Evolution of Data Science: Enhancing Productivity, Not Replacing You

In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Jorge Figueroa:</p> <strong>AI Monopoly - Join or fight. </strong>Some developers are scared about AI being monopolized instead of thinking about the possibilities and opportunities new AI platforms represent in their projects.<br /></p> <strong>AI - A new danger to some professions or a new tool?  </strong><br />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.</p>

00:17:39

Ensuring Scalable Data Science Solutions

<strong>In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Dinakar Deshmukh:</strong></p> <ul> <li><strong>what does it mean for a solution to be scalable</strong></li> <li><strong>how have we lacked in this area as an industry </strong></li> <li><strong>why it’s important to make solutions that last</strong></li> <li><strong>the future of data science products and scalability</strong></li> </ul>

00:15:52

Successfully Leveraging AutoML to Solve Complex Problems

In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Chad Harness:</p> <ul> <li>describe automl to the business person </li> <li>how do these types of tools work </li> <li>what are the right ways to leverage automl </li> <li>expectations for the future of automl tools</li> </ul>

00:21:18

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

In this episode of TAG Data Talk, host Dr. Beverly Wright talks with Justin Grieshop:</p> <ul> <li>Describe the backgrounds and disciplines in data science and analytics </li> <li>How to show the importance of diversity of thought</li> <li>Advice for understanding the right education and how to extract value from those resources</li> </ul>

00:16:27

Building and Managing the Data Science Product Development Process

In this episode of TAG Data Talk, we talk with Patrick Halpin about Building and Managing the Data Science Product Development Process</p>

00:18:42

Evolution of Data Scientists: The Transitioning Role of Data Scientist

In this episode of TAG Data Talk, we talk with Lyndsey Padden about the Evolution of Data Scientists: The Transitioning Role of Data Scientist</p>

00:23:25

Dispelling the AI Hype

In this episode of TAG Data Talk, we talk with Nan Li about Dispelling the AI Hype</p>

00:21:15

Developing and delivering a quick data science project

In this episode of TAG Data Talk, we talk with Kevin Enyekwe and Chanyong Yoo about Developing and Delivering a Quick Data Science project</p>

00:26:29

Enabling an Analytics Culture through Transformative Processes

In this episode of TAG Data Talk, we talk with Stephanie Dean about Enabling an Analytics Culture through Transformative Processes</p>

00:21:47

Leading Data Scientists

In this episode of TAG Data Talk, we talk with Ken Viciana about Leading Data Scientists</p>

00:22:51