Last year's version of OpenAI GPT-3 already had some interesting insights about the coming year's data science trends, see more about it here. However, the past few week's hype about ChatGPT implies that something big is happening now, and the technology has reached at some important milestones. As the new year is also here, I went on and followed my tradition and asked the AI the big question, see it with the answers listed below:

𝐖𝐡𝐚𝐭 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐭𝐡𝐞 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐝𝐚𝐭𝐚 𝐬𝐜𝐢𝐞𝐧𝐜𝐞 𝐭𝐫𝐞𝐧𝐝𝐬 𝐢𝐧 2023?

1. Automation and AI-driven analytics: Automation and AI-driven analytics will continue to play a major role in data science trends in 2023. This will allow organizations to process and analyze large amounts of data quickly and accurately, enabling them to make better decisions and increase their efficiency.   2. Augmented analytics: Augmented analytics is the combination of machine learning and natural language processing to help data scientists visualize data quickly and effectively. This technology will allow data scientists to quickly identify and explore patterns and trends in data, enabling them to make faster and more informed decisions.   3. Explainable AI: Explainable AI is the ability to explain the results of a data science model to non-technical audiences. This technology will help bridge the gap between data scientists and non-technical decision makers, allowing them to understand the results of data science models and make better decisions.   4. Edge computing: Edge computing will become increasingly important in 2023, as more and more devices will be connected to the internet and data will need to be processed in real-time. This will allow data scientists to quickly process and analyze data from devices at the edge of the network.   5. Data privacy and security: As more and more data is collected, stored and analyzed, the importance of data privacy and security will become increasingly important. Data scientists will need to ensure that data is secure and that privacy laws are adhered to. Visuals: during the last two weeks of 2022 I collected about 10k tweets containing the hashtag #datascience. Then extracted all the tags from each tweet, and draw their hashtag network. In this network, each node corresponds to a hashtag, while two tags are linked if they were co-tweeted. Finally, I applied some filtering to keep the backbone of the network with the most important nodes and links.