I am currently working as a junior data analyst. Janjaemi Coding's lecture is a really good lecture to use as a reference while working in the field. In addition, I strongly agree with the study methods and work advice(?) that he gives during the lectures, which he personally acquired while studying after experiencing the data analysis industry first. If students are still taking this lecture, if you listen carefully to the advice he gives during the lectures and study only according to the advice, I think you will hear that you are good at your job in the future. In particular, as a liberal arts major, I tried to memorize everything and learn all the skills at once while studying, so the progress was slow, and sometimes I focused on things that were not important at the moment, which was inefficient. However, as Janjaemi Coding advised, I now know the meaning of successful learning by acquiring even 50% of the knowledge rather than trying to become perfect all at once, and as a result of changing my learning method in this way, the progress and technical ability improvement were very fast. The company recognized his ability and even scouted him internally as a team that creates machine learning deep learning models in the data analysis team. The next characteristic of Janjaemi Coding's lecture is that he knows well what liberal arts majors don't know well and explains it very easily. Usually, when an expert teaches a beginner, there is something called an Expert Blind Spot. There are parts that an expert in that field cannot properly explain to a beginner what they need. This is because they have already become too much of an expert and forget what beginners found difficult and confusing when they first learned it. However, Janjaemi Coding understands even these parts well and explains them very easily. This is a lecture that I really recommend to beginners. If you listen to Janjaemi Coding's lectures in the series order and learn slowly, you will be able to develop the ability to learn on your own in the future. Thank you.