https://workyoucanexplain.com/tags/career-planning/ Recent content in Career Planning on Work You Can Explain Hugo en-US Copyright © Work You Can Explain Thu, 30 Jul 2026 00:00:00 -0500 https://workyoucanexplain.com/posts/is-a-computer-science-major-worth-it-in-the-ai-era/ Thu, 30 Jul 2026 00:00:00 -0500 https://workyoucanexplain.com/posts/is-a-computer-science-major-worth-it-in-the-ai-era/ <p>A computer science major can still be worth it in the AI era, but the title of the degree is not enough. A strong program gives you a coherent foundation, repeated technical practice, feedback, mentors, peers, projects, and access to opportunities that would be difficult to assemble alone.</p> <p>AI does change the decision. It can reduce the effort required to produce some code, and it changes how software work is divided. It does not remove the need to define a problem, understand systems, evaluate evidence, test behavior, protect data, maintain software, and explain decisions.</p> https://workyoucanexplain.com/posts/desirable-talents-in-the-ai-era/ Tue, 28 Jul 2026 00:00:00 -0500 https://workyoucanexplain.com/posts/desirable-talents-in-the-ai-era/ <p>Three human talents are worth deliberate practice alongside AI: broad foundations, initiative, and mental robustness.</p> <p>This is a practice framework, not a prediction about which jobs will grow or which personality will succeed. The value is immediate. These talents help a student frame better questions, begin worthwhile work, respond to evidence, and remain responsible for AI-assisted results.</p> <p>Treat them as skills. Build them through dated artifacts and observable actions, not labels such as “well-rounded,” “self-starter,” or “resilient.”</p> https://workyoucanexplain.com/posts/build-a-small-portfolio-with-depth/ Fri, 24 Jul 2026 00:00:00 -0500 https://workyoucanexplain.com/posts/build-a-small-portfolio-with-depth/ <p>A strong student portfolio does not need many projects. Select a few pieces that you can explain deeply: the problem, constraints, decisions, implementation, tests, failures, evidence, credits, and what you would improve.</p> <p>Do not invent users, metrics, test results, deployment status, or independent work. Honest limitations show more judgment than unsupported claims.</p> <h2 id="select-for-depth">Select for depth</h2> <p>Choose a project when you can provide most of this evidence:</p> <ul> <li>a clearly stated problem and audience;</li> <li>requirements and non-goals;</li> <li>meaningful decisions with alternatives;</li> <li>source and reproducible setup;</li> <li>tests tied to expected behavior;</li> <li>one investigated failure or revision;</li> <li>a working demonstration or accurate screenshots;</li> <li>your specific contribution;</li> <li>credits and license information; and</li> <li>reflection based on observed results.</li> </ul> <p>A small class project can be valuable when the reasoning is visible. Ten cloned tutorials with changed colors provide weak evidence.</p>