Podcasts about datacamp

As artificial intelligence (AI) reshapes the way we work, the need for employees to understand and use data has never been more important. According to recent research from DataCamp, 86% of leaders say data literacy is essential for their teams' daily tasks — and 62% say the same about AI literacy. But without these foundational skills, organizations risk falling behind. In this episode of The Business of Learning, we spoke with Dr. Bill Brantley, CPTM, president and chief learning officer at BAS2A, and Lauren LePage, learning strategist at Chick-fil-A  Tune in now to explore how L&D can build AI and data literacy across the workforce and gain insights on:  How to assess AI readiness and measure data literacy using tools like skills mapping and competency models. The critical role of leadership in modeling data-informed decision-making and creating a culture of ethical AI use. Key steps to begin building an AI- and data-literate workforce. 

Mówi się, że zanim zaczniemy biegać musimy nauczyć się chodzić. W tej mądrości ludowej kryje się wiele prawdy, którą można zastosować do nauki jakiegokolwiek zagadnienia, np. systemu kontroli wersji Git.Powierzchowna znajomość Gita i jego najpopularniejszych komend może nam zapewnić spokój na całkiem długi czas, ale w pewnym momencie zaczniemy dostrzegać trudności w radzeniu sobie z pewnymi sytuacjami. Niechlujne wpisy w historii zmian na pewno utrudnią nam ustalenie kto, kiedy i dlaczego coś zmienił, a brak znajomości podstawowych zagadnień i mechaniki Gita spowoduje, że nieraz poczujemy się zagubieni i bezradni.Rozmawiamy o tym jak zadbać o to, żeby nasza historia zmian była jasna i przejrzysta, a przez to przydatna i z jakiego zakresu uzupełnić wiedzę teoretyczną o Gicie, a także dzielimy się wskazówkami na temat przydatnych ustawień i komend.Dźwięki wykorzystane w audycji pochodzą z kolekcji "107 Free Retro Game Sounds" dostępnej na stronie https://dominik-braun.net, udostępnianej na podstawie licencji Creative Commons license CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).Informacje dodatkowe:Git: https://git-scm.com/"How to Write a Git Commit Message", cbeams: https://cbea.ms/git-commit/"Git turns 20: A Q&A with Linus Torvalds", GitHub: https://github.blog/open-source/git/git-turns-20-a-qa-with-linus-torvalds/"How did Git get its name?", Initial Commit: https://initialcommit.com/blog/How-Did-Git-Get-Its-NameConventional Commits: https://www.conventionalcommits.org/en/v1.0.0/"Darwin Information Typing Architecture (DITA)", Wikipedia: https://pl.wikipedia.org/wiki/Darwin_Information_Typing_Architecture"Git Squash Commits: A Guide With Examples", DataCamp: https://www.datacamp.com/tutorial/git-squash-commits"How to Create and Push an Empty Commit in Git", Tower FAQ: https://www.git-tower.com/learn/git/faq/git-empty-commit"8.1 Customizing Git - Git Configuration", Git: https://git-scm.com/book/en/v2/Customizing-Git-Git-Configuration

How can product leaders approach bridging the gap between engineering and product management? In this podcast hosted by Nacho Andrade, DataCamp's Chief Product and Technology Officer Eduardo Oliveira will be speaking on bridging the gap between engineering and product management. Oliveira shares his unique perspective on transitioning from an engineering background into a product leadership role, and the key lessons he's learned about identifying and addressing critical product gaps to drive business impact.

We are back with another installment of our Exploring Tech Job Series where we dig into different technical roles to provide insight into what that role is all about. This week we are chatting about Data Engineering and we are joined by a real-life Data Engineer, Rosie! Join us as we chat about what a Data Engineer is, the day-to-day of the job, how you might get into Data Engineering, and so much more. New episodes come out fortnightly on Wednesday morning (NZT). Check out Datacamp to learn more about data engineering Where to Find Us: ⁠ ⁠Instagram⁠ ⁠Tik Tok⁠ ⁠The Hot Girls Code Website⁠⁠ Sponsored by: ⁠Trade Me Jobs⁠

