Luyện nói tiếng Anh bằng Shadowing qua video: think like a business analyst in tech (3 core skills + solving case study)

After graduating college in 2019, I started my first job as a business analyst. (my 1st day at Spotify, 2019) I was excited but I was also really scared because I studied music business at NYU and I didn't have any direct business analytics background, so there were a lot of things I had to learn from scratch.

And with two and a half years of learning from my mentors, colleagues, and through my fair share of trial and errors, these are the three most important skills to have as a business analyst in tech. (1: technical skills, 2: problem solving skills, 3: strategic thinking & business sense) I'll talk about what they actually are with real case examples and how you can learn them whether you're a student or in your full time job.

If you're interviewing, these are also the key skills that companies are evaluating you on for a BA interview.

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So, let's get it started!

Skill no. 1, your technical skills.

Especially if you want to work in tech with an analyst title, SQL is the most important than Excel Google Sheets.

SQL is a language that enables you to pull millions and billions of user information stored in databases to answer business questions.

Let's say you work at Netflix, and some of the questions that you would be asked would be things like how many monthly active users do we have per region per day?

Or specifically, how many monthly active users do we have per region that have finished watching Squid Game?

If you know SQL, you can easily pull these numbers and answer questions fast instead of having to wait for data scientists to employ for you or rely solely on dashboards.

How can you learn SQL?

So I'm going to talk about if you don't have any experience with SQL and if you have some experience.

If you have your experience with SQL, I would recommend starting with datacamp's class on intro to SQL.

This is free and you get to learn the basic SQL logics and command lines@.

I also love how it's super interactive so everything feels very real when you're coding on it.

If you have some experience with SQL or if you're preparing for a SQL text@ screen, I highly highly recommend going to leetcode or Interview Query.

On these websites, you will see all the SQL teg questions from companies like Facebook, Google that were actually asked during the interviews.

And even if you're not interviewing, I find this extremely useful just to see how SQL is used to solve real business problems at specific companies.

I would definitely be sure to ace the easy question and get comfortable with medium questions.

So aside from SQL, data wrangling skills in Excel and Google Sheets is another essential.

Once you pull thousands of rows of raw data at your SQL, the next step is to load the data into Excel or Google Sheets to then organize your raw data and visualize graphs there.

In the typical BA interview for tech companies, there's usually a take home case study where you're given a set of raw data in Excel and you have to analyze the data and extract insights.

And no, you don't have to know all these formulas out there!

In my day to day, I mainly use pivot tables, sumifs, vlookups, index/match.

and if anything it would just be more (sumifs vlookup pivot table index/match) advanced variations of them.

I think what's more important here is knowing how to solve the problem.

As an example, I didn't know sumif existed. (sumif??) All I knew was I wanted to sum all the rows based on a criteria.

I Googled exactly that and I realized sumif can help me do that.

So Google is going to be your best friend when it comes to data wrangling.

How do you learn data wrangling skills?

My biggest tip to learn it is to play around with the data yourself and practice.

If you're in an internship and even if it's not related to data, let your manager know that you like to gain data skills and would love to see if you can work on related projects or even if there's anyone you can speak to.

When I was doing my digital marketing internship at Roc Nation, my internship had almost nothing to do with data analysis.

But because I asked, I was able to get time with analysts and see how they do their analysis in Excel and how they wrangle their data and visualize things.

If you're working full time, the next time you get any data point, ask to see what the raw data looks like in Excel or Google sheet form.

And try to see how people wrangle and structure their data.

What I used to do was making a copy of the sheet and try to do it myself.

And last tip is practice.

The more you do it, the more you will see the pattern building your own systemic way of visualizing things.

Skill no. 2, problem solving skills.

This is about how you can break down ambiguous questions into smaller pieces in a structured way.

So as a business analyst, you often be asked very ambiguous questions in your job or even in your interview during the case study round.

A classic question is a root cause analysis which I get asked all the time at work too.

So get ready for this question.

Imagine you work at Netflix We're seeing monthly active users declining on the Netflix app.

Can you root cause the issue?

What recommendations do you have?

To approach any ambiguous problem, there's a general structure to problem solving that I learned from my mentors.

Break down the problem into smaller questions or pieces, then use data to answer your questions.

After that, you can consolidate your learnings into insights, and based on your insights you provide your business recommendations.

So the first question I'll think about is impact size.

How long have we been seeing this MAU decline, and what is the size of this user decline?

Also look at geography.

Are we seeing this on a global level or in a specific market?

And seasonality too.

Did we see MAU decline last year around the same time too or did it only happen this year?

