Continuous Learning: A Data Scientist’s Odyssey | by Zijing Zhu | Sep, 2023

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Navigating the ever-changing field

To be a data scientist is to sign up as a lifetime learner. Something new is always coming up in the data science field — — a new algorithm, a new practice, a new concept. How should we data scientists keep current and navigate through this ever-changing field? In this article, I would like to share what to learn and how to learn continuously as someone who is self-taught in data science and currently works in this field.

Why learning continuously

Before discussing how to learn as a data scientist continuously, it’s important to first understand why. Learning without purpose seldom gives an actual uplift in skills, and since it’s hard to keep you motivated consistently. For me, continuous learning is almost a necessity at work. Encountering roadblocks at work is common due to the complex nature of the problems. Building something from scratch or improving problem-solving methods always requires extra research, reading, and practice. Besides, I am also a curious person and eager to learn more about the latest trends and cutting-edge technologies in my field. Going through my past projects and articles at Medium, some techniques that were cutting-edge back then look so outdated today. I am amazed by how fast this field is evolving, and at the same time, I feel obligated to evolve and update my skill sets. For me, continuous learning increases work efficiency, boosts confidence and job security, and inspires me in content creation. In fact, a mindset of continuous learning is beneficial for anyone seeking growth and willing to step out of their comfort zone, not just limited to data science or the tech industry. Find your ‘why’ before you start.

Deciding what to learn is important. It depends on your current skill level and your short-term and long-term goals. I focus on learning two types of skills: hard skills to generate insights and soft skills for delivering those insights effectively.

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Hard skills

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