Episoder
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(00:00) Introducing Catherine Shen’s Career
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(00:32) Transition from Luxury to Pharma
(01:55) Role of Data in Pharma
(02:52) Evolution of Data Engineering
(04:06) Innovative Data Solutions Impact
(07:23) AI’s Role in Pharma Industry
(10:24) Future AI Investments and Strategy
(13:09) Solving Unstructured Data Challenges
(14:02) Partnering with Math Company
(16:44) Measuring Success in Partnerships
(20:05) Pivotal Leadership Moments
(23:22) Believing in Innovation and Confidence
(25:29) Balancing Personal and Professional Life
(26:12) Building Confidence Over Time
(30:55) Creating Innovative Healthcare Collaborations
(33:02) Confidence Grows with Tenacity
(34:38) Excitement for the Future of Data
(36:39) Catherine’s Closing Remarks -
(00:00) Intro: Negative connotations in AI
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(00:21) Synthetic data fills gaps
(00:35) Guest introduction
(01:23) Importance of data quality
(02:14) Data-centric machine learning focus
(03:02) Bias mitigation strategies
(03:41) Role of human in AI loop
(04:34) Synthetic data in AI
(05:29) Pre-trained models and data quality
(06:02) Experiments with data quality
(06:39) Leading AI and research projects
(07:24) Explainability in AI models
(08:57) Privacy concerns in AI analysis
(10:34) Open source model benchmarking
(11:33) Motivation for open source contributions
(12:28) Long-term open source involvement
(13:50) Mentoring in open source projects
(15:19) Starting with open source
(16:35) Contributing beyond code
(17:50) Building community through collaboration
(18:48) Power of open source accessibility
(19:52) Open source challenges
(20:38) Success factors for open source projects
(22:58) Career-defining moments
(24:49) First encounter with open source
(26:28) Introduction to AI through NLP
(28:02) Pivoting from PhD to industry
(29:02) Career lessons and continuous learning
(30:13) Advice for women in tech -
Mangler du episoder?
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(01:22) Research on skills and technology
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(01:47) Changes in job search methods
(02:29) Algorithmic hiring and firm adaptations
(03:25) New roles from technology
(04:54) Ripple effects of technological changes
(06:06) Skating to where the puck is
(07:07) Building future-proof skills
(08:02) AI tools in daily work
(09:00) AI's impact on jobs
(10:08) Mega trends: technology, climate, demographics
(11:17) Testing tools and adapting workflow
(12:44) AI and future of hiring
(13:45) Longer time to hire with tech
(15:34) AI reshaping the labor market
(17:03) Gaining skills for complex roles
(18:20) Turing Trap: AI vs human augmentation
(19:05) Challenges for early career seekers
(20:26) Mentorship and human capital development
(21:41) Updating skills before job transitions
(23:21) Impact of job loss on earnings
(24:52) Career conversations and landscape awareness
(26:31) Advice for young researchers
(27:24) Staying motivated through research -
(Intro 00:00:00) Generative AI discomfort.
(00:00:36) Excited for data and MLOps.
(00:01:10) First one-on-one chat.
(00:02:29) Career transitions and "aha" moments.
(00:03:05) Bored easily, switched roles.
(00:05:22) Starting with startups.
(00:07:27) Learning skills at startups.
(00:09:26) Startups vs. big companies.
(00:11:55) Best time to join startups.
(00:13:41) Risky career, conservative money.
(00:15:27) Startups in twenties ideal.
(00:16:03) Label Box overview, responsibilities.
(00:19:50) Importance of data quality.
(00:24:05) Exciting Gen AI use cases.
(00:27:56) Future of AI agents.
(00:31:12) Justifying data quality investment.
(00:36:54) AI concerns and excitement.
(00:42:50) Building your community.
