ChatGPT and Data Science Projects

Generative AI, used in ChatGPT and Bard, will provide innovative ways for teams to collaborate and execute projects, transforming the way data science teams operate and interact. In short, as these models evolve, their potential impact on how teams execute data science projects will be substantial.

More generally, generative AI, powered by large language models, is revolutionizing the data science landscape. ChatGPT has already demonstrated remarkable achievements, such as excelling in university exams, the bar exam for US lawyers, and medical school entrance exams. Answering these questions goes beyond merely “Googling the answer” but requires the integration of many concepts and facts. As future and more specialized models emerge, they will undoubtedly impact various industries.

Recently, I was asked about the influence of these technologies on data science project management. Hence, in this blog post, I will explore how AI-driven tools can enhance team processes and collaboration within a data science or AI project context.

So, how will ChatGPT (or a similar tool, like Bard) impact the way teams conduct data science? This question has two dimensions. First, we can consider how data scientists and other team members can utilize ChatGPT to deliver better insights more rapidly by examining ChatGPT’s impact from both a data science life cycle and a team collaboration perspective.

Utilizing ChatGPT Throughout the Data Science Lifecycle

Let’s explore how a tool like ChatGPT can be utilized across the data science life cycle, following the phases defined by CRISP-DM.

Business Understanding: ChatGPT can help data science teams interact more effectively with stakeholders, enabling a better understanding of the problem and the potential use of predictive models. In the future, chatbots may interact with stakeholders to explore project requirements, such as how the model might be used and what organizational process changes would be required to leverage the model.

Data Understanding: A chatbot could also interact with data architects, or a different chatbot helping the data architect, to help them better understand what data is available as well as other data attributes, such as the meaning of different data fields and the quality of the data.

Data Preparation: Chat GPT could be used to provide suggestions on how to transform and store data (or to actively do these data transformation tasks). It could also generate code to merge datasets, and for example, transform data into a dataframe which could be used for modeling. Furthermore, ChatGPT could offer feature engineering ideas. Technologies such as ChatGPT have already demonstrated significant productivity improvements in software development, particularly for less experienced programmers. For example, it was recently found that software developers who were paired with an “AI programmer” were 56% more productive (as compared to a control group).

Hence, ChatGPT could also help accelerate any required data preparation coding. However, it has also been observed that these large language models can generate incorrect code. Consequently, for the foreseeable future, one can envision a pair-programming setup where a bot collaborates with a person to develop code more efficiently. While it’s certainly possible that in the long term, the bot could generate code with just high-level instructions, at this stage, a “human in the loop” is still clearly necessary.

Modeling: As previously mentioned, current versions of ChatGPT can be helpful at developing machine learning code (e.g., in Python or R). Hence, a simple way that ChatGPT will be leveraged within a data science project is to accelerate writing R and Python code to clean and store data, build visualizations, and build ML models (perhaps via pair-programming of a human with a chatbot). Note that tools already exist that incorporate ChatGPT as an assistant within an editor.

Evaluation: Determining the accuracy of a model can be challenging. In other words, understanding if a model is “correct” is often a difficult task. A common approach is to test the model with data that was not used to train the model (i.e., back-testing the model). A chatbot can help ensure a comprehensive evaluation, including exploring if there is bias in the model. While current versions of chatbots, like ChatGPT-4, cannot perform the actual model evaluation, they can provide a framework to help teams appropriately evaluate a model and communicate the results to stakeholders.

Deployment: Deployment requirements vary greatly, depending on the organization and the data science project context. Deployment might require an organization to change its processes, which might be required so that the organization can effectively utilize machine learning insights. In this situation, a chatbot could help people understand how their role is evolving and how to best leverage ML insights. It is also possible that an ML system will need IT infrastructure and support to be deployed. In this situation, a bot could assist release engineers to configure and deploy a robust infrastructure for the new ML solution.

ChatGPT as a Data Science Project Facilitator and Process Expert

ChatGPT can serve as a valuable resource for facilitating data science projects. In other words, ChatGPT will be reading all of our DSPA blog posts (as well as lots of other information which is focused on executing data science projects effectively, such as academic articles, and Youtube Videos). Leveraging this data, ChatGPT, or a specialized ChatGPT model, could be trained to use this knowledge to guide teams through the data science process.

For example, a bot could be useful to:

Act as a process expert: A chatbot could, for example, schedule the daily meeting, and keep that meeting on track (ex., encourage problem triage to be taken offline) as well as document key action items from the meeting. These items could then be prioritized and tracked.

Encourage communication: A chatbot could help foster effective communication between a product owner, the team and the stakeholders. This can be useful, for example, to help the team get a better understanding of how a predictive model might be used within a specific organizational context.

Contribute to Process Improvement: As a facilitator of communication and coordination, a chatbot can participate in retrospective meetings, since it could be able to identify and suggest ways for the team to work more effectively.

Provide Support for Ethical Oversight: With the increasing prevalence of AI and ML models, concerns about their ethical and responsible use have risen. Data scientists are needed to ensure these models are built, deployed, and monitored responsibly, considering issues like fairness, transparency, and privacy. A chatbot can aid data scientists in identifying potential ethical concerns and suggest best practices to address them.

In short, by acting as a facilitator and process expert, ChatGPT can significantly enhance the efficiency and effectiveness of data science projects, ensuring teams stay on track, communicate well, and continuously improve their processes.