Richie Cotton and his co-host Adel have published over 150 episodes of the DataFramed podcast with some amazing guests. Richie's also a data evangelist for  DataCamp and a data scientist by training. He helps train DataCamp students and has been instrumental in the company's success. DataCamp has trained data teams at more than 2,500 companies, including 80 percent of the Fortune 100. Richie has degrees in Math from the University of Warwick and the University of York. In our conversation, we discuss: The Evolving Role of Data Scientists: Richie Cotton shares insights into the changing landscape of data science and how the role of data scientists is expected to transform in the next decade, emphasizing the increasing accessibility of powerful tools.Ethical Considerations in AI: we delve into the ethical implications of artificial intelligence, exploring the responsibility of data scientists and AI practitioners in addressing issues such as bias, transparency, and privacy in their work.Future Skills for Data Scientists: Richie discusses the essential technical skills for data scientists, including the ongoing debate between Python and R, and highlights the importance of staying adaptable as technology evolves.AI's Impact on Business Operations: we explore the potential impact of AI on various industries, focusing on business operations and efficiency, providing valuable insights for professionals looking to harness AI for strategic advantages.Predictions for Artificial General Intelligence (AGI): Richie shares intriguing perspectives on the timeline for achieving artificial general intelligence, citing predictions that AGI might become a reality in the 2030s and discussing the potential implications of such advancements.Teaching and Learning Data Science at Scale: we shed light on Richie's experience at DataCamp, where he has been instrumental in teaching data science to hundreds of thousands of individuals, highlighting the platform's mission to make data science education accessible globally.Additional resourcesListen to Richie's podcast — https://www.datacamp.com/podcastOther episodes you might enjoy — https://aiandthefutureofwork.buzzsprout.com/520474/14341487-navrina-singh-founder-ceo-of-credo-ai-discusses-ai-governance-ethics

Mari Nazary is the Chief Experience Officer at Bloom Institute of Technology, formerly known as Lambda School, Mari has dedicated over 15 years to transforming the landscape of online education. She believes in making education a universal key, accessible and effective for all, without the financial burden often associated with higher learning.Mari's journey in EdTech began with her role at Rosetta Stone, where she developed the company's first online classroom experience. Her passion for instructional design and product development led her to impactful positions at Education First, Voxy, and DataCamp. In these roles, she spearheaded the expansion of learning products, significantly impacting millions of learners worldwide.At BloomTech, Mari has been instrumental in redefining the education experience. Her initiatives include building a proprietary platform, reducing the cost per learner, and incorporating an AI tutor, significantly enhancing the learning process and improving outcomes. She oversees critical aspects like product development, data management, instructional design, operations, and customer support, with a focus on results-driven learning experiences that cater to a diverse range of students.Mari's background as the daughter of Afghan immigrants in Queens, NY, has profoundly influenced her perspective on education. She holds degrees in classics, Spanish, and linguistics and is a fervent advocate for making tech careers accessible to those traditionally underserved by higher education. Living in Washington, D.C., Mari continues to challenge the status quo, ensuring that education paves a direct path to greater opportunities and success.Recommended Resources:AI for Education blogLenny's Podcast

!!WARNING!! Due to some technical issues the volume is not always constant during the show. I sincerely apologise for any inconvenience Francesco     In this episode, I speak with Richie Cotton, Data Evangelist at DataCamp, as he delves into the dynamic intersection of AI and education. Richie, a seasoned expert in data science and the host of the podcast, brings together a wealth of knowledge and experience to explore the evolving landscape of AI careers, the skills essential for generative AI technologies, and the symbiosis of domain expertise and technical skills in the industry.   References Become a generative AI developer in this FREE code-along series. Learn to build a chatbot using the OpenAI API, the Pinecone API, and LangChain, and learn to build NLP and image applications with Hugging Face. https://www.datacamp.com/ai-code-alongs Learn to use ChatGPT and the OpenAI API in the OpenAI Fundamentals skill track. https://www.datacamp.com/tracks/openai-fundamentals Get started with deep learning using PyTorch in the Introduction to Deep Learning with PyTorch course. https://www.datacamp.com/courses/introduction-to-deep-learning-with-pytorch    

Las violentas protestas que vivió Chile de manera sistemática desde 2019 generaron una desestabilización social y política para la democracia de este país. Para analizar el contexto que vive Chile, estuvo con nosotros Aldo Cassinelli, reconocido académico, escritor y actual Director Ejecutivo de Estudio de Opinión DataCamp. Autor del libro "Un sistema político para el Chile que viene", publicación que recientemente lo hizo merecedor del galardón al mejor libro político del año, premio otorgado durante el Victory Napolitan Award 2023, el pasado mes de agosto en la ciudad de Washington DC. ¿Por qué los chilenos votaron NO a la nueva Constitución? ¿Cómo la migración, inseguridad e inestabilidad política están afectando a Chile? ¿Cuál es el pronóstico para el Plebiscito que tendrán los chilenos este domingo 17 de diciembre? Conozca todos los detalles durante esta interesante conversación sobre Chile y América Latina. Libro: "Un sistema político para el Chile que viene" - https://www.buscalibre.us/libro-un-sistema-politico-para-el-chile-que-viene/9789566121039/p/54298780