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(00:00:21) Importance of soft skills for technical professionals
(00:00:39) Introduction to the guest's extensive career and leadership in the military and data fields
(00:01:16) Discussion on breaking barriers in a male-dominated field
(00:01:45) Leadership journey and pivotal moments in career
(00:02:12) Starting a non-traditional career and early involvement with data
(00:03:04) Returning to federal service after 9/11 and subsequent roles in intelligence
(00:03:30) Impact of being selected as a White House leadership fellow
(00:04:25) Importance of breadth and depth in skill development
(00:05:19) How to lead without authority or official title
(00:06:02) Developing emotional intelligence (EQ) and soft skills
(00:06:27) Importance of partnerships and collaboration in the data and AI community
(00:07:03) Challenges of soft skills for technical professionals
(00:07:45) Practical advice for developing soft skills and teamwork
(00:08:44) Peer mentoring and the value of collaborative learning
(00:09:54) Adaptability in a rapidly changing tech environment
(00:10:38) The need to dream big and be prepared for future roles
(00:11:09) Lifelong commitment to curiosity and upskilling
(00:12:00) Encouragement to apply for challenging roles
(00:13:10) Starting an office from scratch as a Chief Data Officer
(00:14:15) Using an agile approach to deliver capability and value
(00:15:16) Building partnerships and collaborating with military executives
(00:16:27) Importance of strategic communication in leadership
(00:17:34) Balancing technical skills with leadership responsibilities
(00:18:22) Fostering innovation and creativity within data and AI teams
(00:19:34) Hosting hackathons and datathons to solve hard problems
(00:21:00) Providing a safe environment for team creativity and risk-taking
(00:21:51) Balancing technical knowledge with leadership roles
(00:22:48) Staying current through teaching and continuous learning
(00:24:13) Teaching as a way to learn and stay relevant
(00:25:01) Encouraging participation in datathons and hackathons
(00:25:52) Advice for emerging leaders on staying relevant and dreaming big
(00:27:30) The importance of lifelong learning and curiosity
(00:27:55) Plans after retirement and ongoing impact in the data field
(00:30:10) Building the next generation of leaders through volunteering and advising
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(00:00:00) Intro(00:00:22) Introduction to Snowflake and AI(00:00:34) Guest introduction and background(00:00:57) Excitement about the conversation(00:01:13) Impressions of Snowflake's office(00:01:21) Discussion on Snowflake Summit and AI/ML announcements(00:01:52) Overview of Snowflake as a unified platform(00:02:16) Evolution of Snowflake from data warehousing to AI(00:02:37) AI features and tools in Snowflake(00:02:55) Announcements at Snowflake Summit(00:03:05) Getting started with Snowflake(00:03:16) Starting with Snowflake based on specific roles(00:03:45) Specialization within Snowflake's platform(00:03:55) Deciding where to start with Snowflake(00:04:25) Importance of Python and SQL in Snowflake(00:05:35) Snowflake as a platform for various roles(00:05:57) Snowflake's learning platform and resources(00:06:05) Overview of Snowflake's developer resources(00:06:17) Introduction to Snowflake's North Star program(00:06:53) North Star courses for different workloads(00:07:13) North Star's foundation course(00:07:35) Learning paths within North Star(00:07:59) Availability of North Star courses on Coursera(00:08:34) Discussion on teaching generative AI courses(00:09:18) Differences between teaching online and live talks(00:09:37) Experiences in writing scripts for courses(00:10:30) Challenges in creating course content(00:11:29) Tips for writing course scripts(00:11:50) Experiences in teaching and content creation(00:12:12) Changes in NLP and language models over the years(00:12:24) Evolution of interest in NLP(00:13:21) Excitement about the resurgence of NLP(00:14:07) Shift in momentum for AI research(00:14:38) Concerns about the use of generative AI(00:14:59) Misuse of LLMs in outdated