Creators of Intelligence: Industry secrets from AI Leaders that can be easily applied to build and ace your data science career
- Length: 395 pages
- Edition: 1
- Language: English
- Publisher: Packt Publishing
- Publication Date: 2023-05-09
- ISBN-10: 1804616486
- ISBN-13: 9781804616482
- Sales Rank: #1908197 (See Top 100 Books)
Get access to the secret recipe for a successful data science career from 18 AI leaders
Key Features
- One-on-one interviews with global Data Science leaders who share their insights and expertise.
- Pragmatic advice on how to become a successful Data Scientist and Data Science Leader.
- Guidance on overcoming common pitfalls and challenges to ensure your projects succeed and deliver value.
Book Description
A Gartner report in 2018 led to headlines such as “85% of AI implementations will fail by 2022”. It’s unclear whether there was a mass extinction event for AI implementations at the end of 2022 but the question remains: how can I ensure that my project doesn’t become a statistic?
Back in 2015, headlines told us that Data Scientists were the new ‘Rock Stars’ of business, and the demand for the skill set has only grown since then. So just how do you become a Data Scientist Rock Star? As a new senior data leader, how do you build and manage a productive team? What is the path to becoming a Chief Data Officer?
Creators of Intelligence contains a series of in-depth, one-on-one interviews where recognized Data Science leader, Dr. Alex Antic, delves into the answers to these questions, and many more, with some of the world’s leading Data Science leaders and CDOs.
Interviews from: Cortnie Abercrombie, Edward Santow, Kshira Saagar, Charles Martin, Petar Veličković, Kathleen Maley, Kirk Borne, Nikolaj Van Omme, Jason Tamara Widjaja, Jon Whittle, Althea Davis, Igor Halperin, Christina Stathopoulos, Angshuman Ghosh, Maria Milosavljevic, Dr. Meri Rosich, Dat Tran, Stephane Doyen
What you will learn
- Where to start with AI ethics and how to evolve from frameworks to practice.
- Tips on building and managing a data science team Advice for organizations seeking to build or mature a data science capability.
- Stop beating your head against a brick wall – pick the environment that will support your success.
- Stories from successful data leaders as they reflect on success and failure in the development of data strategy.
- How business areas can best work with data science teams to drive business value.
Who This Book Is For
The book caters to a wide range of audiences from people working in the data science industry to data science leaders and chief data officers. This book will also cater to senior business leaders, who are interested in learning how data and analytics are used to support decision-making in different domains and sectors. Students who are contemplating a career in Artificial Intelligence (AI) and the broader data sector will also find this book useful. Those developing and delivering university-level education – including undergraduate, postgraduate, and executive programs may also find it useful
Creators of Intelligence Foreword Contributors About the author Additional contributor Preface Who this book is for What this book covers Get in touch Share Your Thoughts Download a free PDF copy of this book Chapter 1: Introducing the Creators of Intelligence Chapter 2: Cortnie Abercrombie Wants the Truth Getting into the business Discussing diversity and leadership Implementing an ethical approach to data Establishing a strong data culture Designing data strategies Summary Chapter 3: Edward Santow vs. Unethical AI Developing responsible AI pathways Applying ethics in practice Considering the broader impact of AI on society Responding to the challenges of generative AI Summary Chapter 4: Kshira Saagar Tells a Story The path to data science Implementing a data-driven approach Discussing leadership in data culture Storytelling with data Getting into the industry now Looking to the future of AI Summary Chapter 5: Consulting Insights with Charles Martin Getting into AI Balancing research and consulting Advising companies on their AI roadmap Understanding why data projects fail Measuring impact Integrating data Finding the limits of NLP Explainable AI and ethics Summary Chapter 6: Petar Veličković and His Deep Network Entering the world of AI research Discussing machine learning using graph networks Applying graph neural networks Pushing research boundaries with machine learning Using graphs for AGI Bridging the gap between academia and industry Getting into research Summary Chapter 7: Kathleen Maley Analyzes the Industry Pursuing a career in analytics Striving for diversity Becoming data-driven Dealing with dueling datasets Overcoming roadblocks Establishing an effective data culture Learning about analytics Looking to the future Summary Chapter 8: Kirk Borne Sees the Stars Getting into the field Advising a new organization on becoming data-driven Structuring teams Managing data scientists Why do AI projects fail? Building an effective data culture Teaching data science Predicting the future of AI Summary Chapter 9: Nikolaj Van Omme Can Solve Your Problems Getting started Assessing the progress of AI ML and OR Becoming data-driven Setting your project up to succeed Exploring leadership Measuring success Developing ethical AI in an organization Starting out in data Looking to the future Summary Chapter 10: Jason Tamara Widjaja and the AI People Getting started in data science Becoming data-driven Managing data science projects Why AI projects fail Communicating a realistic expectation to clients and partners Establishing a data culture The importance of data governance Discussing leadership Advising new entrants to the field Generative AI and ChatGPT Predicting the future Summary Chapter 11: Jon Whittle Turns Research into Action Building a career Translating research into real-world impact Developing AI that is ethical, inclusive, and trustworthy AI in Australia Discussing leadership Predicting the future of AI Entering the industry today Summary Chapter 12: Building the Dream Team with Althea Davis Getting into data Increasing diversity and inclusion Working in consulting Establishing a data service and culture Managing projects Why does AI fail? Summary Chapter 13: Igor Halperin Watches the Markets Coming to AI from another field Applying ML to problems in finance Making AI explainable and trustworthy Planning for successful AI Navigating hype Discussing the role of education Considering the future of AI Summary Chapter 14: Christina Stathopoulos Exerts Her Influence Becoming a data science leader Observing changes in the field Increasing diversity and inclusion in the field Advising new organizations Understanding why projects fail Using data storytelling Understanding the fundamental skills of data science Getting hired in data science Progressing into leadership Summary Chapter 15: Angshuman Ghosh Leads the Way Getting into AI Watching the field evolve Becoming data-driven Organizing a data team Building a good data culture within an organization Understanding the value of data storytelling Hiring new team members Summary Chapter 16: Maria Milosavljevic Assesses the Risks Getting into analytics Discussing diversity and inclusion AI and analytics Becoming data-driven Ethical AI Establishing a good data culture Why do data science projects fail? Discussing data leadership Looking to the future Summary Chapter 17: Stephane Doyen Follows the Science Getting into data science Becoming a leader Becoming data-driven Developing AI solutions for the medical field Putting the “science” in “data science” Establishing a data culture at an organization Building the right team Looking to the future of AI Summary Chapter 18: Intelligent Leadership with Meri Rosich Becoming a chief data officer Improving diversity and inclusion Discussing the high failure rates of AI projects Becoming a data-driven organization Establishing an effective data culture What makes a good data leader? The importance of data storytelling Making AI ethical and trustworthy Advice for aspiring data scientists Looking forward Summary Chapter 19: Teaming Up with Dat Tran Entering the industry Discussing the high failure rates of AI projects Setting up for success Establishing a good data culture Being a data leader Discussing data storytelling Hiring team members Advice for beginners Looking to the future Summary Chapter 20: Collective Intelligence Entering the field and becoming a successful data scientist Becoming a CDO and senior data leader Developing an effective data strategy Establishing a strong data culture Becoming data-driven Ethical and responsible AI Data literacy Scaling your data capability Structuring and managing data science teams Avoiding AI failure Measuring Success Storytelling with data Predicting the future of AI Striving for diversity and inclusion The changemakers Index Why subscribe? 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