5 Widely-used GPTs to enhance your data science workflows
GPTs are the Generative Pre-trained Transformers that refer to a class of LLMs used for a variety of applications and generate different kinds of content. In the field of data science also, their applications are revolutionizing the industries. They are trained on massive datasets of texts, and codes and can effectively understand human language to generate outputs based on prompts.
These simple Generative AI models are proving to be highly beneficial for data scientists who are using these models to automate various data science tasks, improve their efficiency and accuracy, and gain deeper insights from data.
As per a recent study by IDC, the global AI in data analytics market is expected to reach a whopping $204.3 billion by 2025. This means we will be seeing rapid adoption of GPTs in the data science industry. If you are looking to get into a data science career, then here are some of the most popular GPTs you can leverage to make your work faster and more accurate.
1. Data Analyst
Well, this is not a data science job role we are talking about, but a custom GPT model named – Data Analyst that has been designed to expedite the data exploration and data visualization process.
As per a survey by Kaggle, a significant amount of data professional’s time is consumed in data cleaning and exploration tasks. So, this incredible data science tool helps in analyzing datasets and generating various visualizations as per data scientists’ prompts.
For example – a data scientist can ask Data Analyst to “create a scatter plot comparing customer age and purchase frequency”. With this simple prompt, they can get relevant outcomes. This not only helps save their precious time but also helps with faster work processes.
Data Analyst also comes with a few limitations as well. It cannot properly handle huge datasets and the visualizations generated by it may require further human interventions.
2. Data Cleaner
Cleaning and refining data is another time-consuming task for data science professionals but it is an essential task to do as the overall outcome of the data science projects depends on it. As per a study by Experian, data quality issues cost businesses in the US an estimated $3.1 trillion every year.
Data Cleaner is a popular Generative AI GPT model helpful in automating various data cleaning tasks such as identifying and handling missing values, correcting inconsistencies, formatting data for analysis, and more. It helps a lot of time and human errors and allows them to focus on more analytical tasks.
But to use Data Cleaner, you need to have excellent domain expertise as GPT suggestions may not be always a perfect solution for your project and you need to verify the outcomes with your own data science skills and knowledge.
Consider enrolling in top data science certification programs to learn how to effectively use GPTs in your data science career.
3. AutoML GPT
AutoML or Automated Machine Learning refers to the process of automating various aspects of the machine learning model development process. GPT can greatly help to enhance the AutoML by assisting them in tasks like feature selection, and hyperparameter tuning.
Deloitte in its recent report highlighted that adoption of AutoML has increased over time as currently, 42% of businesses have reported using it in their AI projects.
By using AutoML with GPT, professionals can significantly streamline model building. However, it is important to understand the underlying logic behind the chosen features and hyperparameters.
4. ScholarGPT
It’s challenging to stay updated with the latest research in the ever-evolving data science industry. New tools and technologies are coming up every day. So, ScholarGPT offers a complete solution for this problem by serving as an efficient research assistant.
When data scientists feed keywords or research topics in ScholarGPT, they get a prompt response and recommendations for relevant academic papers. This is specifically beneficial to save time in searching for literature and ensures researchers are aware of the latest advancements in this domain.
5. Code GPT
Every data science project involves extensive coding and Code GPT offers to be a great help in various types of coding processes. It can help to generate code snippets, translate natural language instructions into code, and may even suggest how to enhance the codes.
This in turn helps to improve data science professional’s coding efficiency and helps them reduce errors along with completing their tasks faster.
For example – A data scientist can give this tool a prompt like “Write a Python function to calculate the average customer order value”, and get relevant outputs.
Conclusion
These Generative AI models of GPTs are revolutionizing the data science industry by making tasks more accurate, error-free, and faster. It is also helping data science professionals save lots of time and focus more on strategic and analytics tasks. However, the full-fledged use needs proper human oversight as they can come with bias and deter outcomes.
As we move towards the future and these Generative AI tools are gaining popularity, we can expect more evolved and customized tools to be launched that will serve specific data science needs. By properly leveraging GPTs strategically, data science can drastically improve their efficiency.