The Impact of Large Language Models on the Need for Data Science Projects

While ChatGPT and similar tools can accelerate the data science process, it is important to consider how the emergence of generative ML models may affect the demand for data science projects. Will these chatbots, capable of creating and deploying smaller, more focused ML models, reduce the need for data science projects?

One perspective is that a general ChatGPT model could be sufficient to address almost all scenarios, leading to a situation where the only data science projects are those that improve ChatGPT and its competitors. In this scenario, ChatGPT would lead interactive discussions to understand the business problem, identify available data, and then iterate to create a specialized model (either a generative AI model or a more specific one, depending on the situation).

However, a more plausible scenario is that multiple generative AI models will be deployed, each trained on specific data sets. For instance, an accounting version might need to be trained on data from a specific tax jurisdiction to avoid merging various tax laws into a single pseudo-tax model.

In short, the emergence of generative ML models like ChatGPT is more likely to lead to an increase in data science projects rather than a decrease. This is because these models offer new capabilities and opportunities that can be leveraged across various domains, driving innovation and expanding the potential applications of data science

Now Versus the Potential Future

While the current version of ChatGPT cannot perform all of these tasks, it is certainly possible that future iterations will be capable of doing so. The impact of next-generation machine learning models, including the evolution of ChatGPT and competing tools in the coming years, will be significant. However, the timeline for these advancements remains uncertain.

Initially, tools like ChatGPT will make data scientists more efficient, particularly for junior data scientists who will benefit from the suggestions that ChatGPT can already generate. It can also be helpful helping document the project.

However, the future of team collaboration in the development and use of AI applications remains an open question. Nevertheless, the emergence of more powerful machine learning models does not, at least for the foreseeable future, eliminate the need for a data science team and an effective team process. Stakeholders, data scientists, machine learning engineers, and others will still need to work together to ensure the creation and utilization of effective predictive models.

ChatGPT’s Perspective

If you are curious, below is my short conversation with ChatGPT-4 on this topic. This was my question to ChatGPT-4:

I would like to write a blog post on how ChatGPT-4 (and future versions of ChatGPT) will impact the execution of a data science project. My focus is on the team process – things like the data science life cycle (such as CRISP-DM) and how the team coordinates (such as frameworks like Scrum and data driven scrum). Note that this is not the technical details of how models are built. Can you create a draft blog post for me?

Below is the answer. As you will note, it has some useful insights.

The world of data science is continuously evolving with the advent of new technologies and methodologies. Among these innovations, the integration of AI-powered language models like ChatGPT-4 is revolutionizing the way data science teams work together. In this blog post, we will explore how ChatGPT-4 and future versions of ChatGPT are impacting the execution of data science projects, with a specific focus on team processes, the data science life cycle such as CRISP-DM, and coordination frameworks like Data-Driven Scrum.

Streamlining the CRISP-DM Life Cycle with ChatGPT

The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a widely-accepted data science life cycle model, which typically comprises six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. ChatGPT-4 has the potential to enhance each stage of this cycle:

  • Business understanding: ChatGPT can help in refining project objectives and identifying relevant data sources based on natural language descriptions of the problem.
  • Data understanding: ChatGPT can provide insights through natural language summaries of data, identifying patterns, trends, and data quality issues.
  • Data preparation: ChatGPT can be leveraged to clean and preprocess data, such as identifying missing values, outliers, or automatically generating data labels.
  • Modeling: ChatGPT can assist in choosing appropriate models, suggesting optimal hyperparameters, and even generating code snippets.
  • Evaluation: ChatGPT can offer detailed evaluation reports and recommendations for model improvement, enabling data scientists to refine their models effectively.
  • Deployment: ChatGPT can help generate code for deploying models, making it easy to integrate models into production systems and maintain documentation.

Enhancing Collaboration and Communication with ChatGPT

ChatGPT can also play a crucial role in facilitating communication and collaboration among data science team members. By providing a natural language interface, ChatGPT allows team members to easily share their insights and ideas, even if they do not possess the same technical expertise. This can result in a more inclusive and diverse range of perspectives, ultimately leading to better decision-making.

Furthermore, ChatGPT can act as a centralized knowledge repository for teams, allowing members to quickly query project-specific information, access historical project data, or retrieve documentation. This can save time and effort, reducing the need to manually search through files or emails, and ultimately improving overall team efficiency.

Transforming Scrum and Data-Driven Scrum with ChatGPT

Scrum and Data-Driven Scrum frameworks have become popular methods for managing data science projects. ChatGPT-4 can be a game-changer in enhancing these frameworks by:

  • Sprint Planning: ChatGPT can assist in breaking down user stories into tasks, estimating effort levels, and prioritizing work. This can result in more accurate sprint planning and better alignment with project goals.
  • Stand-ups: ChatGPT can generate automated summaries of team members’ progress, facilitating efficient stand-up meetings and ensuring everyone is on the same page.
  • Sprint Review: ChatGPT can help create natural language summaries of sprint accomplishments and areas for improvement, making it easier for stakeholders to understand the project’s progress.
  • Sprint Retrospective: ChatGPT can provide insights on team performance and suggest actionable improvements, enabling teams to continuously refine their processes and work more effectively.


As AI-powered language models like ChatGPT-4 become more advanced and integrated into data science workflows, the potential impact on team processes and collaboration is immense. By streamlining the data science life cycle, enhancing communication, and transforming frameworks like Scrum and Data-Driven Scrum, ChatGPT-4 and future versions promise to revolutionize the way data science teams operate. By embracing these new tools, teams can unlock new levels of efficiency, innovation, and success in their projects.