deploying models that solve business problems. However, what happens post-deployment? And how should data teams go about monitoring models in production? Hakim Elakhrass is the Co-Founder and CEO of NannyML , an open-source python library that allows users to estimate post-deployment model performance, detect data...

One of the biggest challenges facing the adoption of machine learning and AI in Data Science is understanding, interpreting, and explaining models and their outcomes to produce higher certainty, accountability, and fairness. Serg Masis is a Climate & Agronomic Data Scientist at Syngenta and the author of the book, Interpretable Machine Learning with Python . For the last two decades, Serg has...

Anjali Samani , Director of Data Science & Data Intelligence at Salesforce , joins the show to discuss what it takes to become a mature data organization and how to build an impactful, diverse data team. As a data leader with over 15 years of experience, Anjali is an expert at assessing and deriving maximum value out of data, implementing long-term and short-term strategies that directly enabl...

In 2020, OpenAI launched GPT-3, a large language AI model that is demonstrating the potential to radically change how we interact with software, and open up a completely new paradigm for cognitive software applications. Today’s episode features Sandra Kublik and Shubham Saboo, authors of GPT-3: Building Innovative NLP Products Using Large Language Models . We discuss what makes GPT-3 unique, trans...

While leading a mature data science function is a challenge in its own right, building one from scratch at an organization can be just as, if not even more, difficult. As a data leader, you need to balance short-term goals with a long-term vision, translate technical expertise into business value, and develop strong communication skills and an internalized understanding of a business's values and...

In pharmaceuticals, wrong decisions can not only cost a company revenue, but they can also cost people their lives. With stakes so high, it’s vital that pharmaceutical companies have robust systems and processes in place to accurately gather, analyze, and interpret data and turn it into actionable steps to solving health issues. Suman Giri is the Global Head of Data Science of the Human Health Div...

Building data science functions has become tables takes for many organizations today. However, before data science functions were needed, the finance function acted as the insights layer for many organizations over the past. This means that working in finance has become an effective entry point into data science function for professionals across all spectrums. Brian Richardi is the Head of Finance...

Democratizing data, and developing data culture in large enterprise organizations is an incredibly complex process that can seem overwhelming if you don’t know where to start. And today’s guest draws a clear path towards becoming data-driven. Meenal Iyer , Sr. Director for Data Science and Experimentation at Tailored Brands, Inc. , has over 20 years of experience as a Data and Analytics strategist...

When many people talk about leading effective Data Science teams in large organizations, it’s easy for them to forget how much effort, intentionality, vision, and leadership are involved in the process. Glenn Hofmann , Chief Analytics Officer at New York Life Insurance , is no stranger to that work. With over 20 years of global leadership experience in data, analytics, and AI that spans the US, Ge...

The healthcare industry presents a set of unique challenges for data science, including how to manage and work with sensitive patient information and accounting for the real-world impact of AI and machine learning on patient care and experience. Curren Katz , Senior Director for Data Science & Project Management at Johnson & Johnson , believes that despite challenges like these, there are...