It’s about time Data Scientists thought about embracing ChatGPT

ChatGPT has found itself at the centre of various actual data science applications, notably in analysing social media sentiment

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ChatGPT’s many applications are well documented: People are finding different ways to coerce OpenAI’s hit sensation into accomplishing various tasks—beginning with answers to mundane questions like what is Shakespeare’s most famous work and going up all the way to helping build cliched screenplays. The AI model is constantly learning by training on a virtually endless pile of text-based data, combing for relationships between various words within to respond to queries based on stacks of guesses. Is it perfect? Far from it, but while the model is still very much a work in progress, its data-intensive foundations could be harnessed by data scientists to accomplish so much.


Demystifying data through AI

ChatGPT’s many applications are well documented: People are finding Users correspond with ChatGPT via prompts to obtain a result. These prompts, alternatively, could be utilised to make sense of extensive data sets by extracting insights accurately. Data scientists can engage with ChatGPT to obtain natural language responses to complex problem statements. Its versatility makes it a great tool to not only parse the Internet for common answers but improve the efficiency of data science workflows. ChatGPT can help make data simple in many ways: It can be used for content generation, data summarisation, and data cleaning, and also for machine translation, cutting down time and costs.

How exactly?


ChatGPT has found itself at the centre of various actual data science applications, notably in analysing social media sentiment, predicting customer behaviour to aid with marketing strategies, and, most famously, generating text summaries from large banks of text. As a data science tool, ChatGPT can inform politics, academia, and corporate plans. Its proficiency with language and words can quicken the laborious process of data cleaning and pre-processing by extracting and structuring relevant information from unstructured data, automating data labelling and sentiment analysis, and identifying and correcting data entry errors. Its language capabilities can also make collaboration among team members easier by generating clear analysis summaries that can be shared easily and translating convoluted technical material into simpler terms.

ChatGPT can help with generating descriptive analytics reports by summarising key trends and patterns in data and converting elaborate visualisations into easily understandable explanations. It can also improve exploratory data analysis (EDA) by going over the data and offering possible hypotheses to investigate, guiding the user through feature selection and data transformation, and generating appropriate questions to instigate further investigations.

Furthermore, ChatGPT can simplify dashboards for people not acquainted with them. Through automated commentary and insights, ChatGPT can provide straightforward descriptions of trends, patterns, and contextual information to understand the data more reasonably while delivering actionable suggestions based on insights obtained from the dashboard. Other instances of ChatGPT proving useful to data scientists include video and image recognition, wherein it can identify media content through captions and help make analysis easier. And while creating predictive models, ChatGPT can ease a significant amount of burden—firstly, by helping choose the appropriate machine learning algorithm that fits the data and objectives; secondly, by offering guidance on parameter fine-tuning; and finally, by creating easily readable interpretations of model outputs for the benefit of conveying results to non-technical stakeholders.



ChatGPT isn’t flawless as a chatbot, and—by extension—it isn’t perfect as a data science tool. One of the limitations to consider when using the model in data science applications is its issues with substantiating context. While it may produce text that might seem fine and faultless, but it doesn’t necessarily understand the reason why said text is used, which can cause inaccuracies in specific applications. Ironically, the biggest problem could very well be the data itself: ChatGPT is a self-learning system reliant on high-quality training data to provide error-free answers. In cases where the data is compromised, biased, or taken from dubious sources, some applications might run into trouble. Finally, the high level of computing resources required to optimize these models can pose a hindrance to some companies looking to adopt them.

A bubble waiting to burst?

Data science is, in many ways, the poster child of modern technology, commonly among the most sought-after domains for professionals. With the emergence of ChatGPT occurring simultaneously, there exists massive potential at the intersection of these two topics. We can expect the generative AI model to make strides in the coming years and sort out blips concerning scalability and interpretability. As the tool gradually refines itself and processes other data (images, audio), it will welcome the possibility of integrating itself with other machine learning models and tools to create more powerful data science workflows. The future could witness expanding ChatGPT to other areas of data science like sentiment analysis and customer service. And with a more considerable scale, real-time processing of massive data in production environments could be possible. Data scientists can soon look over and beyond leveraging ChatGPT’s current data-interpreting capabilities and firmly utilise it as a powerful data tool to produce precise insights and predictions.

The article has been written by Dr Abhinanda Sarkar, Director Academics, Great Learning