The 2 pillars of information science—statistics-backed evaluation and code—include an entire range of constraints. Structure your query the unsuitable way, and you would possibly mess up a complete pipeline. Apply a formula incorrectly, and the outcomes of your test might not reflect reality.

Working inside constraints doesn’t should feel rigid or limiting, though—quite the opposite. A nimble data skilled often must lean into their creativity while still working inside a predefined set of parameters. This holds true in our day-to-day workflows, in the way in which we learn latest skills, and in how we set ourselves up for a successful data profession.

The articles we’ve highlighted this week cover very broad terrain, from constructing recommender systems to finding project ideas in your portfolio. What they’ve in common is a fresh outlook on questions and challenges that many in the sphere might consider settled. If “we’ve all the time done things this manner” is a sentence you are inclined to scoff at, you’ll likely find something right up your alley on this week’s selection.

• . After announcing the death of the dashboard three years ago, Taylor Brownlow revisits this ubiquitous, occasionally useless, sometimes indispensable fixture of information teams to advertise a more nuanced view not only of dashboards but of the way in which data professionals produce and communicate meaningful insights: “Changing how we work is rather more difficult than adopting latest tools.”
• .From e-commerce sites to Netflix, recommender systems have shaped our online experiences for years. Amine Dadoun’s latest article suggests that it’s time to shake up this wide-reaching domain by leveraging the most recent advances in knowledge graphs.
• . Faced with an advanced latest project, some machine learning engineers could be desirous to put the fanciest algorithm into motion as soon as possible. As Olga Chernytska explains at first of her latest series on constructing robust ML systems, what they need to really do is plan ahead in order that their solution covers each business needs and technical requirements.

• . Job descriptions (and hiring managers) are sometimes biased towards the measurable and easy-to-assess: X years of this, Y years of that. Abhi Sawhney’s debut TDS post is a helpful antidote to this tendency, and foregrounds other areas that may provide help to stand out as a knowledge analyst, from proactivity to empathy.
• . In a competitive job environment, small things could make a difference—including the combo of projects in your portfolio. Matt Chapman argues that you need to leave tried-and-true datasets and overdone analyses to other job applicants, and offers several promising directions you may explore as an alternative.