tasks(00:15:54) Pessimism about the overuse of LLMs(00:16:15) Exploring correct use cases for LLMs(00:16:29) Potential use cases for LLMs(00:17:04) Example of Snowflake's internal use of LLMs(00:17:25) Chatbots as a first line of defense in support(00:18:19) Content generation with LLMs(00:18:40) Example of a content engine using LLMs(00:19:23) Excitement about enterprise use of LLMs(00:19:59) Benefits of internal knowledge sharing using LLMs(00:20:03) Use cases for LLMs in organizations(00:21:04) Encouragement to stick with niche interests(00:21:50) Shift in focus from computer vision to NLP(00:22:01) Advice for PhD students on choosing specializations(00:22:35) Importance of following research trends(00:23:15) Recommendations for getting started in NLP(00:24:18) Importance of understanding fundamentals in NLP(00:24:28) Starting with core NLP papers(00:24:58) Keeping up with new research in AI(00:25:05) Strategies for staying updated on AI research(00:26:23) Following key figures in AI on social media(00:26:45) Trends in AI research and their impact(00:27:14) Challenges in staying current with AI papers(00:27:43) Use of social media for AI research(00:28:27) Academic communities on different platforms(00:29:36) Visual learning in AI education(00:29:55) Excitement about the future of AI(00:30:06) AI's impact across industries(00:30:15) Exploration of new use cases in AI(00:30:46) Examples of creative AI use cases(00:31:32) Curiosity about AI's future impact on industries(00:31:50) Potential changes in education through AI(00:33:05) Excitement about new AI-driven education tools(00:33:40) Personalized education with AI(00:33:47) The future of women in data and tech
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(Intro 00:00:00) Introduction by Maggie
(00:00:19) Data science roles evolving
(00:00:34) Guest Introduction - Maggie Ma
(00:00:56) Maggie’s diverse data science background
(00:01:12) What drew Maggie to data?
(00:01:35) University majors and early internships
(00:02:37) Discovering geospatial data science
(00:03:08) Predicting COVID-19 spread
(00:03:29) Logistics and transportation data science
(00:03:34) Geospatial data complexity
(00:04:13) Advice for geospatial data work
(00:04:43) Maggie’s journey to freelancing
(00:05:55) Impact and imposter syndrome
(00:06:03) Research team experiences
(00:07:13) Content creation feedback
(00:08:09) Overcoming imposter syndrome
(00:08:36) Challenges in tech industry
(00:09:57) Handling imposter syndrome daily
(00:10:20) Focus on self-improvement
(00:11:32) Authenticity on social media
(00:12:14) Behind the scenes content
(00:13:03) Sharing authentic daily lives
(00:13:41) Building a social media presence
(00:14:39) Staying updated in data science
(00:15:50) Managing information overload
(00:16:59) Career moves within data science
(00:17:27) Unplanned career progression
(00:18:03) Switching roles within data
(00:19:10) AI’s impact on data jobs
(00:19:31) Specific roles in data science
(00:20:10) Lower barrier of entry
(00:21:30) Using AI to debug code
(00:22:00) Advice for aspiring data scientists
(00:22:42) Importance of reflection
(00:23:49) Making peace with your path
(00:24:13) Future business and personal growth
(00:24:33) Building an AI startup
(00:25:42) Customized data science projects app
(00:26:27) Target release date for app
(00:27:01) Incorporating AI in learning
(00:28:07) Encouraging the community
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Join us for an insightful episode of the Data Bytes Podcast featuring Gaia Bellone, Chief Data Scientist at Prudential Financial. Gaia's illustrious career spans roles such as Senior Vice President at KeyBank and Executive Director at Chase, highlighting her expertise in data science and AI.
In this episode, Gaia shares her career journey, from her academic beginnings at Carnegie Mellon University and Università Bocconi to leading groundbreaking projects in the financial services industry. Listen in as she discusses the evolving role of data science, key industry trends, and provides valuable advice for aspiring data scientists on balancing professional growth with personal well-being.
