[Side Projects After Work] Big Data Analysis Engineer Practical Exam (Task Types 1, 2, and 3)

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  appleskim5694

  Reviews 1

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  Average Rating 5.0

  06/17/2023

  5

  100% enrolled

  I finished the course a week before the exam! I wrote the code myself and compared it with the lectures as instructed by the instructor. It was a great learning method! I think it was a good idea to take the course because the instructor answered my questions quickly and in detail! I will adapt by reviewing the lectures and solving Kaggle problems for the next week! If you are thinking about taking the course, I highly recommend it!

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  • roadmap

    Instructor

    06/21/2023

    Thank you for recommending it as a good study method!! I feel like I've grown a lot when I look at Taebum's questions!! I hope you organize the remaining period well and achieve good results.

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  • appleskim5694

    07/07/2023

    Teacher, I passed with a stable score!! Thank you for everything😄

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    5
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  thstmddns

  Reviews 12

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  Average Rating 5.0

  07/08/2024

  5

  75% enrolled

  Hello, teacher? I am finally leaving a review. After passing the 7th practical exam with great enthusiasm, I challenged the 8th exam and passed. For the 7th practical exam, I bought a textbook and studied by memorizing all the codes in the textbook. However, on the day of the exam, the codes in my head were all jumbled up and I couldn't use them properly, so I suffered a bitter defeat. When preparing for the 8th re-examination, I took the teacher's lecture. I took the lecture at 3x speed for 4 days. And I studied by solving the materials provided and comparing the source codes every time I took the lecture. Using the tips the teacher gave me during class, I was able to solve the problem in 1 hour on the test day and got 90 points. (I was the first to leave the test room I was in haha) What was helpful about the teacher's lecture was that 1. Consistent writing method can be applied - My previous study method was to memorize the codes in the textbook and blog, so I had to memorize numerous codes for the problem, but through the teacher's lecture, I was able to prepare as a single process to prepare for any problem. 2. Data provision - The data provided are blank spaces and source code colab files. After attending each lecture, I was able to quickly get a feel for it by writing code directly in the blank space file, executing it, and comparing it with the source code file. 3. Task 3 - With the tips given in Task 3, I was able to memorize 3 lines of code and get 20/30 points. 4. Email - The information about the exam that you sent me via email a week before the exam was very helpful (especially the mind map). Thanks to you, I was able to obtain the engineer's license and also develop my skills in AI development, which I am currently working on. Thank you!!!

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  • roadmap

    Instructor

    07/08/2024

    Congratulations on passing :) You watched the lecture at 3x speed and left the room first~~ You seem to be a talented person!! Thank you so much for pointing out the helpful parts and explaining them!! Since you're going into AI, I think I'll see you again at another gathering someday! You worked hard. 💪💪💪

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  wonbin08135415

  Reviews 1

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  Average Rating 5.0

  07/09/2023

  5

  100% enrolled

  Hello. I passed the practical exam in "Non-major + First Exam". I was lucky to pass the written exam because it was mostly about memorization, but when I actually tried to prepare for the practical exam, I was at a loss. I thought that self-study would not work, so while looking for a lecture, I happened to come across Mr. After Work's lecture on YouTube, and I took the lecture through Infraon. What helped me while studying were: 1. Easy explanations that even someone like me who had never touched Python could understand 2. Curriculum structure just for the Big Data Analysis Engineer practical exam (the best) 3. Review using blank notes, Kaggle, etc. 4. Quick feedback on questions Etc. I studied while working, but just listening to the lecture and reviewing was a great help and not difficult. In the actual practical exam, I couldn't solve the 3rd type 2 question that I had never thought about at all, so I turned it in blank (15 points deducted), but I got everything else right and passed with a score of 85 (final result). I am so grateful that I was able to pass the exam and learn the charm of Python through Mr. Afterwork's lecture. Now, I want to continue learning not just by passing the exam, but to acquire more functions of Python that I don't know about and to utilize them more. I hope that it will be helpful to those who are considering taking the course, so I am leaving a review. I sincerely thank you for creating such a great lecture!!

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    Instructor

    07/14/2023

    I am so touched that you summarized 4 helpful parts. I think it must be the result of studying hard while working hard 😍 I sincerely congratulate you on passing the exam, and there are so many things you can do with Python, so I recommend you take on another challenge before you forget it!! You worked hard 👏👏👏

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  donedone

  Reviews 1

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  Average Rating 5.0

  07/07/2023

  5

  100% enrolled

  Wow.. When I was studying on my own for the first time in the Big Quarter practical exam, there were a lot of things I didn't understand, so I ended up failing. Then I found out about the After-Work Others lecture, so I took it. I didn't have much time because I was studying while working, but I was able to pass on the second try thanks to the recommended coding and various tips! Data analysis is a difficult field, but if you only take the exam, there's no lecture better than this one. Thanks to you, I passed smoothly with a score of 90, so I'm so happy! I'll have to study data more in the future, using Kaggle, etc.

