AI News, 9 Mistakes to Avoid When Starting Your Career in Data Science

9 Mistakes to Avoid When Starting Your Career in Data Science

If you wish to begin a career in data science, you can save yourself days, weeks, or even months of frustration by avoiding these 9 costly beginner mistakes.

Many beginners fall into the trap of spending too much time on theory, whether it be math related (linear algebra, statistics, etc.) or machine learning related (algorithms, derivations, etc.).

This approach is inefficient for 3 main reasons: This theory-heavy approach is traditionally taught in academia, but most practitioners can benefit from a more results-oriented mindset.

Thanks to mature machine learning libraries and cloud-based solutions, most practitioners actually never code algorithms from scratch.

While a strong degree in a related field can definitely boost your chances, it's neither sufficient nor is it usually the most important factor.

In addition, many hiring managers will specifically look for your ability to be self-sufficient because data science roles naturally include elements of project management.

To avoid this mistake: Currently, in most organizations, data science teams are still very small compared to developer teams or analyst teams. So while an entry-level software engineer will often be managed a senior engineer, data scientists tend to work in more cross-functional settings.

To avoid this mistake: In this guide, you learned practical tips for avoiding the 9 costliest mistakes by data science beginners: For more over-the-shoulder guidance, we also offer a comprehensive Machine Learning Masterclass that will teach you data science while allowing you to build an impressive portfolio along the way.

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