LLMs for Data Science

This chapter is about how generative AI can automate data science. Generative AI, in particular LLMs, has the potential to accelerate scientific progress across various domains, especially by providing efficient analysis of research data and aiding in literature review processes. A lot of the current approaches that fall within the domain of Automated Machine Learning (AutoML) can help data scientists increase their productivity and make data science processes more repeatable. In this chapter, we’ll first discuss how data science is affected by generative AI and then cover an overview of automation in data science.

Next, we’ll discuss how we can use code generation and tools in diverse ways to answer questions related to data science. This can come in the form of doing a simulation or enriching our dataset with additional information. Finally, we’ll shift the focus to the exploratory analysis of structured datasets. We can set up agents...

The impact of generative models on data science

Generative AI and LLMs like GPT-4 have brought about significant changes in the field of data science and analysis. These models, particularly LLMs, can revolutionize all the steps involved in data science in many ways, offering exciting opportunities for researchers and analysts. Generative AI models, such as ChatGPT, can understand and generate human-like responses, making them valuable tools for enhancing research productivity.

Generative AI plays a crucial role in analyzing and interpreting research data. These models can assist in data exploration, uncover hidden patterns or correlations, and provide insights that may not be apparent through traditional methods. By automating certain aspects of data analysis, generative AI saves time and resources, allowing researchers to focus on higher-level tasks.

Another area where generative AI can benefit researchers is in performing literature reviews and identifying research gaps...

Automated data science

Data science is a field that combines computer science, statistics, and business analytics to extract knowledge and insights from data. Data scientists use a variety of tools and techniques to collect, clean, analyze, and visualize data. They then use this information to help businesses make better decisions. The responsibilities of a data scientist are wide-ranging and often involve multiple steps that vary depending on the specific role and industry. Tasks include data collecting, cleaning, analyzing, and visualizing. Data scientists are also tasked with building predictive models to help in decision-making processes. All the tasks mentioned are crucial to data science but can be time-consuming and complex.

Automating various aspects of the data science workflow allows data scientists to focus more on creative problem-solving while enhancing productivity. Recent tools are making different stages of the process more efficient by enabling faster iterations...

Using agents to answer data science questions

Tools like LLMMathChain can be utilized to execute Python for answering computational queries. We’ve already seen different agents with tools before.

For instance, by chaining LLMs and tools, one can calculate mathematical powers and obtain results effortlessly:

from langchain import OpenAI, LLMMathChain
llm = OpenAI(temperature=0)
llm_math = LLMMathChain.from_llm(llm, verbose=True)
llm_math.run("What is 2 raised to the 10th power?")

We should see something like this:

> Entering new LLMMathChain chain...
What is 2 raised to the 10th power?
2**10
numexpr.evaluate("2**10")
Answer: 1024
> Finished chain.
[2]:'Answer: 1024'

Such capabilities, while adept at delivering straightforward numerical answers, are not as straightforward to integrate into conventional EDA workflows. Other chains, like CPAL (CPALChain) and PAL (PALChain), can tackle more complex reasoning challenges, mitigating...

Data exploration with LLMs

Data exploration is a crucial and foundational step in data analysis, allowing researchers to gain a comprehensive understanding of their datasets and uncover significant insights. With the emergence of LLMs like ChatGPT, researchers can harness the power of natural language processing to facilitate data exploration.

As we mentioned earlier, generative AI models such as ChatGPT have the ability to understand and generate human-like responses, making them valuable tools for enhancing research productivity. Asking our questions in natural language and getting responses in digestible pieces and shapes can be a great boost to analysis.

LLMs can help explore textual data and other forms of data, such as numerical datasets or multimedia content. Researchers can leverage ChatGPT’s capabilities to ask questions about statistical trends in numerical datasets or even query visualizations for image classification tasks.

Let’s load up a dataset...

Summary

Beginning with an examination of AutoML frameworks, this chapter highlighted the value these systems bring to the entirety of the data science pipeline, facilitating each stage from data preparation to model deployment. We then considered how the integration of LLMs can further elevate productivity and make data science more approachable for both technical and non-technical stakeholders.

Diving into code generation, we saw parallels with software development, as discussed in Chapter 6, Developing Software with Generative AI, observing how tools and functions generated by LLMs can respond to queries or enhance datasets through augmentation techniques. This included leveraging third-party tools like WolframAlpha to add external data points to existing datasets. Our exploration then shifted toward the use of LLMs for data exploration, building upon the techniques for ingesting and analyzing voluminous textual data detailed in Chapter 4, Building Capable Assistants, on question...

Questions

Please have a look to see if you can come up with the answers to these questions from memory. I recommend you go back to the corresponding sections of this chapter if you are unsure about any of them:

1. What steps are involved in data science?
2. Why would we want to automate data science/analysis?
3. How can generative AI help data scientists?
4. What kind of agents and tools can we use to answer simple questions?
5. How can we get an LLM to work with data?

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