Feeling inspired to write your first TDS post? We are always open to contributions from new authors..
The roadmap to success in data science offers many different paths, but most of them include a strong focus on mathematical and programming skills (example: this excellent guide for aspiring data professionals who Sankhya Mondal published earlier this week). However, once you've covered your bases in those areas, what's next? What topics do data scientists need to gain expertise in to differentiate themselves from the rest in a crowded job market?
Our weekly highlights focus on some of the areas you may want to explore in the coming weeks and months, providing practical advice from authors who are deeply rooted in a broad cross-section of academic and industry roles. From mastering the ins and outs of data infrastructure to expanding storytelling skills, let's take a closer look at some of those peripheral, but still crucial, areas of potential growth.
- Beyond skills: unlocking the full potential of data scientists
“Data scientists possess a unique perspective that allows them to generate their own innovative business ideas: ideas that are novel, strategic, or differentiating and that are unlikely to come from anyone other than a data scientist.” Eric Colson expands on a thought-provoking premise, namely that companies are underutilizing data scientists by focusing too much on their technical skills, at the expense of their creativity and innovative thinking. - Three crucial data lessons I learned from a non-ai data conference
ai has so completely dominated conversations in recent years that it's refreshing to hear about other ways data scientists can stay at the forefront of their field. Nithyaa Ramamoorthy reflects on her recent conference experience and how it inspired her to pay more attention to topics that may seem less shiny than the last LLM, but can increase your value as a data professional, from cost containment and data translation to information design. - The Ultimate Productivity System for Data Science Leaders
For anyone on the data science management path, whether in the early stages or later in their career, it can sometimes feel like leadership skills are expected to grow organically with nothing more. than the passage of time. While that might be true in some respects, Rebecca VickeryThe latest contribution details some of the concrete steps you can take to ensure you stay focused and productive even as the demands of your role grow.
- Mastering basic math will make you a better data scientist
We know, we know: we didn't promise math. but what Torsten WalbaumThe new paper suggests that data professionals may want to worry less about complex formulas and models, and allow themselves to become more comfortable producing rough but robust estimates. - From ai Canvas to MLOps Stack Canvas: are they essential?
As tools and data stacks grow in complexity, it becomes all too easy for product stakeholders to lose track of how all the moving parts are supposed to work together. Chayma Zatout is here to help, with a practical introduction to building and using canvases, “a visual framework that helps individuals and teams map and analyze various aspects of a given project (…) in a structured way.” - The AWS Bedrock Tutorial You Wish You Had: Everything You Need to Know to Prepare Your Machine for AWS Infrastructure
“How can you take a nifty little machine learning prototype on your laptop and turn it into a powerful full-fledged web application?” Taking a few steps away from the nitty-gritty details of data analysis, the mind of myers It encourages data professionals to consider their technology setup and optimize it for smooth and effective workflows. - From Insights to Impact: Presentation Skills Every Data Scientist Needs
It's not exactly news that strong storytelling is at the core of many, if not most, data science functions; However, it remains an undercovered area in many programs, an area in which you will be expected to magically improve on your own. yu dong addresses some of the core aspects of successful presentations in his latest article and includes concrete suggestions on how to design successful slides. - How to Create Opportunities and Succeed in Data Science Job Applications
As Robson Tiger makes it clear that the process of becoming a top job seeker and identifying the right opportunities requires its own set of skills, most of which have very little to do with data or algorithms and instead revolve around self-presentation (and marketing), networking and communication.
