Data Science Careers, Training, and Hiring: A Comprehensive Guide to the Data Ecosystem: How to Build a Successful Data Science Career, Program, or Unit

Description

This book is an information packed overview of how to structure a data science career, a data science degree program, and how to hire a data science team, including resources and insights from the authors experience with national and international large-scale data projects as well as industry, academic and government partnerships, education, and workforce.  Outlined here are tips and insights into navigating the data ecosystem as it currently stands, including career skills, current training programs, as well as practical hiring help and resources. Also, threaded through the book is the outline of a data ecosystem, as it could ultimately emerge, and how career seekers, training programs, and hiring managers can steer their careers, degree programs, and organizations to align with the broader future of data science. Instead of riding the current wave, the author ultimately seeks to help professionals, programs, and organizations alike prepare a sustainable plan for growth in this ever-changing world of data.   The book is divided into three sections, the first “Building Data Careers”, is from the perspective of a potential career seeker interested in a career in data, the second “Building Data Programs” is from the perspective of a newly forming data science degree or training program, and the third “Building Data Talent and Workforce” is from the perspective of a Data and Analytics Hiring Manager. Each is a detailed introduction to the topic with practical steps and professional recommendations.   The reason for presenting the book from different points of view is that, in the fast-paced data landscape, it is helpful to each group to more thoroughly understand the desires and challenges of the other. It will, for example, help the career seekers to understand best practices for hiring managers to better position themselves for jobs. It will be invaluable for data training programs to gain the perspective of career seekers, who they want to help and attract as students. Also, hiring managers will not only need data talent to hire, but workforce pipelines that can only come from partnerships with universities, data training programs, and educational experts. The interplay gives a broader perspective from which to build.  Acknowledgments Contents About the Author Abbreviations About the Book Chapter 1: Introduction 1.1 The Data Science Opportunity 1.2 Data People: An Extended Metaphor References Chapter 2: Building Data Careers 2.1 Assessing Data-Enabled Career Paths 2.1.1 The Data Generalist 2.2 The Rise of the Data Specialists 2.2.1 Artificial Intelligence: Machines Acting Like People 2.2.2 Machine Learning (ML): Learning from the Past 2.2.3 Computer Vision and Image Processing: Recognizing Objects and Images 2.2.4 Natural Language Processing (NLP): Understanding Written Language 2.2.5 Internet of Things (IoT): Sensors Bring the Internet into the Real World 2.3 Skills for the Career You Want 2.3.1 Innovation Skills (Not “Soft Skills”) 2.3.2 Four Invaluable Skills for Success 2.3.3 Technical Skills 2.4 Learning Data Science 2.4.1 Evaluating and Finding the Perfect Pathway 2.4.2 Picking a Career Path 2.5 The Job Market 2.5.1 Size and Scale 2.5.2 Skills in Demand 2.5.3 Data Career Experiences 2.5.4 Location Matters 2.5.5 Who’s Hiring References Chapter 3: Building Data Programs 3.1 A GPS for Learning and Work 3.2 Institutional Culture 3.3 Interdisciplinary Collaborations (The Role of Faculty) 3.4 U.S. Models for Data-Focused Programs 3.4.1 Building a New Academic Unit 3.4.2 Expanding an Existing Unit 3.4.3 Data Literacy for All 3.4.4 Creating New Connectors 3.4.5 Creating New Stand-Alone Entity 3.4.6 Data Residency or Exchange Program 3.4.7 Career Services and Support 3.5 Resources for Data Science Curriculum 3.5.1 Data Science Module Curriculum: Learning Levels 3.6 Continuous Instructor Learning 3.6.1 Faculty Career Advancement 3.6.2 Benefits of Interdisciplinary Collaborations 3.6.3 Faculty Training and Credentialing 3.6.4 Faculty Recruitment 3.6.5 Collaborations with Industry and Government 3.7 Access to Data 3.7.1 Trusting Data 3.7.2 Cleaning Data 3.7.3 Resources for Data 3.7.4 Data Program Solutions 3.8 Top Recommendations References Chapter 4: Building Data Talent and Workforce 4.1 Why Is Hiring for Data Science and Analytics Different? 4.2 Don’t Go It Alone: Build a Culture of Recruiting 4.2.1 The Traditional Role of Human Resources 4.2.2 An Updated Culture of Recruiting 4.2.2.1 Talent Acquisition Partnership Guidelines for Managers 4.2.3 The Foundation of Partnership 4.2.4 The Missing Link 4.3 Reasonable Expectations 4.3.1 The Data C-Suite 4.4 Skills Assessment 4.5 Talent Sourcing: An Eye Toward Diversity 4.5.1 Academia 4.5.2 Startups 4.5.3 Industry Partners, Consultants, and Non-Traditional Partners 4.6 Continuing Education for Managers and Workers References Chapter 5: Conclusion 5.1 The New World Chapter 6: Resources 6.1 Program Overview: Over 450 Data Science Degree Programs Across the Nation