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Catie introduction (00:00:56)
Research discussion (00:01:21)
Human-robot interaction (00:02:24)
Choreo robotics (00:03:16)
Dance background (00:03:41)
Movement and emotion (00:04:29)
Robot design (00:05:42)
Human expectations (00:06:10)
Googly eyes (00:06:53)
Utility and inspiration (00:08:07)
TED talk (00:08:17)
Doomsday sayers (00:08:48)
Robot origins (00:08:58)
Robot limitations (00:10:14)
Technology misconceptions (00:11:09)
Robotics careers (00:13:08)
Interdisciplinary robotics (00:13:28)
Human interactions (00:14:25)
Entrepreneurial journey (00:16:00)
Zeni app (00:16:23)
Personal challenges (00:17:17)
Resilience and support (00:19:03)
Learning and feedback (00:20:35)
Future of Zeni (00:21:13)
Technological adaptation (00:21:19)
Agency and autonomy (00:23:21)
Vision for the future (00:24:04)
Role of art (00:25:54)
Encouraging play (00:26:19)
Inspirational message (00:27:52)
Gratitude and teamwork (00:28:35)
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(00:00:00) Intro
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(00:00:20) AI in Hiring
(00:00:37) Bias in Automation
(00:01:04) Welcome to Podcast
(00:01:09) Guest Introduction
(00:01:16) Journalism to AI
(00:01:26) First Encounter with AI
(00:02:10) Job Interview with Robot
(00:02:48) Research and Rabbit Hole
(00:05:24) Hiring Tools Bias
(00:05:51) Systemic Hiring Issues
(00:07:04) Human Bias in Hiring
(00:08:09) Bias in AI Tools
(00:13:26) Echo Chamber Effect
(00:13:58) Workplace Surveillance
(00:14:12) Amazon Hiring Example
(00:22:04) AI and Employee Surveillance
(00:24:01) Stress from Surveillance
(00:24:38) No Privacy on Work Computer
(00:25:07) Tools to Track Activity
(00:27:45) Productivity Theater
(00:28:19) Meaningful Productivity
(00:31:24) Tools for Flight Risk
(00:35:45) Need for Transparency
(00:40:11) Suggestions for Job Seekers
(00:41:07) Forced Consumerism
(00:43:02) Journalistic Role
(00:43:15) Outro -
Data Bytes listeners get an exclusive discount to join Women in Data. View discount here.
(00:00:00) Gen AI benefits discussed.
(00:00:18) Peggy Tsai introduction.
(00:01:06) Data leadership conversation starts.
(00:01:39) Challenges for data officers.
(00:02:20) AI compliance and regulation.
(00:03:27) Merging data and AI roles.
(00:04:08) Data quality importance.
(00:05:02) Gen AI adoption strategies.
(00:06:23) C-suite collaboration impacts.
(00:07:12) Board's interest in Gen AI.
(00:08:34) Is Gen AI a hype?
(00:09:14) Gen AI's personal benefits.
(00:10:10) AI's widespread adoption.
(00:11:27) Pandemic's impact on data.
(00:12:15) Excitement for future innovations.
(00:12:55) Customized language models.
(00:13:55) Small language models' advantages.
(00:14:44) External AI models for customers.
(00:15:35) Future of Gen AI.
(00:16:08) Data foundation is critical.
(00:17:10) Importance of data governance.
(00:18:36) Consistent data definitions.
(00:19:38) Data governance challenges.
(00:20:04) Investment in data management.
(00:20:58) Unstructured data governance.
(00:21:41) Data retention policies.
(00:22:38) AI model biases.
(00:23:45) Data life cycle management.
(00:24:03) Data hoarding risks.
(00:24:34) Improving data quality.
(00:25:13) Simplify data assets.
(00:26:26) Strong cross-functional partnerships.
(00:27:15) Storytelling for data value.
(00:28:23) Mentorship and sponsorship.
(00:29:03) Peggy's mentorship experience.
(00:30:26) Advocacy for women in data.
(00:32:32) Importance of representation.
(00:33:19) Women in Data community.
(00:34:47) Conference networking benefits.
(00:36:14) Importance of community.
(00:36:33) Advice for young professionals.
(00:37:46) Broader perspective in work.
(00:38:36) High school student mentorship.
(00:39:57) Growth mindset in AI.
(00:40:20) Peggy's career advice.
(00:40:49) Supporting each other.
(00:41:02) Outro and call to action.
