쉐도잉 연습: Data Science Interview Prep: 3 Key Resources You Need to Know - 영상으로 영어 말하기 배우기

Hey guys, welcome back to my channel.

In this video, I will do another Q&A session to answer questions I did not get the chance to answer in the last video.

As I mentioned in the last video, my Q&A session will be too long if I answer all questions in a single video, so I split it into two videos.

Last time, we focused on the number one voted question, which was how to land a more senior data scientist job.

What would be different from preparing for entry and mid-level role

if you want to hear my answer to

that question i have a whole video focusing on it

that you can check out to learn more in this video

i am going to cover in four more questions from you

guys these questions range from interview preparation resources to broader career

advice thanks again to everyone who submitted questions let's get started

our first question deals with resources for interview preparation the question

is how to prepare for product analytics data scientist positions what preparation strategy to use what resources to use

and what depth of knowledge required for questions like statistics probability product sense

and python there are actually several great questions here and i'm going to briefly answer all of them

in terms of the resources i recommend i want to point

you to my blog on the resources i use for interview

preparation you can find the link in the description below this blog has all the resources I used.

It also categorizes the resources based on different subjects, so you can easily find what you are looking for.

If all of these resources seem a bit overwhelming, don't panic.

You don't need to learn all of them.

Depth is more important than breadth.

Just pick one or two resources in each category and get started, and use your own preferences to make your choices.

If you like reading, pick a book.

If you prefer watching, pick YouTube videos or an online course.

Preparation is is a lot easier if you work with your natural preferences.

In terms of the depth of knowledge required, it's hard to provide a standard because it varies from coming to coming.

The best way to get the sense of the level of difficulty is to collect some sample interview questions online.

Gastor is usually a very helpful resource in this regard.

However, if you cannot find any sample questions for coming online,

you can also communicate with the recruiter and ask if they can send you more detailed instructions.

While some people hesitate to ask a recruiter for anything, remember that this is their job.

They are motivated to help you.

You can even consider hopping on a call with a recruiter to ask specific questions.

This advice also applies to the part of the question that asks if we need data structures and algorithm for Python questions.

Some comments required it and others don't.

So it's helpful to clarify with a recruiter.

You can even ask for sample Python questions from the past.

If you want more help with Python questions, I made a blog about it with my friend Rob that you can check out.

Just to clarify though, the target audience is data scientists' machine learning.

For product data scientists, the blog makes it clear that it's typically the first two types coding questions.

I also have a video on this topic that may be helpful.

That's all I have to say about this question.

Hopefully you'll find it helpful.

Let's Let's move on to the next question in our Q&A

This question is from Nora and deals with A-B testing The question is, how to have a deep understanding of A-B testing

and past product case interviews that involve it when my work does not provide opportunities for doing A-B testing?

Thanks for your question, Nora

I can recommend three great resources for deepening your understanding of A-B testing Udacity's A-B testing course

my own videos on the topic and the book Trustworthy Online Control Experiments A Practical Guide to AP Testing Specifically,

I suggest tackling those resources in that order

The Udacity course will cover the fundamentals Then watch my videos and read the book to deepen your understanding

The book I recommend is a great comprehensive resource

but you may find that it's not as in-depth as you would like However, the book does a great job of recommending resources for more advanced topics,

such as how to do geo-based randomization.

With these resources, you can advance your understanding of AP testing, even if your work does not give you opportunities.

That's my answer to this question.

Moving on to the next one.

The next question says, I found follow-up questions from case interviews especially hard.

Are there any ways for me to prepare for those?

Thanks to Lucy for asking this.

Follow-up questions can be quite difficult and I find that they're especially hard for people who rely on frameworks

The fact is that most of the time interviewers can tell if a candidate is following a framework

Sometimes they will purposely try to destructure you To avoid getting derailed,

when you practice you want to be able to not only express your ideas but also to defend them

You need to train yourself to think independently and critically Therefore, to answer this question about answering follow-up questions,

I want to briefly talk about how to leverage frameworks and how to develop your critical thinking skills.

First of all, I'm not saying frameworks are totally bad.

Far from it.

Frameworks are helpful when you get started and don't know how to answer certain questions.

However, as you learn more, you're also going to have your own ideas.

This may come from your experience as a customer or your criticism of others' ideas or frameworks.

Incorporating your own ideas into answers is how you make your answers unique and train yourself to think independently.

Now, I will use myself as an example.

When I first started learning how to crack case interviews I felt

that the questions were

so hard I struggled to come up with a single idea for a long time

so I did some research by reading books and blog posts

and watching YouTube videos and learned how others would answer the question at

that stage I thought that the people were

so brilliant to be able to come up with those ideas

but as I learned more I I began to feel

that some of the answers did not make much sense to me For example, in some interviews, when asked how to diagnose if a metric shifted to a negative direction

The candidate uses the framework to talk about different factors without even clarifying how large the change is

and if the change is sudden or gradual

Another example is people recommended looking at 10 metrics from an A-B test And I wondered if this is even feasible

I never monitored 10 metrics in a project in my own work experience

so that made me question the feasibility of that answer

So those answers gave me a great starting point

but I was able to use my own experience

and understanding to make them better I developed my own personal

frameworks to answer certain questions You can find some of them in this video

By taking the time to think through your own personal knowledge and develop more unique answers to questions

you will be far better equipped to defend your answers and respond to follow-up questions

okay we have one more question

or rather two more questions for this q a session these

are from abushak he first asks based on your experience what

things would you suggest someone focus on in the early stage of their data science career

which if learned earlier would benefit throughout the career

100

or at least make sense easier in the long run this is a great question

101

The two things I would say are the most important are communication and technical skills Without good communication skills, putting forth your ideas, getting noticed,

102

and eventually advancing your career will all be much harder.

