4 answers
Ryan
Student
Middletown, Connecticut
1
Question
Updated
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How do you think the field of data science is going to be impacted by AI and machine learning??
Data scientists already use AI and machine learning to build models, but how is using AI to code and conduct analyses going to impact the field?
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4 answers
Patrick Meyer
Executive - Data Analytics and AI
1028
Answers
Naperville, Illinois
Updated
Patrick’s Answer
Working closely with data scientists and being part of the AI field myself, I’ve seen firsthand how AI and machine learning are reshaping the landscape of data science. While data scientists have long relied on machine learning to build predictive models and uncover insights, the rise of generative AI, especially tools that can write code, automate data cleaning, and even generate statistical analyses, is taking things a step further. These advancements are shifting the traditional role of data scientists from primarily doing hands-on technical tasks to focusing more on strategic thinking, problem formulation, and decision-making based on the outputs AI helps produce. I am constatnly telling my team to build these skills if they want to stay relevant in the profession.
Ryan, please know that AI-powered tools like code assistants can now generate Python or R scripts, automate parts of the data pipeline, and will speed up model testing. This means routine tasks like exploratory data analysis, basic model training, and even some visualization work can be done faster and more efficiently. So these are clear signs that the role of a data scientist is and needs to evolve. There is now a growing emphasis on understanding how to ask the right questions, interpret AI-generated results accurately, and apply them responsibly in a real-world context. Ethical use of data, bias detection in algorithms, and strong domain knowledge are becoming even more critical skills. These are all the areas that I focus on on a daily basis with my clients.
I beleive that rather than replacing data scientists, AI is becoming a powerful partner. One that enhances productivity and opens up room for more creative and impactful work. The future of data science will likely be more collaborative, where human intuition and strategic insight work hand-in-hand with AI tools to solve complex problems at scale. So, for anyone entering or growing in this field, it's essential to not only stay sharp on technical skills but also develop critical thinking, ethical awareness, and the ability to communicate data-driven insights clearly to non-technical audiences.
Ryan, please know that AI-powered tools like code assistants can now generate Python or R scripts, automate parts of the data pipeline, and will speed up model testing. This means routine tasks like exploratory data analysis, basic model training, and even some visualization work can be done faster and more efficiently. So these are clear signs that the role of a data scientist is and needs to evolve. There is now a growing emphasis on understanding how to ask the right questions, interpret AI-generated results accurately, and apply them responsibly in a real-world context. Ethical use of data, bias detection in algorithms, and strong domain knowledge are becoming even more critical skills. These are all the areas that I focus on on a daily basis with my clients.
I beleive that rather than replacing data scientists, AI is becoming a powerful partner. One that enhances productivity and opens up room for more creative and impactful work. The future of data science will likely be more collaborative, where human intuition and strategic insight work hand-in-hand with AI tools to solve complex problems at scale. So, for anyone entering or growing in this field, it's essential to not only stay sharp on technical skills but also develop critical thinking, ethical awareness, and the ability to communicate data-driven insights clearly to non-technical audiences.
Thank you so much for your insight! I really appreciate it.
Ryan
Stefan Elser
Data Scientist
8
Answers
Colorado Springs, Colorado
Updated
Stefan’s Answer
Hi Ryan,
I've found that using generative AI to help write or improve my code for me has freed up some of my time to allow me to get to the real data science tasks faster. I'm still working on incorporating the technology into my workflow, but many times as a data scientist I find myself spending my time solving programming problems and not data science problems. While I enjoy programming, my skills are better suited to testing and designing machine learning models and this piece of model development frequently is cut short due to the time it takes to get started and eventually push the final model into a production ready state.
In addition, I can use AI to help me research the latest advances in machine learning by summarizing recent papers, which helps me to stay up to date.
Stay curious!
I've found that using generative AI to help write or improve my code for me has freed up some of my time to allow me to get to the real data science tasks faster. I'm still working on incorporating the technology into my workflow, but many times as a data scientist I find myself spending my time solving programming problems and not data science problems. While I enjoy programming, my skills are better suited to testing and designing machine learning models and this piece of model development frequently is cut short due to the time it takes to get started and eventually push the final model into a production ready state.
In addition, I can use AI to help me research the latest advances in machine learning by summarizing recent papers, which helps me to stay up to date.
Stay curious!
Teklemuz Ayenew Tesfay
Electrical Engineer, Researcher and Software Developer
230
Answers
Ethiopia
Updated
Teklemuz Ayenew’s Answer
Hello,
AI and machine learning are changing the way data science works, automating tasks like data cleaning, analysis, and model building to make everything faster and more efficient. As coding becomes less of the focus, the role of the data scientist is shifting towards strategic thinking, domain knowledge, and clear communication. It’s still important to have a solid foundation in things like statistics, to stay curious and critical, and to validate AI-generated results. Data professionals should adopt tools like ChatGPT, AutoML, and other AI platforms to enhance efficiency, while applying human judgment to guide, refine, and ensure the quality of the results.
In this new landscape, a great data science portfolio focuses on reasoning, explainability, and storytelling rather than flawless code. Strong communication skills, critical thinking, and the ability to collaborate with different teams are crucial for turning data into actionable insights. Understanding the context, asking the right questions, and being able to communicate findings in a clear and compelling way are also key skills. As AI tools become more accessible, data scientists need to position themselves as thoughtful interpreters and ethical advisors. The key to thriving in this field—one that’s increasingly automated but still heavily reliant on human insight—is a commitment to continuous learning and adaptability.
AI and machine learning are changing the way data science works, automating tasks like data cleaning, analysis, and model building to make everything faster and more efficient. As coding becomes less of the focus, the role of the data scientist is shifting towards strategic thinking, domain knowledge, and clear communication. It’s still important to have a solid foundation in things like statistics, to stay curious and critical, and to validate AI-generated results. Data professionals should adopt tools like ChatGPT, AutoML, and other AI platforms to enhance efficiency, while applying human judgment to guide, refine, and ensure the quality of the results.
In this new landscape, a great data science portfolio focuses on reasoning, explainability, and storytelling rather than flawless code. Strong communication skills, critical thinking, and the ability to collaborate with different teams are crucial for turning data into actionable insights. Understanding the context, asking the right questions, and being able to communicate findings in a clear and compelling way are also key skills. As AI tools become more accessible, data scientists need to position themselves as thoughtful interpreters and ethical advisors. The key to thriving in this field—one that’s increasingly automated but still heavily reliant on human insight—is a commitment to continuous learning and adaptability.
This was super helpful, thank you!
Ryan
Qiaoli Wang
3
Answers
Buffalo, New York
Updated
Qiaoli’s Answer
AI and machine learning are definitely transforming the field of data science. While data scientists already use these technologies to build models, AI is making it easier and faster to code, analyze, and generate insights. This means that tasks that once took a lot of manual effort, like feature engineering or model tuning, will become more automated, allowing data scientists to focus on higher-level problem-solving and decision-making. However, it also means that data scientists will need to adapt by developing skills in using and understanding AI tools, as well as thinking critically about the results AI produces to ensure they’re valid and reliable. It’s an exciting time to be in data science, as these advancements will continue to expand what’s possible!