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(00:00:00) - Creating a course with AI
(00:00:35) - Welcome Dr. Jon Krohn
(00:01:58) - AI's impact on teaching
(00:02:33) - Updating Jupyter notebooks
(00:03:10) - Using Claude 3 Opus
(00:04:13) - Free content availability
(00:06:35) - O'Reilly and free access
(00:07:37) - Early content creation
(00:10:01) - Impact on global readers
(00:12:14) - Interactive SQL course
(00:12:48) - Evaluating AI models
(00:13:53) - Comparing GPT-4, Gemini, Claude 3
(00:15:17) - Data privacy with LLMs
(00:21:24) - AI in team workflows
(00:27:07) - LLMs in data science
(00:29:49) - Automating data labeling
(00:32:28) - Creating synthetic data
(00:35:02) - Exciting AI advancements
(00:35:49) - Expanding audience reach
(00:38:20) - Positive future show concept
(00:40:17) - Optimistic view of the future
(00:43:26) - Popularity of the show
(00:46:01) - Closing thoughts and thanks
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(Intro 00:00:00) Importance of asking for help and admitting ignorance.
(00:00:31) Introduction to Colleen's journey into data analytics.
(00:01:23) Background in athletics and transition to data career.
(00:02:12) Current role in healthcare and previous athletic career.
(00:02:48) Transferable skills from athletics to data analytics.
(00:03:47) Work ethic, resilience, and team collaboration in data.
(00:06:20) Balancing work and personal life for sustainable career.
(00:07:33) Avoiding burnout by pacing yourself in your career.
(00:08:05) Importance of taking breaks to recharge and solve problems.
(00:12:18) Overcoming feeling behind when changing careers.
(00:13:09) Not pretending to know everything and asking questions.
(00:14:26) Bringing hard work mentality from athletics to data.
(00:16:28) Importance of mentorship and asking for advice.
(00:20:25) Discovering data analytics as a career option.
(00:21:18) Background in data collection and analysis in athletics.
(00:23:31) First job experiences in tech and real estate.
(00:25:47) Applying data skills to various industries and interests.
(00:27:38) Embracing challenges and continuous learning in data.
(00:28:57) Finding balance between new challenges and stable tasks.
(00:30:07) Advice for career changers entering data analytics.
(00:32:07) Learning on the job and continuous education.
(00:34:07) Current role and future aspirations in leadership.
(00:36:11) Being grateful for the opportunity to work in data.
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(Intro 00:00:00) Deep learning inspired by the human brain.
(00:00:29) Besa's leadership and analytics background.
(00:01:06) Excitement for Besa's interdisciplinary background.
(00:01:28) Besa's journey combining technology and human condition.
(00:03:49) AI's impact on healthcare discussed.
(00:05:41) Neuromorphic hardware and future technology.
(00:07:22) Modeling common sense and emotion in AI.
(00:08:26) Human uniqueness and AI creativity.
(00:10:41) AI in healthcare and elder care.
(00:14:08) Emotional AI and human attachments.
(00:15:24) AI in psychoanalysis and psychotherapy.
(00:17:14) Privacy risks with AI.
(00:19:35) AI's potential mental health pitfalls.
(00:22:00) Data protection laws for AI.
(00:23:12) Need for AI regulations.
(00:25:30) Educating regulators on AI.
(00:26:50) Importance of asking questions.
(00:30:10) Research interests in AI ethics.
(00:32:31) Advice: stay curious, ask questions.
(00:33:22) Encouragement to continue learning.
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(00:00:30 - 00:01:52) Welcome and Introduction
(00:01:52 - 00:04:07) Democratizing Data
(00:04:07 - 00:06:59) Balancing Architecture and Flexibility
(00:06:59 - 00:10:08) Internal vs. Consulting Support
(00:10:08 - 00:13:09) Measuring ROI in Data Projects
(00:13:09 - 00:16:26) Improving Business and Technical Team Collaboration
(00:16:26 - 00:20:20) In-Person vs. Virtual Collaboration
(00:20:20 - 00:22:21) Importance of Networking and Personal Growth
(00:22:21 - 00:27:31) Sandy's Career Turning Points
(00:27:31 - 00:29:33) Mentorship: Being a Great Mentor and Mentee
(00:29:33 - 00:32:38) Advancing Diversity and Equity in Tech Careers
(00:32:38 - 00:33:25) Closing Remarks
(00:33:25 - End) Outro and Membership Invitation
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(00:00:00) Importance of changing the way we teach math to be more inclusive.