What is data and AI literacy, and why is it central to DataCamp's mission? In this episode, our host, Alexandra Ebert, MOSTLY AI's Chief Trust Officer, had the chance to talk to truly like-minded people. DataCamp's CEO and co-founder, Jo Cornelissen, and Maggie Remynse, VP of Curriculum, have both seen firsthand how transformative knowledge and access to data is.  If you are interested in Data and AI literacy, make sure you check out the vast resources DataCamp is going to share throughout September 2023 during their annual Data and AI Literacy Month. There are top-notch experts sharing their knowledge during webinars, podcast episodes, and even a virtual conference on September 28th. And the best of all, it's completely free of charge. Sign up here: https://bit.ly/3sMu8pJ 

We catch up with Christos Makridis to talk about music, blockchain, Nashville, and how he sees Living Opera as a positive way to help artists and musicians in particular. Bio: Christos A. Makridis is a research affiliate at Columbia Business School, Stanford University and the University of Nicosia. He is COO of Living Opera, and CEO/founder of Dainamic, a startup that aims to democratize access to AI for mid and small sized banks. Christos serves as a Research Professor at the W. P. Carey School of Business and Research Affiliate at the Global Security Initiative (both in Arizona State University), an Adjunct Associate Research Scholar at the Chazen Institute in Columbia Business School, a Digital Fellow at the Digital Economy Lab in Stanford University, a Non-resident Fellow at the Institute for Religious Studies at Baylor University, an Adjunct Scholar at the Manhattan Institute, a Senior Adviser at Gallup, a policy adviser, and an entrepreneur. He is the CEO/co-founder of Dainamic, a technology startup working to democratize the use and application of data science and AI techniques for small and mid sized organizations, and CTO/co-founder of Living Opera, a web3 startup working to bridge classical music and blockchain technologies. Christos previously served on the White House Council of Economic Advisers managing the cybersecurity, technology, and space activities, as a Non-resident Fellow at the Cyber Security Project in the Harvard Kennedy School of Government, as a Digital Fellow at the Initiative at the Digital Economy in the MIT Sloan School of Management, a a Non-resident Research Scientist at Datacamp, and as a Visiting Fellow at the Foundation for Defense of Democracies. Christos' primary academic research focuses on labor economics, the digital economy, and personal finance and well-being. He also writes frequently for syndicated outlets in the press and serves on the Council of Advisers for the National Center on Sexual Exploitation. Christos earned a Bachelor's in Economics and Minor in Mathematics at Arizona State University, as well a dual Masters and PhDs in Economics and Management Science & Engineering at Stanford University. About Living Opera Founded by two opera singers and an economist, Living Opera is a multimedia art-technology company that unites the classical music and blockchain communities to produce transformative content. Living Opera takes a holistic approach to life, work, and education: “living” means “full of life and vigor,” and “opera” means (in Latin) “labor, effort, attention, or work.” Living Opera NFT collections, such as Magic Mozart, are designed to bring the art and tech worlds together by expanding the audience of people who traditionally engage with classical music and fine art.

You don't need to be a scientist to unlock the insights in your data. In fact, DataCamp's Maggie Remynse and CBRE's HoChun Ho say the secret to using data is hard-earned industry experience.

In 2022, we saw significant developments in the field of data. From the emergence of generative AI to the growth of low-code data tools and AI assistants—these advancements signal an upcoming paradigm shift, where data-powered tools and machine learning systems will radically transform workflows across various professions.2022 also saw digital transformation remain a major theme for organizations across industries as they sought to embrace new ways of working, reaching customers, and providing value. As 2023's looming economic uncertainty puts pressure on organizations to maximize ROI from their investments, digital and data transformation will continue to be one of the key levers by which organizations can cut costs and scale value for their stakeholders.So we've invited DataCamp's co-founders, CEO Jonathan Cornelissen and COO Martijn Theuwissen to break down the top data trends they are seeing in the data space today, as well as their predictions for the future of the data industry.Jonathan Cornelissen is the CEO and co-founder of DataCamp. As the CEO of DataCamp, he helped grow DataCamp to upskill over 10M+ learners and 2800+ teams and enterprise clients. He is interested in everything related to data science, education and entrepreneurship. He holds a PhD in financial econometrics, and was the original author of an R package for quantitative finance.Martijn Theuwissen is the COO and co-founder of DataCamp. As the COO of DataCamp, he helps DataCamp's enterprise clients on their data and digital transformation strategies, enabling them to make the most of DataCamp for Business's offering, and helping them transform how their workforce uses data.

In programming, collaboration and experimentation can be very stressful, since sharing code and making it visible to others can be tedious, time-consuming, and nerve-wracking.Tools like Power BI are changing that entirely, by opening up new ways to collaborate between team members, add layers of customized and complex security to the data teams are working with, and making data much more accessible across organizations. Ginger Grant joins the show to talk about how organizations can utilize Power BI, Dax, and M to their fullest potential and create new opportunities for experimentation, innovation, and collaboration. Ginger is the Principal Consultant at the Desert Isle Group, working as an expert in advanced analytic solutions, including machine learning, data warehousing, ETL, reporting and cube development, Power BI, Excel Automation, Data Visualization and training. In addition to her consultant work, she is also a blogger at and global keynote speaker on developments and trends in data. Microsoft has also recognized her technical contributions by awarding her a MVP in Data Platform. In this episode, we talk about what Power BI is, the common mistakes organizations make when implementing Power BI, advanced use cases, and much more.

