쉐도잉 연습: The BEST Coding Interview Roadmap in 2023 (free) - 영상으로 영어 말하기 배우기
레슨 만드는 중...
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So I created a leak code roadmap and made it free for everyone.
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But before I show you, make sure to like and subscribe.
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Nevermind, let me just show you.
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It's sort of like a non -linear video game where you kind of have options of
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which order you want to complete each topic in.
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Except, of course, this is leak code, so it's definitely not going to be as fun.
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But the reason I made this is that I've kind of had a visual map of this in my head.
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I kind of know the relationship between these topics.
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Like, for example, linked lists are kind of your introduction to graphs, where you have nodes and edges, and then right after that you learn about trees.
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Most people will learn this in a computer science course.
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But there's definitely a lot of topics here you probably won't learn in class, things like tries, which naturally come after trees.
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But remember that trees also cover binary trees, and before you learn about binary trees, I think it makes sense to learn about binary search
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because it's kind of the same algorithm that you use binary trees for.
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I've been wanting to make this for a very long time, but I just finally had time during the holidays.
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It feels good to have a visualization for my thoughts, but I think also this will help a lot of people.
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Whether you're a beginner being introduced to these topics, the first topic is arrays and hashing.
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You don't really need to know a lot of data structures and algorithms to solve this topic.
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You just need to have programming knowledge about loops and conditionals
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and of course basic data structures like arrays and hash maps
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but you don't really need to know how they're implemented
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and after that you learn more complicated things but you do
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so in a very structured way and
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if you're completely brand new to data structures
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and algorithms you can always check out my courses
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and get lifetime access to them we recently started adding articles
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to complement all of the video solutions that's my short little plug but moving on, I consider most of this map pretty objective.
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I think most people would agree that two -pointers should come before binary search, because binary search is sort of a specialization of two -pointers.
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The same thing could be said about sliding window, and similarly linked list, a lot of these linked list problems require two -pointers,
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so I think it makes sense to learn two -pointers before that.
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And of course, trees should come before linked lists.
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It's a more complicated version of a linked list.
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And then right after trees, things get pretty open ended.
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Like I said, we have tries, but we also have heaps, which are a specialization of binary trees.
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They are also a bit more complicated, I think.
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And we also have backtracking, which I think makes a lot of sense to learn after trees, because backtracking is essentially just a big decision tree.
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Like looking at one of these thumbnails, you can kind of see it here backtracking problems are naturally suited to trees, at least in terms of visualization.
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And then after backtracking things get even more open -ended, we move on to graphs, which we already kind of learned about with trees and linked lists.
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And many graph problems require recursive backtracking, usually depth -first search, so I think it makes sense to do it in this order.
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Also, we have one -dimensional dynamic programming.
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Dynamic programming is kind of a specialization of backtracking where you add caching and then, you know, there's the true dynamic programming solution,
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which looks a bit more concise, something like this.
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And that usually doesn't involve recursion, but I still think learning backtracking and getting good at it before tackling dynamic programming will make it a lot easier.
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And the reason I have two -dimensional dynamic programming after graphs,
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of course, we have it after one -dimensional dynamic programming because two -dimensional dynamic programming problems typically involve graphs.
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I think unique paths is one that does involve like a two -dimensional graph, longest increasing path in a matrix.
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You can imagine that's also going to be related to graphs.
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But we also have advanced graphs, which are kind of more academic related algorithms.
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Things like Dijkstra's algorithm with network delay time, you'll need a Dijkstra's algorithm.
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And that algorithm also involves a heap.
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It's the shortest path algorithm.
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So it makes sense to have advanced graphs come after the
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heaps category at this point i'm sure you're starting to get
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an idea of what i'm talking about all these topics are related to each other
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and learning it in that way can kind of help you solve individual problems
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if you couldn't solve a problem in one of these complicated
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categories there might be a reason for it might be
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because you're lacking in one of the more simple categories
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and it can be very difficult to figure that out
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because as you're just going through a single big list of leak code problems in a random order, you don't really know that.
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It's hard to figure out what you're good at and what you're not good at, and what you might need to practice more on.
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And some of these, I will admit, are a bit opinionated.
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Things like intervals.
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Why would you need to learn intervals after you learn about heaps?
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Well, there's not necessarily a good reason, though interval problems typically require sorting, which is different from heap sorting.
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But the main reason I put this here after heaps is because, first of all, intervals are probably a bit less important than heap problems,
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but also because one of these problems does require a heap but most of them don't.
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So you could, you know, make an argument that some of this list could be a little bit different.
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You could say the same thing about these greedy problems.
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Definitely not all of these greedy problems will require a heap.
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I think most of them will not.
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But still, I think greedy problems are less important than heap problems.
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Then last couple ones, we have bit manipulation.
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Again, I put this after two -dimensional or one -dimensional dynamic programming because one of these I think does involve dynamic programming.
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But other than that, there's no reason that you should learn bit manipulation after dynamic programming.
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But I do think it's generally less important than dynamic programming, so it makes sense.
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And math and geometry, though, I think this one makes a bit more sense because you should learn this after you learn graphs, because a lot of these are related to matrices.
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And some of these also do involve bit manipulation.
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I think these three do.
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But I guess if I wanted to make this map as accurate as possible, instead of having a topic like arrays and hashing, and then right after that,
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we have new topics like two -pointers.
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Does it really make sense for somebody to have to solve all of these problems, even some of the more difficult ones, before moving on to some easy two -pointer problems?
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Like some of these are pretty easy.
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I would argue definitely not.