(00:00:34) Introduction to the podcast guest, author and professor.
(00:00:58) Teaching at James Madison University; living in Alexandria, VA.
(00:01:28) Balancing in-person, hybrid, and online teaching formats.
(00:02:05) Overview of the book, "Essential Math for AI."
(00:02:27) Inspiration for writing the book; addressing the needs in AI education.
(00:04:11) Key concepts: types of intelligence, core mathematical foundations for AI.
(00:05:00) Starting points for learning math relevant to AI: calculus, linear algebra, probability.
(00:05:14) Connecting math learning to real-life applications for better understanding.
(00:06:12) Discussing the struggle with abstract math education and its real-world application.
(00:07:20) Changing how math is taught to retain more talented individuals.
(00:08:55) Addressing the fear of math and the impact on AI engagement.
(00:09:38) Overcoming math anxiety by setting clear goals and self-teaching.
(00:11:48) How AI advancements will change mathematics education.
(00:12:01) Benefits of AI in education, including automation and personalized learning.
(00:15:02) Historical perspective on the evolution of work and technology.
(00:15:29) Embracing fast technological advancements for societal benefits.
(00:17:20) Mathematics as a truth filter in the age of information.
(00:18:27) New book on prompting and data engineering.
(00:19:25) Exploring the intersection of AI models and data pipelines.
(00:21:09) The end-to-end story of AI from hardware to organizational strategy.
(00:21:45) The importance of understanding the bigger picture in AI.
(00:23:01) Journey into mathematics and AI, starting from Lebanon.
(00:25:44) Advice for young people navigating today's environment.
(00:26:08) Being true to oneself and seeking knowledgeable mentors.
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(Intro 00:00:00) Companies leaping into AI without solid data foundations.
(00:01:12) Importance of conferences for connecting and learning.
(00:01:26) Christina emphasizes energy and storytelling at tech conferences.
(00:02:17) LinkedIn connections making conference experiences better.
(00:02:33) Christina’s background at Google and Waze.
(00:03:13) Analytics ascendancy model explained.
(00:04:10) Common issue of companies jumping into AI prematurely.
(00:05:38) Importance of sequential steps in the analytics journey.
(00:06:23) Christina’s ACE framework (Advise, Create, Educate).
(00:07:06) Roles of advising, creating content, and educating.
(00:08:22) Handling C-suite pressure regarding AI hype.
(00:09:07) Evaluating current capabilities and setting expectations.
(00:10:27) Common pitfalls in the analytics journey.
(00:12:20) Challenges and risks in advanced analytics.
(00:13:57) Regulation and risk in finance and healthcare.
(00:14:59) Responsibility for assessing risk and regulation.
(00:15:19) Cross-functional nature of risk assessment.
(00:16:12) Advice on continuing the analytics journey.
(00:16:44) Maintaining a positive mindset and continuous learning.
(00:18:27) Future role of AI in analytics.
(00:19:41) AI’s potential and limitations in turbocharging analytics.
(00:21:49) Christina’s personal analytics journey.
(00:22:27) From studying statistics to founding Dare to Data.
(00:25:33) Advice for aspiring data professionals.
(00:25:37) Importance of curiosity, learning, and communication skills.
(00:27:14) Being a translator between business and technology.
(00:27:49) Christina’s SQL courses on LinkedIn Learning.
(00:28:35) Future courses and learning opportunities.