103

These skills are vital to your career and you can make yourself stand out if you focus on them early.

104

How do you develop good communication skills though?

105

It takes practice, but I have a few suggestions

106

that can help you work on this vital skill first take

107

opportunities to present your work practice is key for developing any skill whether it be in school

108

or the workplace when you have a chance to present take it

109

when you do present your work the preparation you do is

110

critical prep time gives you a chance to think about how to communicate effectively

111

and develop strategies feedback from the audience is also important.

112

Reflecting on the feedback and the question you get from the audience helps you with improving your presentation.

113

Developing good communication skills also means being aware of what communication is.

114

Good communication is not just about talking well.

115

Communication is also about listening.

116

Aim to be a good listener.

117

Listen to others' ideas and concerns.

118

Although everyone on a team or at the company often has the same overall goal, they often have different ideas about how to get there or how to execute.

119

You can learn from their ideas.

120

Being able to really listen is crucial to communication, but it's also a skill many people neglect.

121

So it is smart to learn it early.

122

Besides communication skills, technical skills are another thing I would encourage you to work on early in your career.

123

Technical skills obviously allow you to do your job and are therefore critical.

124

Common technical skills for data scientists include statistics, coding, SQL, machine learning, and data analytics.

125

You should feel comfortable with those technical skills because other people rely on you to provide data-informed decisions.

126

Besides those necessary skills, I would also suggest data scientists learn more software engineering skills.

127

Data scientists work closely with engineers, so knowing the fundamentals of software engineering can prove extremely helpful.

128

For example, understanding things like the software development life cycle, the importance of writing clean and modular code, the importance of writing tests,

129

the difference between web and mobile development, and data quality by logging on front-end versus back-end.

130

Knowing the software engineering fundamentals can give you a distinct advantage as a data scientist.

131

That's my answer to the first question.

132

Abyshek also asked, you and Jesse talk about the importance of market reviews in the preparation process.

133

Can you suggest some places where one can do such things?

134

I mean, if there are any kind of study groups or something like that.

135

Well, the short answer is that I'm not aware of such a community.

136

Actually, I have thought about creating one before, and I did some research about it.

137

What I found was that it needs time and effort to maintain a free community so that members follow certain community rules,

138

such as no advertisements, only post relevant information,

139

etc. So the challenging part was about effectively managing such a community so that everyone benefits by being part of it.

140

If you know anyone who would be interested in being a moderator

141

or community manager I'd be more than happy to advocate it on my channel

142

and encourage people to join It is an excellent suggestion Alright, that's all the questions I have time for in this video Please let me know

143

if you think these Q&A sessions are helpful If so, I can do more videos like this every month or every few months I would appreciate your feedback Finally,

144

don't forget to subscribe to my channel if you haven't done already There is more content to come.

145

I will see you guys soon.

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이 영상, 영어 회화 연습에 왜 좋을까?

이 영상은 자연스러운 구어체가 가득해 영어 회화 연습에 최적입니다. 특히 Q&A 형식이라 일상 대화에서 자주 사용하는 표현들이 많고, 말하다가 멈추거나 수정하는 부분도 있어 실제 발화와 흡사해요. 이를 통해 "shadowspeaks"나 "shadow speech" 연습을 할 때 자연스러운 리듬을 익힐 수 있습니다. 또한, 전문 용어와 일반 용어가 적절히 섞여 있어 IELTS 스피킹과 같은 시험 준비에도 도움이 될 거예요.

자연스러운 표현, 뜯어보기

"If all of these resources seem a bit overwhelming, don't panic." : "overwhelming"이라는 단어로 너무 많은 정보에 압박을 받는 느낌을 정확히 전달하고, "don't panic"은 친근한 조언의 말투입니다. 영상에서 이런 표현을 사용하면 듣는 사람이 편안하게 정보를 받을 수 있어요.
"Preparation is a lot easier if you work with your natural preferences." : "work with your natural preferences"는 "자신의 성향에 맞게 하다"는 뜻으로, 복잡한 내용을 간단히 표현한 예입니다. 이렇게 구체적인 표현을 사용하면 의미가 명확해집니다.
"While some people hesitate to ask a recruiter for anything, remember that this is their job." : "while"을 사용해 대조关系를 나타내고, "remember that this is their job"은 설득력 있는 주장을 하는 표현입니다. 논리적인 대화를 할 때 자주 사용하니 기억해두세요.

영상 영어 공부를 위한 연습 루틴

간단한 "repeat-and-record" 루틴으로 실력을 키워보세요. 먼저 영상을 5초씩 재생하고 멈추며, 화자의 발음과 억양을 따라 말해보세요. 그다음 자신이 말한 것을 녹음해 원본과 비교해보세요. 특히 "shadowspeaks" 연습을 할 때는 리듬과 강세를 주의깊게 살펴야 해요. 하루에 10분씩 이렇게 연습하면, 자연스러운 영어 발화가 익숙해질 거예요. 또한, 영상에서 나오는 표현을 노트에 적어두고, 일상 대화에서 사용해보는 것도 좋습니다. 이렇게 실제로 사용해보면 기억에 오래 남아요.

이 영상의 문법

화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.

문형	영상 속 표현
조건문 if + 절, will/would + 동사 — 조건과 그 결과	if learned earlier would benefit · If you know anyone who would be
관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보	question, which was · everyone who submitted · people who rely
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	have thought · haven't done

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.

섀도잉 방법: 단계별 전체 가이드 읽기 →

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이 영상, 영어 회화 연습에 왜 좋을까?

자연스러운 표현, 뜯어보기

영상 영어 공부를 위한 연습 루틴

이 영상의 문법

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법