The insurance industry thrives on data from utilizing data and analytics to determine policy rates for customers to working with relevant partners in the industry to improve their products and services, data is embedded in everything that insurance companies do. But insurance companies also have a number of hurdles to overcome, whether it's transitioning legacy data into new processes and technology, balancing new projects and models with ever-changing regulatory standards, and balancing the ethical considerations of how to best utilize data without resulting in unintended consequences for the end user. That's why we've brought Rob Reynolds onto the show. Rob is the VP and Chief Data & Analytics Officer at W. R. Berkley, a multinational insurance holding company specializing in property and casualty insurance. Rob brings over two decades of experience in Data Science, IT, and technology leadership, with a particular expertise in building departments and establishing highly functioning teams, especially in highly dynamic environments. In this episode, we talk in-depth about how insurance companies utilize data, the most important skills for anyone looking for data science jobs in the insurance industry, why the need for thoughtful criticism is growing in data science, and how an expertise in communication will put you ahead of the pack.

With the increasing rate at which new data tools and platforms are being created, the modern data stack risks becoming just another buzzword data leaders use when talking about how they solve problems. Alongside the arrival of new data tools is the need for leaders to see beyond just the modern data stack and think deeply about how their data work can align with business outcomes, otherwise, they risk falling behind trying to create value from innovative, but irrelevant technology. In this episode, Yali Sassoon joins the show to explore what the modern data stack really means, how to rethink the modern data stack in terms of value creation, data collection versus data creation, and the right way businesses should approach data ingestion, and much more. Yali is the Co-Founder and Chief Strategy Officer at Snowplow Analytics, a behavioral data platform that empowers data teams to solve complex data challenges. Yali is an expert in data with a background in both strategy and operations consulting teaching companies how to use data properly to evolve their operations and improve their results.

2022 was an incredible year for Generative AI. From text generation models like GPT-3 to the rising popularity of AI image generation tools, generative AI has rapidly evolved over the last few years in both its popularity and its use cases. Martin Musiol joins the show this week to explore the business use cases of generative AI, and how it will continue to impact the way the society interacts with data. Martin is a Data Science Manager at IBM, as well as Co-Founder and an instructor at Generative AI, teaching people to develop their own AI that generates images, videos, music, text and other data. Martin has also been a keynote speaker at various events, such as Codemotion Milan. Having discovered his passion for AI in 2012, Martin has turned that passion into his expertise, becoming a thought leader in AI and machine learning space. In this episode, we talk about the state of generative AI today, privacy and intellectual property concerns, the strongest use cases for generative AI, what the future holds, and much more.

Data Analytics has played a major role in Chelsea's journey to becoming the seventh most valuable football club in the world, Chelsea has won six league titles, eight FA Cups, five League Cups, and two Champions League titles. Today, we are going behind the scenes at Chelsea FC to see how they use data analytics to analyze matches, inform tactical decision-making, and drive matchday success in one of the world's top football leagues, just in time for the 2022 FIFA World Cup in Qatar! Federico Bettuzzi is a Data Scientist at Chelsea FC. As a specialist in match analytics, Federico works with Chelsea's first team to inform tactical decision making during matches. Federico joins the show to break down how he gathers and synthesizes data, how they develop match analyses for tactical reviews, how managers prioritize data analytics differently, how to balance long-term and short-term projects, and much more.

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 episode, Ganes Kesari joins the show to share the frameworks and processes that organizations can follow to become data-driven, measure their data maturity, and win stakeholder support across the organization. Ganes is Co-Founder and Chief Decision Scientist at Gramener, which helps companies make data-driven decisions through powerful data stories and analytics. He is an expert in data, analytics, organizational strategy, and hands-on execution. Throughout his 20-year career, Ganes has become an internationally-renowned speaker and has been published in Forbes, Entrepreneur, and has become a thought leader in Data Science. Throughout the episode, we talk about how organizations can scale their data maturity, how to build an effective data science roadmap, how to successfully navigate the skills and people components of data maturity, and much more.

