Hilke Schellmann, a best-selling author, dives into the complexities of algorithmic bias in hiring. She shares her journey from journalism to exploring AI, revealing alarming insights about hiring tools that can perpetuate existing biases. The conversation uncovers the mental toll of workplace surveillance and questions its effectiveness. Hilke emphasizes the need for transparency and fairness in AI systems, urging job seekers to navigate these challenges with awareness and strategy. A call to empower women in data wraps up the discussion, promoting community and growth.

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. --- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support

Data Bytes listeners get an exclusive discount to join Women in Data. ⁠⁠⁠View discount here. (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 --- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support

(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. --- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support

Exploring AI's impact on healthcare, modeling common sense and emotion, human uniqueness in creativity, risks of emotional AI, AI in psychoanalysis, data protection laws, AI regulations, educating regulators, importance of asking questions, AI ethics research interests, advice to stay curious and continue learning.

Data Bytes listeners get an exclusive discount to join Women in Data. ⁠View discount here. (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 --- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support

Data Bytes listeners get an exclusive discount to join Women in Data. View discount here. (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. --- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support

As a Data Bytes Listener, you get an exclusive discount for WiD Pro Membership. Join here (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. --- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support

(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. --- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support

[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