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    Instructor

    07/07/2023

    90 points! Wow!! Congratulations on passing with a high score. You listened to the lectures on content you didn't understand and improved your skills to this extent!!! You're talented :) 👏👏👏

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  abcd581262671

  Reviews 1

  ∙

  Average Rating 2.0

  11/01/2025

  Edited

  2

  31% enrolled

  I'm currently enrolled in a data-related major and completed a 4-month AI bootcamp. I've never learned machine learning at school, and since I relied on GPT during the bootcamp, I consider myself a 'novice who's only heard a lot' and purchased this course for certification. I haven't finished the entire course yet and am writing this midway through, so I'm not sure how my opinion might change. This isn't to say the materials or lectures are terrible. It's just that there are many areas that seem lacking to me. If you don't mind the usual speaking style or pace, aren't sensitive to details, understand quickly, or can review well on your own, this probably won't matter. Overall review == You don't seem to explain things well. The reviews seem much better compared to the actual teaching quality. I am sensitive to sound, but am I the only one who feels this way? Whether you're uploading at 1.1x speed, speaking too fast, or have poor pronunciation, you trail off at the end of sentences and then suddenly speed up when moving to the next point, which is very uncomfortable. You seem to explain things hastily. Your speaking pace varies dramatically between fast and slow. Are you editing out silent pauses? The more I listen to the lectures, the more breathless and suffocated I feel. Like a radio advertisement, the explanations continue without any breathing space. At 1x speed, many parts fly by too quickly to understand, but at 0.9x speed, there are uncomfortable issues like parts becoming too slow or audio distortion, making it too frustrating to listen. Even with content I've written about before, listening to the lecture makes it harder to organize in my head, and the lecture content just passes through my ears. Even when I try to focus and listen properly, the pace is inconsistent, there's too much information, and it feels like you're just reciting what you know rather than explaining for my benefit. It seems like you're recording in real-time rather than practicing and then filming. I wish you would prepare systematically and explain accordingly. As everyone can see from the machine learning block videos on the desk, there are too many unnecessary comments like 'let me move this, let me bring this back, let me do this like this' that aren't related to the lesson. These seem like things you don't need to say, but you mention every single one. This distracts attention and breaks the flow of content, completely breaking concentration. Same with other Colab videos. Using hands-on demonstrations instead of PPT actually seems to have lowered the quality. From someone who already knows the content, it felt like you were making it more difficult to explain. Beginners have no idea what you're talking about. I think you need to organize the content before speaking. Please make Colab names match the curriculum names or maintain consistent formatting. These details falling short makes the lectures look even worse. Explanations should fully acknowledge that others are beginners and be tailored to their perspective, but this isn't even good for review. In my view, it's too fast and chaotic for beginners - you'd probably need to go through it about 3 times. The current lectures are honestly just you explaining things alone, with a structure where students must read and understand on their own, and memorize by themselves. You said not to worry because you repeat things multiple times, but the more I listen, the more worried I become. Wait, I don't understand anything and the lecture is over? Should I watch the lecture again? Continuing to listen is tiring and makes me sleepy, and when will I memorize? It just creates complicated feelings. I think the reason is the explanation method + details. You explain with method A, then switch to method B, explain point 1, then additionally explain point 2... Could you please explain step by step in order? You should reorganize the flow of how to explain things in your head before filming. Try explaining like this... Main concept explanation - Basic code and basic results - Problem explanation - Code applied to the problem - e.g.) One-hot encoding is used in these situations, done like this. - The basic code looks like this and means this. The results come out like this. - So, with this actual data and this problem? - The code I just explained would change like this. (Explain the changed code parts) - Since I said one-hot encoding does this, the code results would come out like this, right? - Looking at these parts, you can see how, why, and what changed due to this. Do you understand what I mean...? I usually don't bother writing reviews like this and just move on. But I'm really frustrated. Other people will purchase this later too, and I hope they don't waste their money. I'm also writing this long review because I feel bad. If you see this review and still think it's just one person's opinion, that you explain well, and you're too busy to handle minor details, etc. That would be exactly what bothers me. Please take a few of your videos and review them again. Consider whether someone who knows nothing could understand this immediately, organize it in their head, and follow along well when listening to this.

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    Instructor

    11/03/2025

    Thank you for the feedback. I'll refer to the points you mentioned and work on improving in a better direction while keeping the lecture time from getting longer!

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