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 Ladyzhets joins the show to talk about the state of Data Journalism today, and shares from her experience as a data journalist Betsy is an independent science, health, and data journalist focused on COVID-19 and Founder of the COVID-19 Data Dispatch, an independent publication providing updates and resources on public COVID-19 data. She is also currently working as a Senior Journalism Fellow with the Documenting COVID-19 project at the Brown Institute for Media Innovation and MuckRock. Her work has been featured in Science News, FiveThirtyEight, MIT Tech Review, and the Covid Tracking Project. Throughout the show, we discuss the importance of letting data shape a narrative, what characteristics of traditional journalism are needed for data journalists, the best practices for delivering effective data stories, how the rise of AI and data visualization are impacting data journalism, and much more. Links shared during the episode: Data Sonification The COVID-19 Data Dispatch The Data Visualization Society Learning on DataCamp? Take part in this week's XP-challenge: http://www.datacamp.com/promo/free-week-xp-challenge-2022

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 increased value across organizations. Zacharias Voulgaris, PhD joins the show to talk about his experience with the Julia programming language and his perspective on the future of Julia's widespread adoption. Zacharias is the author of Julia for Data Science. As a Data Science consultant and mentor with 10 years of international experience that includes the role of Chief Science Officer at three startups, Zacharias is an expert in data science, analytics, artificial intelligence, and information systems. In this episode, we discuss the strengths of Julia, how data scientists can get started using Julia, how team members and leaders alike can transition to Julia, why companies are secretive about adopting Julia, the interoperability of Julia with Python and other popular programming languages, and much more. Check out this month's events: https://www.datacamp.com/data-driven-organizations-2022 Take the Introduction to Julia course for free! https://www.datacamp.com/courses/introduction-to-julia

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 Senior Director of Data and Analytics for ML & AI at Transamerica. Vanessa has experience in data transformation, leadership, and strategic direction for Data Science and Data Governance teams, and is an experienced senior data manager. Vanessa joins the show to share how she is helping to lead Transamerica's Data Transformation program. In this episode, we discuss the biggest challenges Transamerica has faced throughout the process, the most important factors to making any large-scale transformation successful, how to collaborate with other departments, how Vanessa structures her team, the key skills data scientists need to be successful, and much more. Check out this month's events: https://www.datacamp.com/data-driven-organizations-2022

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 Driven by Data: The Podcast, the Founder & CEO of Orbition, a talent solutions provider, for scaling Data, Analytics, & Artificial Intelligence teams across the UK, Europe and the USA. As an accomplished expert and thought leader in talent acquisition, attraction, and retention, as well as scaling data teams, Kyle was named one of Data IQ's 100 Most Influential People in Data for 2022. In this episode, we talk about how data teams can position themselves to attract top talent, how to properly articulate how data team members are adding value to the business, how organizations can accidentally set data leaders up to fail, how to approach upskilling, and how data leaders can create an employer branding narrative to attract top talent. Check out this month's events: https://www.datacamp.com/data-driven-organizations-2022

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 University, a self-service education portal that features both free and paid courses on AI and machine learning that are available to the public. With over 30 years of experience, Sharon is a leading expert in data training and employee upskilling programs, from development through execution. Sharon joins the show to talk about what makes an effective data training program, how to ensure employees retain the information, how to properly incentivize training participation, why organizations should prioritize training, and much more. This is essential listening for anyone developing a training program for their team or organization.

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 debris, and identify and prevent catastrophic collisions. Justin is responsible for artificial intelligence and autonomy technology development within the Space Domain Awareness Delta of the United States Space Force Space Systems Command. With over a decade of experience spanning space domain awareness, high performance computing, and air combat effectiveness, Justin is a recognized leader in defense applications of artificial intelligence and autonomy. In this episode, we talk about how the US Space Force utilizes deep learning, how the US Space Force publishes its research and data to find high-quality peer review, the must-have skills aspiring practitioners need in order to pursue a career in Defense, and much more.

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 skills through a relevant, high-value learning program. Emily Hayward is the Data and Digital Change Manager at CBRE, a global leader in commercial real estate services and investment. Emily is a transformational leader with a track record of leading successful high-profile technology, data, and cultural transformations across both the public and private sectors through an ardent belief that change cannot be achieved without first winning people over. Throughout the episode, we talk about Emily's approach to building CBRE's learning program, effective change management, why it's critical to secure executive sponsorship, and much more. Looking to build a data literacy program of your own? Check out DataCamp for Business: https://bit.ly/3r7BgsF

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. Andy Cotgreave, Senior Data Evangelist at Tableau Software and co-author of The Big Book of Dashboards, joins the show to break down data visualization and storytelling, drawing from his 15-year career in the data space. Andy has spoken for events like SXSW, Visualized, and Tableau's conferences and has inspired thousands of people to develop their data skills. In this episode, we discuss why data visualization skills are so essential, how data visualization increases organizational data literacy, the best practices for visual storytelling, and much more. This episode of DataFramed is a part of DataCamp's Data Literacy Month, where we raise awareness about Data Literacy throughout September through webinars, workshops, and resources featuring thought leaders and subject matter experts that can help you build your data literacy, as well as your organization's. For more information, visit: https://www.datacamp.com/data-literacy-month/for-teams

