In the era of data-driven decision-making, the complexity of data analysis necessitates advanced expertise and tools of data science, presenting significant challenges even for specialists. Large Language Models (LLMs) have emerged as promising aids as data science agents, assisting humans in data analysis and processing. Yet their practical efficacy remains constrained by the varied demands of real-world applications and complicated analytical process. In this paper, we introduce DSEval -- a novel evaluation paradigm, as well as a series of innovative benchmarks tailored for assessing the performance of these agents throughout the entire data science lifecycle. Incorporating a novel bootstrapped annotation method, we streamline dataset preparation, improve the evaluation coverage, and expand benchmarking comprehensiveness. Our findings uncover prevalent obstacles and provide critical insights to inform future advancements in the field.

This is a list of resources I think would be useful for those who are just starting to explore the amazing Machine Learning domain of Computer Science and want to learn more about Neural Networks and their applications. The general idea behind putting these resources together and publishing this list is that when I just started I saw posts with hundreds of links without description and I simply didn't know which of them are worth spending time on. Focusing on most useful ones and giving short summaries instead is a good idea. I am not a Deep Learning expert and everything I wrote down is just my personal experience with these resources, very subjective opinion. In-depth Convolutional Neural Networks course highly recommended if one wants to learn about image recognition, Computer Vision-related problems and so on. The problemset is amazing; it has probably the best numpy tutorial I have ever seen and makes people implement algorithms they saw in lectures in pure Python numpy, which seems to be a great idea as it helps to get better understanding of how everything actually works.