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Evolution of Data Science: Enhancing Productivity, Not Replacing You
Manage episode 380315974 series 2293348
In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Jorge Figueroa:
AI Monopoly - Join or fight. Some developers are scared about AI being monopolized instead of thinking about the possibilities and opportunities new AI platforms represent in their projects.
AI - A new danger to some professions or a new tool?
Will use reporting as an example. Reports were created by IT specialists and were fixed and timely to create. These were mostly replaced by BI tools. Surely there were report-experts how thought BI was a danger to their work at some point in time that later migrated to using BI tools and found them a great evolution from basic reporting. Even when integrating ML or Data Science projects is faster and easier with newly available platforms and resources, experts are still needed to work out these integrations. You should not consider your expertise replaced but empowered by these new tools. Millions of research hours and dollars in investment are now at your disposal to expedite your work. I recently had a talk with some friends from the art community and they feel threatened by tools like Dall-e, Stable Diffusion, Craiyon, etc. I explained new tools often look threatening to some professions but are an opportunity. Those who learn to use them faster and better have an advantage over others. As an example, Prompt Engineering is now an important competence.
65 episodes
Manage episode 380315974 series 2293348
In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Jorge Figueroa:
AI Monopoly - Join or fight. Some developers are scared about AI being monopolized instead of thinking about the possibilities and opportunities new AI platforms represent in their projects.
AI - A new danger to some professions or a new tool?
Will use reporting as an example. Reports were created by IT specialists and were fixed and timely to create. These were mostly replaced by BI tools. Surely there were report-experts how thought BI was a danger to their work at some point in time that later migrated to using BI tools and found them a great evolution from basic reporting. Even when integrating ML or Data Science projects is faster and easier with newly available platforms and resources, experts are still needed to work out these integrations. You should not consider your expertise replaced but empowered by these new tools. Millions of research hours and dollars in investment are now at your disposal to expedite your work. I recently had a talk with some friends from the art community and they feel threatened by tools like Dall-e, Stable Diffusion, Craiyon, etc. I explained new tools often look threatening to some professions but are an opportunity. Those who learn to use them faster and better have an advantage over others. As an example, Prompt Engineering is now an important competence.
65 episodes
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1 Getting the Absolute Best Data Science Talent to Solve Marketing Problems 18:46

1 Reducing Barriers to Complex Data Science Entry by Leveraging AI 18:08

1 Defining and Adapting your Data Science Career 25:41

1 Building Data Science and AI Capabilities to Last 22:14

1 Applying a Data-Informed Approach to Influence Leaders and Business Decision-Makers 24:03

1 Evolution of Data Science: Enhancing Productivity, Not Replacing You 17:39


1 Successfully Leveraging AutoML to Solve Complex Problems 21:18

1 Getting the Education Right: Choosing academic programs to stimulate a successful career in data 16:27

1 Building and Managing the Data Science Product Development Process 18:42

1 Evolution of Data Scientists: The Transitioning Role of Data Scientist 23:25

1 Developing and delivering a quick data science project 26:29

1 Enabling an Analytics Culture through Transformative Processes 21:47

1 Leveraging Data Science for Actionable Insights 17:46

1 Connecting and Leveraging Academic Partnerships in Data Science 31:40

1 Unlocking Data along the Value Chain in Manufacturing 20:53

1 Identifying the Why Why: Strategies to understand true problems and questions for data science projects 18:45

1 Building and Advancing a Data Science Career 24:43

1 Aligning IT data function with data science efforts 27:03

1 Data Science and Artificial Intelligence from a CTO's perspective 19:36


1 Enabling AI Efforts by Improving Data Governance 23:55

1 Enterprise Data Literacy to Scale Analytics Solutions 28:18

1 Making Analytics a Business as Usual Function 29:42


1 Importance of Involving Analytics Stakeholders Early 21:43


1 The Lost Art of Listening for Improved Analytics 17:03

1 Leveraging Workforce Analytics for Strategic Advantage 31:07


1 The Value of Imperfection: Embracing the Realities of Model Building 20:25


1 The Criticality of Understanding Business Context in Analytics 18:35

1 Encouraging Model Adoption among the Executive Suite and Beyond 25:25



1 History, Future, and Concerns with Artificial Intelligent Solutions 30:30

1 Measuring the Effectiveness of Data Science Solutions. 26:54

1 Application of Data Science for Cause Based Initiatives 21:48


1 Application of Data Science for Cause Based Initiatives 28:05

1 Understanding Data Science Skillsets and Roles 24:55

1 Forming Solid Business and Analytics Partnerships 21:52

1 True Understanding Versus Prediction in Data Science 19:30

1 Bridging the Academic - Industry Gap: Applying Analytics Principals to Problem Solving 19:16


1 The Last Mile of Analytics: Successful Consumption of Results 22:14


1 Balancing Offensive and Defensive Analytics Strategies 18:27

1 Applying Analytics to the Changing World of Media 19:45

1 Encouraging Analytics Adoption in the Executive Suite 20:59

1 Organizing and Developing Successful Data Science Function 20:55


1 Effectively Communicating Analytics Results 24:37


1 Marketing Analytics to Drive Customer Centricity 22:49

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