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 better than Kate Strachnyi. Kate is the Founder and Community Manager at DATAcated, a company that is focused on bringing data professionals together and helping data companies reach their target audience through effective content strategies. Kate has created courses on data storytelling, dashboard and vizualization best practices, and she is also the author of several books on data science, including a children's book about data literacy. Through her professional accomplishments and her content efforts online, Kate has not only built a massive online following, she has also established herself as a leader in the data space. In this episode, we talk about best practices data vizualization, the importance of technical skills and soft skills for data professionals, how to build a personal brand and overcome Imposter Syndrome, how data literacy can make or break organizations, and much more. This episode of DataFramed is a part of DataCamp's Data Literacy Month, where we raise awareness for Data Literacy throughout the month of September through webinars, workshops, and resources featuring thought leaders and subject matter experts that can help you build your data literacy, as well as your organization's. For more information, visit: https://www.datacamp.com/data-literacy-month/for-teams

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 advocate for data literacy throughout his career, including helping the United Nations understand and utilize data literacy effectively. Throughout the episode, we define data literacy, why organizations need data literacy in order to use data properly and drive business impact, how to increase organizational data literacy, and more. This episode od DataFramed is a part of DataCamp's Data Literacy Month, where we raise awareness for Data Literacy throughout the month of September through webinars, workshops, and resources featuring thought leaders and subject matter experts that can help you build your data literacy, as well as your organization's. For more information, visit: https://www.datacamp.com/data-literacy-month/for-teams

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 full lineup of events.

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 data scientist, because when data teams develop a strong understanding of the problems and solutions lying underneath the surface, they will be able to wield their tools with complete mastery. Ella Hilal joins the show to share why operating from an always-learning mindset will open up the path to a true mastery and innovation for data teams. Ella is the VP of Data Science and Engineering for Commercial and Service Lines at Shopify, a global commerce leader that helps businesses of all size grow, market, and manage their retail operations. Recognized as a leading woman in Data science, Internet of things and Machine Learning, Ella has over 15 years of experience spanning multiple countries, and is an advocate for responsible innovation, women in tech, and STEM. In this episode, we talk about the biggest mistakes data scientists make when solving business problems, how to create cohesion between data teams and the broader organization, how to be an effective data leader that prioritizes their team's growth, and how developing an always-learning mindset based on iteration, experimentation, and deep understanding of the problems needing to be solved can accelerate the growth of data teams.

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 access the data they need, understand it, and make accurate decisions with it. George Trujillo joins the show to share how he believes real-time data has the potential to completely transform the way companies work with data. George is the Principal Data Strategist at DataStax, a tech company that helps businesses scale by mobilizing real-time data on a single, unified stack. With a career spanning 30 years and companies like Charles Schwab, Fidelity Investments, and Overstock.com, George is an expert in data-driven executive decision-making and tying data initiatives to tangible business value outcomes. In this episode, we talk about the real-world use cases of real-time analytics, why reducing data complexity is key to improving the customer experience, the common problems that slow data-driven decision-making, and how data practitioners can start implementing real-time data through small high-value analytical assets.

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, effectiveness, and efficiency. The goal is to empower domain experts in any industry around the world to effectively use machine learning models without having to become experts in the field themselves. Daniel Situnayake is the Founding TinyML Engineer and Head of Machine Learning at Edge Impulse, a leading development platform for embedded machine learning used by over 3,000 enterprises across more than 85,000 ML projects globally. Dan has over 10 years of experience as a software engineer, which includes companies like Google (where he worked on TensorFlow Lite) and Loopt, and co-founded Tiny Farms America's first insect farming technology company. He wrote the book, "TinyML," and the forthcoming "AI at the Edge". Daniel joins the show to talk about his work with EdgeML, the biggest challenges facing the field of embedded machine learning, the potential use cases of machine learning models in edge devices, and the best tips for aspiring machine learning engineers and data science practitioners to get started with embedded machine learning.

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 drift, and link data drift alerts back to model performance changes. Originally, Hakim started a machine learning consultancy with his NannyML co-founders, and the need for monitoring quickly arose, leading to the development of NannyML. Hakim joins the show to discuss post-deployment data science, the real-world use cases for tools like NannyML, the potentially catastrophic effects of unmonitored models in production, the most important skills for modern data scientists to cultivate, and more.

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 been at the confluence of the internet, application development, and analytics. Serg is a true polymath. Before his current role, he co-founded a search engine startup incubated by Harvard Innovation Labs, was the proud owner of a Bubble Tea shop, and more. Throughout the episode, Serg spoke about the different challenges affecting model interpretability in machine learning, how bias can produce harmful outcomes in machine learning systems, the different types of technical and non-technical solutions to tackling bias, the future of machine learning interpretability, and much more.

