Bertil Hatt - How to think about your career in Data science | PyData Global 2023

Discover how to future-proof your data science career with Bertil Hatt, exploring the intersection of human creativity and machine capabilities, and the importance of understanding patterns, visualizing data, and building scalable systems.

Key takeaways

You should find a job that motivates you and aligns with your values.
Innovation often requires change, which can be scary, but can also lead to progress.
Even small changes can be significant, and it’s crucial to understand the motivations behind them.
Human-led innovation is essential, as machines can already perform repetitive tasks.
The fear of replacement by machines is understandable, but it’s crucial to recognize that humans are still needed for many tasks.
Large companies are better equipped to handle changes, as they have more resources.
Small companies can also innovate, but it requires careful planning and adaptation.
The future of data science is uncertain, but it’s crucial to remain flexible and open to new developments.
There will always be a need for human judgment and creativity.
It’s crucial to understand the limitations of machines and the importance of human oversight.
The idea of open science is becoming more prominent, as researchers are collaborating more and sharing knowledge.
The concept of pattern recognition is crucial in data science, and humans should focus on understanding the patterns rather than just applying them blindly.
The future of data science is not just about replacing jobs, but about augmenting human capabilities and freeing humans from repetitive tasks.
Data scientists should focus on finding the patterns in data and using them to their advantage.
The need for human oversight in data science is essential, as machines can make mistakes.
The potential behind AI innovations is enormous, but humans should be cautious and not blindly accept its results.
The concept of data visualization is crucial in making data more accessible and understandable.
The future of data science is uncertain, but it’s crucial to remain excited and optimistic about the potential benefits of AI.
The concept of machine learning is essential in data science, as it enables machines to learn from data and make predictions.
The need for data scientists is not diminishing, but rather, they are evolving to incorporate more machine learning and AI concepts.
The concept of data engineering is becoming more prominent, as it’s crucial to build scalable and efficient systems for data processing.
The future of data science is uncertain, but it’s crucial to be prepared for the changes and adapt to the new landscape.

Bertil Hatt - How to think about your career in Data science | PyData Global 2023

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