Data Science Careers, Training, and Hiring

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

verfasst von: Dr. Renata Rawlings-Goss

Verlag: Springer International Publishing

Buchreihe : SpringerBriefs in Computer Science

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract

Data Science and analytics is not only improving businesses and sparking new industries, it is also improving the human condition under which every person needs food, shelter, clean water, health care, security, and education [1]. This need for transformation in global society shows that the relevance of Data Science is not only its unparalleled ubiquity and enormous scope, but its potential to improve lives and decision-making across a wide variety of areas. Organizations of all sizes and types are increasingly reliant on data as critical to their core operations. For some quick numbers, the Big Data Analytics market—just one slice of the larger Data Science and analytics market—will grow to over $187 billion within the decade [2]. The previously out of bounds worlds of politics, human rights, environmental advocacy, energy, education, healthcare, humanities, arts, and social good are now accessible and welcoming to big data and data scientists. In the midst of all this, there is a huge opportunity for career development. Adding analytics skills now can be a boost in almost any field and create a niche or opportunity for employment. This opportunity is made even greater as statistics reveal a global talent gap in the workforce [3]. For this reason, data degree programs are rapidly emerging across the globe and companies are looking to hire talented people that have a grasp on these new opportunities transforming our world!

Renata Rawlings-Goss

Chapter 2. Building Data Careers

Abstract

The most appealing thing about a career in data is that you have the world open to you. Data is the modern gatekeeper, getting you into almost any arena. The challenge in building a data career is the misconception that the typical career path rolls out in a linear or stepwise way. There are a number of paths and specialties that can be taken or pursued, but they are not neatly outlined, as the Data Science field itself is new. We will discuss career options, the skills you may need for those options, pros and cons of different types of training programs from online courses to full Data Science degrees, including a list of over 460 Data Science degree programs across the country. We will touch on the job market, the ups and downs of management and location, as well as experiences from real data scientists.

Renata Rawlings-Goss

Chapter 3. Building Data Programs

Abstract

Data Science degrees, programs, and initiatives are emerging at a rapid pace at universities and colleges in the U.S. and abroad. Data Science, however, is as much a practice as it is a discipline, raising the questions of whether and how Data Science should be treated in academia. Should it be its own major, department, or division at a university? And what are the foundational elements that comprise a degree in Data Science? Here, we discuss the institutional barriers to developing and implementing Data Science/Analytics programs, the role of faculty, and resources for curriculum. We also feature different models taken by top U.S. institutions for incorporating Data Science on campus, how to access data, as well as potential solutions to the common challenge of faculty burden. Lastly, we go through top recommendations produced by national forums, with hundreds of researchers, convening to discuss these topics.

Renata Rawlings-Goss

Chapter 4. Building Data Talent and Workforce

Abstract

Hiring data talent is desirable and challenging. Due to a global talent shortage, there are hundreds of thousands of Data Science jobs that go unfilled each year. There are, however, a few traits that differentiate the best hiring managers from the rest. Managers that take full advantage of their resources and build a culture of recruiting tend to consistently get the top candidates. In this section, we talk about why hiring for Data Science and analytics talent is different, the flaws in the traditional ways Human Resources departments are used, the strategies for building an updated culture of recruiting and the direct benefits to hiring managers. We also talk about reasonable expectations and the missing links in the hiring process, how to assess skills, talent sourcing and continuing education for current employees. Finally, we discuss considerations for senior-level data scientists and the new Data C-suite.

Renata Rawlings-Goss

Chapter 5. Conclusion

Abstract

To lay the foundation for success in forming a data career, degree program or business team we must return to a point from the introduction. Data is not where the true power is; it is in people. Not just the people who code but the people who use data to make decisions and those whose lives data effects.

Renata Rawlings-Goss

Chapter 6. Resources

Abstract

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Renata Rawlings-Goss

Titel: Data Science Careers, Training, and Hiring
verfasst von: Dr. Renata Rawlings-Goss
Verlag: Springer International Publishing
Electronic ISBN: 978-3-030-22407-3
Print ISBN: 978-3-030-22409-7
DOI: https://doi.org/10.1007/978-3-030-22407-3