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 enable positive business outcomes, and how you can do the same. You will learn the hallmarks of a mature data organization, how to measure ROI on data initiatives, how Salesforce implements its data science function, and how you can utilize strong relationships to develop trust with internal stakeholders and your data team.

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, transformative use-cases it has ushered in, the technology powering GPT-3, its risks and limitations, whether scaling models is the path to “Artificial General Intelligence”, and more. Announcement For the next seven days, DataCamp Premium and DataCamp for Teams are free. Gain free access by following going here.

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 goals in order to earn trust with key stakeholders and build the right team. Elettra Damaggio is no stranger to this process. Elettra is the Director for Global Data Science at StoneX, an institutional-grade financial services network that connects clients to the global markets ecosystem. Elettra has over 10 years of experience in machine learning, AI, and various roles within digital transformation and digital business growth. In this episode, she shares how data leaders can balance short-term wins with long-term goals, how to earn trust with stakeholders, major challenges when launching a data science function, and advice she has for new and aspiring data practitioners.

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 Division at Merck, a biopharmaceutical research company that works to develop innovative health solutions for both people and animals. Suman joins the show today to share how Merck is using data to improve organizational decision-making, medical research outcomes, and how data science is transforming the pharmaceutical industry at scale. He also shares some of the biggest challenges facing the industry right now and what new trends are on the horizon.

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 Data Science and Analytics at Stryker, a medical equipment manufacturing company based in Michigan, US. Brian brings over 14 years of global experience to the table. At Stryker, Brian leads a team of data scientists that use business data and machine learning to make predictions for optimization and automation. In this episode, Brian talks about his experience as a data science leader transitioning from Finance, how he utilizes collaboration and effective communication to drive value, how leads the data science finance function at Stryker, and what the future of data science looks like in the finance space, and more.

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. She has built several data and analytics platforms and drives the enterprises she works with to be insights-driven. Meenal has also led data teams at various retail organizations, and as a wide variety of specialties in Data Science, including data literacy programs, data monetization, machine learning, enterprise data governance, and more. In this episode, Meenal shares her thorough, effective, and clear strategy for democratizing data successfully and how that helps create a successful data culture in large enterprises, and gives you the tools you need to do the same in your organization. [Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there's definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

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, Germany, and South Africa, Glenn knows firsthand what it takes to build an effective data science function within a large organization. In this episode, we talk about how he built NeW York Life Insurance's 50-person data science and AI function, how they utilize skillsets to offer different career paths for data scientists, building relationships across the organization, and so much more. [Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there's definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

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 massive opportunities for data science and machine learning to increase care quality, drive business objectives, diagnose diseases earlier, and ultimately save countless lives around the world. Curren has over 10 years of leadership experience across both the US and Europe and has led more than 20 successful data science product launches in the payer, provider, and pharmaceutical spaces. She also brings her background as a cognitive neuroscientist to data science, with research in neural networks, connectivity analysis, and more. [Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there's definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

Today marks the last episode of our four-part DataFramed Careers Series on breaking into a data career. We've heard from Sadie St Lawrence, Nick Singh, and Khuyen Tran on best practices to adopt to help you land a data science interview. But what about the interview itself? Today's guest, Jay Feng, joins the show to break down all the most important things you need to know about interviewing for data science roles. Jay is the co-founder of Interview Query, which helps data scientists, machine learning engineers, and other data professionals prepare for their dream jobs. Throughout the episode, we discuss The anatomy of data science interviews Biggest misconceptions and mistakes candidates make during interviews The importance of showcasing communication ability, business acumen, and technical intuition in the interview How to negotiate for the best salary possible [Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there's definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

Today is the third episode of this four-part DataFramed Careers series being published every day this week on building a career in data. We've heard from Nick Singh on the importance of portfolio projects, as well as the distinction between content-based and coding-based portfolio projects. When looking to get started with content-based projects, how do you move forward with getting yourself out there and sharing the work despite being a relative beginner in the field?Today's guest tackles exactly this subject. Khuyen Tran is a developer advocate at prefect and a prolific data science writer. She is the author of the book “Efficient Python Tricks and Tools for Data Scientists” and has written 100s of blog-articles and tutorials on key data science topics, amassing thousands of followers across platforms. Her writing has been key to accelerating here data career opportunities. Throughout the episode, we discuss: How content creation accelerates the careers of aspiring practitioners The content creation process How to combat imposter syndrome What makes content useful Advice and feedback for aspiring data science writers   Resources mentioned in the episode: Analyze and Visualize URLs with Network Graph Show Your Work by Austin Cloud Mastery by Robert Greene Deep Questions with Cal Newport Podcast   [Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there's definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

