跟讀練習: Data Science Interview Prep: 3 Key Resources You Need to Know - 通過影片學習英語口語

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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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音標練習掌握每個音

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你能學到什麼

通過這段影片，你能掌握英語對話中自然斷句和資訊重點突出的技巧。比如 speaker 在回答問題時，會用短暫停頓分隔不同要點，還會通過重讀強調關鍵資源（如“部落格”“課程”）。此外，你還能學會如何用簡潔的連線詞（如“不過”“此外”）讓表達更流暢，這對提升口語邏輯性很有幫助。

注意這些語音現象

影片中存在不少連讀和弱讀現象。比如“preparation is a lot easier”中，“is a”連讀成“izə”；“don't need to”中“to”弱讀為“tə”。還有“pick one or two”裡“one or”連讀成“wʌnər”。這些細節能讓你的發音更像母語者，快跟著影片聽聽看吧！

像母語者一樣表達

想要模仿 speaker 的節奏和重音，不妨試試“shadowing”練習（也就是“影子跟讀法”）。播放影片時，延遲 1-2 秒跟讀，注意他在提到“資源分類”“深度優先”時的重音變化，以及講述多個建議時的語速起伏。比如說到“不要恐慌”時，語速會稍慢，加重“不要”的語氣。通過這種練習，你能逐漸掌握英語口語的自然韻律。

現在就開啟影片，一邊看一邊學英語，結合 shadowing 技巧反覆練習，相信你的發音和口語流暢度會有明顯提升。記得利用 shadowing site 上的資源，多聽多模仿，讓自己的表達更地道！

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
條件句 if + 從句，will/would + 動詞 — 條件及其結果	if learned earlier would benefit · If you know anyone who would be
定語從句 who / which + 從句 — 補充說明人或事物	question, which was · everyone who submitted · people who rely
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	have thought · haven't done

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: Data Science Interview Prep: 3 Key Resources You Need to Know - 通過影片學習英語口語

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你能學到什麼

注意這些語音現象

像母語者一樣表達

影片中的語法

什麼是跟讀法？