Book Image

Creators of Intelligence

By : Dr. Alex Antic
Book Image

Creators of Intelligence

By: Dr. Alex Antic

Overview of this book

A Gartner prediction in 2018 led to numerous articles stating that "85% of AI and machine learning projects fail to deliver.” Although it's unclear whether a mass extinction event occurred for AI implementations at the end of 2022, the question remains: how can I ensure that my project delivers value and doesn't become a statistic? The demand for data scientists has only grown since 2015, when they were dubbed the new “rock stars” of business. But how can you become a data science rock star? As a new senior data leader, how can you build and manage a productive team? And what is the path to becoming a chief data officer? Creators of Intelligence is a collection of in-depth, one-on-one interviews where Dr. Alex Antic, a recognized data science leader, explores the answers to these questions and more with some of the world's leading data science leaders and CDOs. Interviews with: 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, and Stephane Doyen.
Table of Contents (23 chapters)
1
Chapter 1: Introducing the Creators of Intelligence

Entering the field and becoming a successful data scientist

It’s been fascinating to watch the field of data science develop over the past decade. This has been primarily driven by an increase in awareness of the value that data analytics can offer.

People who are looking to transition into the field of data science and build a career often ask me for advice on how to go about this. Based on the conversations in this book, and my own views, we can offer the following recommendations.

To become a data scientist, you first and foremost need to develop a strong foundation in the core technical skills required to do the job. These core skills are even more important if you plan to pursue a career in research. The technical skills required to perform data analysis, and develop machine learning models include the following:

  • Mathematics and statistics:
    • Linear algebra
    • Calculus
    • Optimization theory
    • Frequentist and Bayesian statistics, including probability theory
  • Computer...