Today marks the second episode in our DataFramed Careers Series. In this series, we will interview a diverse range of thought leaders and experts on the different aspects of landing a data role in 2022. In the first episode of the series, Sadie discussed at great length the importance of having a solid data science portfolio to land a role in data. But what makes a great data science portfolio? Nick Singh, co-author of Acing the Data Science Interview, joins the show to share everything you need to know to create high-quality, thorough portfolio projects. Throughout the episode, we discuss How portfolio projects build experience Who should be focusing on portfolio projects The different types of portfolio projects Biggest pitfalls when creating portfolio projects How to get noticed with your portfolio projects Concrete examples of great portfolio projects  [Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there's definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

Today is the start of a four-day careers series covering breaking into data science in 2022. With so so much demand for data jobs today, we wanted to demystify the ins and outs of accelerating a career in data. In this series, we will interview a diverse range of thought leaders and experts on the different aspects of standing out from the crowd in the job hunt. Our first guest in the DataFramed Careers Series is Sadie St. Lawrence. Sadie St Lawrence is the Founder and CEO of Women in Data, the #1 Community for Women in AI and Tech. Women in Data is a community of over 20,000 individuals and has representation in 17 countries and 50 cities. She has trained over 350,000 people in data science and is the course developer for the Machine Learning Certification for UC Davis. In addition, she serves on multiple start-up boards, and is the host of the Data Bytes podcast. Sadie joins the show to talk about her career journey in data science and shares the best lessons she has learned in launching data careers. Throughout the episode, we discuss The different types of data career paths available How to break into your data science career How to build strong mentor/mentee relationships Best practices to stand out in a competitive industry Building a strong resume and standing out from the crowd  [Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there's definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

Introducing the DataFramed Careers Series. Over the past year hosting the DataFramed podcast, we've had the incredible privilege of having biweekly conversations with data leaders at the forefront of the data revolution. This has led to fascinating conversations on the future of the modern data stack, the future of data skills, and how to build organizational data literacy.  However, as the DataFramed podcast grows, we want to be able to provide the data science community across the spectrum from practitioners to leaders, with distilled insights that will help them manoeuvre their careers effectively. And we want to do that more often.  This is why we're excited to announce the launch of a four-day DataFramed Careers Series. Throughout next week, we will interview four different thought leaders and experts about what it takes to break into data science in 2022, best practices to stand out from the crowd, building a brand in data science, and more. Moreover, this episode series will mark DataFramed's transition from biweekly to weekly. Starting Monday the 30th of May, DataFramed will become a weekly podcast. For next week's DataFramed Careers Series, we'll be covering the ins and outs of building a career in data, and the different aspects of standing out from the crowd during the job hunt. We'll be hearing from Sadie St Lawrence, CEO and Founder of Women in Data on what it takes to launch a data career in 2022. Nick Singh, Co-author of Ace the Data Science Interview and 2nd time guest of DataFramed will join us to discuss what makes a great data science portfolio project. Khuyen Tran, Developer Advocate at Prefect on will outline how writing can accelerate a data career, and Jay Feng, CEO of Interview Query will join us to provide tips and frameworks on acing the data science interview. For future DataFramed episodes, we'll definitely still cover the different aspects of building a data-driven organization, cover the latest advancements in data science, building data careers, and more. So expect more varied guests, topics, and more specials series like this one in the future.

Data literacy at any organization takes buy-in from all levels of the company, from C-suite leaders all the way to customer-facing team members. But how do you get that buy-in, build a team around data literacy, and transform the way your company works with data? Today's guest, Megan Brown, Director of Data Literacy and Knowledge Management at Starbucks, discusses what they have done to forge data culture and data literacy at Starbucks. Throughout the episode, we discuss How to increase data literacy in an organization How to secure executive sponsorship for data initiatives The importance of user experience research in building data literacy  Balancing short-term business needs with long-term strategic upskilling Humanizing machine learning and AI within the organization 

Diversity in both skillset and experience are at the core of high-impact data teams, but how can you take your data team's impact to the next level with subject matter expertise, attention to user experience, and mentorship? Today's guest, Dan Kellet, Chief Data Officer at Capital One UK, joins us to discuss how he scaled Capital One's data team. Throughout the episode, we discuss: The hallmarks of a high-impact data team The importance of skills and background diversity when building great data teams The importance of UX skills when developing data products The specific challenges of leading data teams in financial services

As data volumes grow and become ever-more complex, the role of the data analyst has never been more important. At the disposal of the modern data analyst, are tools that reduce time to insight, and increase collaboration. However, as the tools of a data analyst evolve, so do the skills.  Today's guest, Peter Fishman, Co-Founder at Mozart Data, speaks to this exact notion.  Join us as we discuss: Defining a data-driven organization & main challenges Breaking down the modern data stack & what it means What makes a great data analyst How data analysts can develop deep subject matter expertise in the areas they serve Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn. Listening on a desktop and can't see the links? Just search for DataFramed in your favorite podcast player.