UPDATE: Read Part Two Here
The transition from university programming to programming in industry can be challenging for a variety of reasons.
The Black Engineers (BE) EMEA network at Goldman Sachs created an internal "How to Think Like a Programmer" guide with the aim of providing the tools to address the gap that exists between university and industry programming, in addition to encouraging programmers and non-programmers alike to learn new languages or update their skills. Another goal was to help those without a computer science background to gain a greater understanding of programming. The aim of this blog series is to make this content available externally to enable university students to have access to this information.
This series will aim to encourage readers to think about how to program applications that scale to industry standards, while learning helpful software engineering techniques along the way. The series will provide an emphasis on the following:
This blog post series will take you on a journey from starting out as a junior developer, to transitioning into a senior developer, and also provide an introduction to software architecture and how it differs from design. The next entry in this series will be focused on what to know when starting a new role as a junior developer.
In academia, the focus is on learning to program, whereas in industry you are writing code with the aim of solving a business problem. Due to the different aims, there are differences in the approach of how code is produced. It is important to be aware of these differences to make it easier to adjust to programming in industry.
Programming at universities can often be focused on the syntax (i.e. how to implement a for or while loop, what does this element of code do, etc), and less focused on the overall problem.
Typically, the problems that students focus on in university are narrower in scope and standalone. In industry, programmers are expected to understand the context in which their solution is expected to operate. Additionally, the scale of the problem is typically significantly greater.
By being aware of the differences outlined in this post, programmers can begin thinking about programming in a different way in preparation for completing their university journey and working within industry.
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