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(00:00:00) - Introduction by Sol Rashidi on data management challenges.(00:00:36) - Sadie welcomes Sol and discusses her background and accolades.(00:02:21) - Sol addresses the impact of AI hype on C-suite roles and data governance.(00:03:17) - Sol explains her approach to overcoming data challenges.(00:04:16) - Discussion on the evolving roles within the C-suite and the challenges of unclear division of labor.(00:05:59) - Sol on the responsibilities of a chief AI officer and the practical challenges in strategy and delivery.(00:07:05) - Sadie and Sol discuss the added complexity of new C-suite roles.(00:08:16) - Sol outlines an ideal CDO role and its necessary scope.(00:10:58) - Sol discusses professional relationships within the C-suite and strategies for negotiation.(00:14:28) - Sol promotes a course on transitioning from a practitioner to the C-suite.(00:15:12) - Discussion on the challenges and strategies for effective leadership in the C-suite.(00:18:23) - Sol on learning from failures and the importance of asking for what you want.(00:21:17) - Discussion on why few women hold leadership positions and how to negotiate effectively.(00:26:08) - Sadie discusses the role of tools like ChatGPT in professional communication.(00:30:06) - Sol shares her plans post-retirement and her new book release.
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[00:00:00] Intro and discussion on information retrieval [00:00:36] Sadie welcomes Harpreet, discussing his achievements and connections [00:01:46] Early interactions and courses between Harpreet and Sadie [00:02:27] Updates on Sadie's SQL course and new roles [00:03:42] Harpreet discusses following curiosity in his career and AI's growth [00:06:55] Future of data science roles and specialization within the field [00:09:40] Unique skills of data scientists and transition to deep learning [00:15:23] Discussion on benchmarks, datasets, and introduction to retrieval augmented generation (RAG) [00:20:16] Explanation and potential applications of RAG models [00:24:09] AI applications in various industries and predictions for future AI integration [00:29:02] Harpreet's personal productivity gains from AI and new tools enhancing workflows [00:34:58] Harpreet's podcast impact on his career and future plans [00:40:44] Recommendations for staying updated in deep learning [00:45:36] Harpreet invites listeners to join his new research initiative--- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support
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Intro [00:00:00]
Priscila discusses the importance of an accessible data infrastructure and data literacy.
[00:00:41] Sadie:
Introduction of Priscila Papazisis, her achievements and roles.
[00:01:06] Sadie:
Discussion on AI and data literacy strategies for organizations.
[00:01:27] Priscila:
Priscila responds about strategies for fostering data literacy in organizations.
[00:02:03] Priscila:
Importance of executive support in building a data-driven culture.
[00:02:36] Priscila:
Training programs for data literacy across various companies.
[00:03:05] Priscila:
Reiteration of the need for accessible data infrastructure.
[00:03:42] Priscila:
Emphasizes employee engagement with data for decision-making.
[00:04:12] Priscila:
Continuous improvement and promoting an environment for feedback.
[00:04:36] Sadie:
Challenges in providing and encouraging training in organizations.
[00:05:07] Priscila:
Finding time and interest for employee training in a busy schedule.
[00:06:25] Priscila:
Importance of understanding statistical concepts, data visualization, and AI in business.
[00:07:39] Priscila:
Critical thinking and application of AI and machine learning in business.
[00:08:20] Priscila:
Understanding industry trends and market dynamics.
[00:08:50] Sadie:
Priscila shares examples of business value from data literacy programs.
[00:09:10] Priscila:
Story about enhancing logistics in a health insurance company.
[00:10:23] Priscila:
The impact of data literacy programs she initiated.
[00:11:16] Priscila:
Operational improvements from data-driven decisions.
[00:12:42] Priscila:
Importance of practical results from data products.
[00:13:19] Priscila:
Engaging in continuous learning and leveraging data literacy.
[00:14:10] Sadie:
Discussing common pitfalls in implementing data and AI programs.
[00:14:28] Priscila:
Key challenges and advice for data and AI program implementation.
[00:16:45] Sadie:
Advice for individuals improving their data and AI literacy.
[00:17:09] Priscila:
Recommended resources and personal approaches to data literacy.
[00:18:37] Priscila:
Emphasis on data storytelling and problem-solving with data.
[00:20:42] Sadie:
The role of storytelling in data and AI.
[00:21:11] Sadie:
Priscila's journey into the data field.
[00:22:10] Priscila:
Career path and evolution in data roles.
[00:23:16] Priscila:
Contributions to the data community and networking.
[00:25:36] Sadie:
The value of community in data and AI.
[00:26:27] Sadie:
Final advice for women in data and AI careers.
--- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support - Se mer
