Читать книгу Minding the Machines - Jeremy Adamson - Страница 10

When a student pursues an applied field such as business or engineering, the curriculum is generally developed in a way that seeks to balance between foundational academic elements and applied profession-specific education. The curriculum is updated and maintained such that it remains aligned with the changing needs of the field. For several professions such as accounting, law, and engineering, this takes place within a partnership between the administrative body of the professional practice and the educational institute, and through accreditation it's ensured that graduates of these programs are broadly educated and prepared to work in the field they have studied. This is unfortunately not the case with data and analytics.

Most North American universities have data science or analytics offerings, but having no natural home they are generally provided through multiple faculties such as business, mathematics, engineering, finance, or computer science. These programs provide instruction in highly simulated and well-defined problem solving, focusing on the improvement of a statistical metric. The data is often perfectly presented and accompanied by a well-articulated data dictionary, in great contrast to real life experience. Additionally, most curricula emphasize such topics as computer vision, natural language processing, and reinforcement learning, fairly esoteric topics that have little applied usage in industry. Finally, and most importantly, effectively none have mandatory coursework on the strategic and operational elements of an advanced analytics and AI team. Without this understanding, typical graduates have a thorough mathematic understanding, much in the way of raw horsepower, but require a significant investment in training before they understand how to leverage their education and apply it to a real-life scenario.

With so many new data and analytics graduates competing with mid-career transitioners and a global talent pool, they often seek ways to stand out as a potential hire. With the exception of highly specialized roles in technology companies, the key development opportunity for these new hires is the formation of leadership abilities in an analytical context. Reframing and focusing analytical concepts into a business context is an immediate and powerful way to differentiate yourself in a new role or in an interview, especially as the profession moves away from long-horizon highly technical solutions toward a focus on immediate value.

For the student, I hope that this book gives you the knowledge to stand out against your peers, to be seen as a strategic thinker, and to be able to add value to whatever organization you choose to work with.