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So I'm also thinking about creating a new version of this graph that's much more detailed.
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Like essentially we'll have multiple nodes for two -pointers.
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We'll have multiple blocks and maybe one of them will be the easy two -pointer block
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and then after that you can solve easy binary search problems.
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And then maybe after that, you can solve medium two -pointer problems.
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But if I did that, you can tell that this graph would have been ginormous.
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It's already pretty big.
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So I wanted to first start out with like a simple version.
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But if people are interested in me creating a more complex one, that's a bit more granular, I'm happy to do that.
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But can you believe that this is free?
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Every single one of these, when you open it up, it has a list of problems.
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Every single one of these problems has a video solution that I created on YouTube.
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It also has code in multiple languages not just python
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but java javascript and c plus plus we're working on adding even more than
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that feel free to contribute on github i also added a
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few more quality of life features things like being able to
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sort by the problem name also by the difficulty things
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that people have been asking for a long time you can also star problems some people were asking for
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that so feel free to you know star all the problems you want also you have to admit
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that it's kind of neat that this is a graph when we're learning about data structures and algorithms which, you know, graphs, you know, you know what I'm talking about.
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And, you know, you can play around with this.
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You can enable dragging, which will let you kind of move these around.
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But, you know, if you do that and you let go of it, it's going to end up opening the panel.
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Sorry if that's annoying.
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And yeah, I mean, did I mention that this is completely free?
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So definitely try it out.
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If you want to do me a favor, tell everyone, you know, your friends, your parents, your girlfriend, oh wait, your dog.
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Tell everyone.
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And especially, please, please tell me any feedback you have, any suggestions i literally read everything i don't always have time to respond
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and i don't always have time to actually implement the suggestions
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but i read every single one i literally obsess over making the site better
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and providing more value to people i'm really looking forward to
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what you guys have to say oh god somebody turned
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that light theme off i don't even know why i implemented
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that but thank you so much for watching hopefully i'll see you pretty soon.
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이 레슨의 어휘와 말하기 포인트
이 C1 수준 말하기 레슨은 영상 “The BEST Coding Interview Roadmap in 2023”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: graph, tree, learn, programming, dynamic. 이 영상에는 섀도잉할 문장 118개와 단어 1686개가 있습니다. 말하는 구간의 길이는 8:20입니다. 화자는 분당 약 202단어로 빠르게 말하므로 연음과 약하게 발음되는 소리가 많습니다. 영어에서 가장 많이 쓰이는 3,000단어에 속하는 단어가 81%뿐이라 어휘가 어려운 편입니다.
이 영상의 핵심 어휘
영상에서 가장 어려운 단어 14개를 발음, 뜻과 함께 정리했습니다.
| 단어 | 발음 | 뜻 |
|---|---|---|
| graph 명사 | /ɡɹæf/ | 그래프, 도표 |
| pointer 명사 | /ˈpɔɪn.təɹ/ | 교편 |
| algorithm 명사 | /ˈælɡəɹɪðm̩/ | 알고리즘, 알고리듬 |
| binary 형용사 | /ˈbaɪ.nə.ɹi/ | 참거짓, 진리 |
| interval 명사 | /ˈɪntɚvəl/ | 간격 |
| hash 명사 | /ˈhæʃ/ | 해시 |
| greedy 형용사 | /ˈɡɹiːdi/ | 욕심 많다, 탐욕스럽다 |
| leak 명사 | /liːk/ | 누설, 루설 |
| node 명사 | /noʊd/ | 결절 |
| thumbnail 명사 | /ˈθʌm.neɪl/ | 엄지손톱 |
| beginner 명사 | /bəˈɡɪnɚ/ | 초심자, 초보자 |
| cache 명사 | /kæʃ/ | 캐시 |
| geometry 명사 | /d͡ʒiˈɑ.mə.tɹi/ | 기하학 |
| subscribe 동사 | /səbˈskɹaɪb/ | 구독하다 |
이 영상의 문법
화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.
| 문형 | 영상 속 표현 |
|---|---|
| 현재완료진행 have/has been + -ing — 전에 시작해 지금도 계속되는 동작 | I've been wanting · have been asking |
| 수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점 | being introduced · could be said · be related |
| 관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보 | heaps, which are · graphs, which are · sorting, which is |
주의할 발음
화자는 don't, you're, I'm 같은 축약형과 약화된 형태를 32번 사용합니다. 들리는 대로 짧게 발음하세요.
- “th” 소리: algorithm /ˈælɡəɹɪðm̩/, thumbnail /ˈθʌm.neɪl/
- “sh”와 “zh” 소리: dimensional /daɪˈmɛn.ʃə.nəl/, specialization [ˌspɛʃəlaɪ̯ˈzeɪ̯ʃn̩], hash /ˈhæʃ/, manipulation /məˌnɪp.juˈleɪ.ʃən/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/
- 긴 단어 — 강세 위치에 주의: dimensional /daɪˈmɛn.ʃə.nəl/, specialization [ˌspɛʃəlaɪ̯ˈzeɪ̯ʃn̩], manipulation /məˌnɪp.juˈleɪ.ʃən/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/, opinionated /əˈpɪn.jə.neɪ.tɪd/
이 영상으로 연습하는 방법
- 먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
- 0.75배속으로 한 문장씩 섀도잉을 시작하고, 익숙해지면 보통 속도로 돌아갑니다.
- 자신의 목소리를 녹음해 원본과 비교하고, graph, pointer, algorithm 같은 단어에 특히 주의합니다.
쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법
쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.