Maybe it's a recurring pattern and related to seasonality?

And these are some other questions I would look into I then dive into the data to find insights for each of these questions.

The biggest mistake and that's what I used to do when I started was to dive straight into the data and I would drown myself in there for hours and not have a narrative and answer.

Let's say after you analyze data, and you may be analyzing 10 to 15 metric.

But when you communicate the insights in your email to your manager, or in your executive summary in your presentation, communicate them in a top-down way.

So I will say it like this.

Top-down communication is when you start with your key message and then go into your supporting details.

There's also a video about top-down communication like this one, and I highly recommend you to check it out.

How do you learn problem solving skills?

First tip is to watch mock case interviews.

I'll link some good ones down in the description box.

I also love seeing product managers do their mock interviews.

Especially these two on the Exponent channel since it's more tech driven.

In terms of what jobs or internship experiences that are more relevant, consulting is a big one.

And the secret is that's where most tech companies recruit their BA's from.

Internship wise, I highly recommend insights internship, analytics, or market research internships.

Basically internships that have something along the lines of using data to drive actionable insights.

Can be quantitative data or qualitative data doesn't matter.

What you're trying to learn here is to turn data into insights and tell a story.

And regardless of whether you're in school or in a full time job, the next time when someone presents, pay attention to the structure of their presentation.

See how they go from point A to point B to point C.

Think about what they did well and what you would improve on if you were the one presenting.

Skill no. 3, strategic thinking and business sense.

What sets great analysts apart from average analysts?

Is their ability to turn data into actionable insights to guide the business recommendation.

Basically, how would this specific piece of data be actionable for the business?

It's not about how many coding languages you know, it's about what the business could do based on the data you provide that matters most.

So, using our example again on...

We're seeing monthly active users declining on the Netflix app.

Can you recall the issue?

What recommendations do you have?

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Previously, we align on a few questions I will answer with data.

101

But before we go analyze the data, I learned that a way to sharpen my strategic thinking is to pause and think first about the So What?

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of getting each data points or insights are actionable.

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Role playing helps, so imagine let's say we are the CEO of the company, how can this data help us make a decision?

104

For impacts sizing the So What? is that if it's alarge amount of users declining then we should be alarmed and come up with large scale counter-actions.

105

Also, looking at the MAU trend, if it's a spiked drop, it likely will be a one time event, perhaps a bug; if it's a gradual trend, perhaps we aren't finding values using our services; if it's a step change, perhaps we made some changes on our product and it's hurting our growth.

106

So seasonality, So What?

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if it's a recurring trend that happens every year, there's not much we can do but will closely monitor.

108

Geography, So What?

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if we see it on a global level, then it may be a bug in our data that's driving down the numbers everywhere.

110

And you'll be surprised that these things actually happen.

111

If it's a specific market, then perhaps our competitor launched a new feature that's taking away our users or there's a macro level trend happened in the market that we should be aware of.

112

External factors such as competition.

113

The So What? here is we should understand what it is and if it would make sense for us to build something similar or have some counter-actions.

114

Demographic, specifically aged group.

115

The So What? here is if it's happening a specific aged group, perhaps we are underserving this cohort of users and we can think of strategies to make our product and experiences more appealing to this aged group.

116

Breaking down the MAU metric, So What?

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If it's less new users coming in, we need to think about our acquisition strategy; if it's more users turning out, we need to think about engagement strategy to retain users.

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What's really good about doing the So What? exercise is it forces you to think strategically before you even pull the number.

119

So our insights on Netflix MAU decline was around competition and higher churn rates among 25 to 34 y/o in the US.

120

Based on the insights, what are some business recommendations you can provide?

121

Looking back at our So Whats?, you can then build upon them.

122

First, I will think about understanding the competition.

123

We can conduct user research to understand the new feature that the competitor has launched and how we can come up with appropriate counter-actions.

124

Second, double down on engagement strategies to reduce the churn among 25 to 34 y/o in the US 1. we can understand the types of content or feature that this cohort tend to consume the most and increase awareness of them.

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For instance, we can service as a specific content on homepage, talk about the feature via email communications or do a PR announcement of a specific show.

126

Second, we can perform a cohort analysis to identify the key behaviors that make a user highly engaged.

127

For instance, is it about using a specific feature or the number of content watch or the diversity of genre they see that make them a highly engaged users?

128

Based on such, we can then take steps to encourage unengaged users to follow the behavior of engaged users to prevent churn.

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How do you learn strategic thinking and business sense?

130

First, before you analyze any data point, pause for a second and think about the So What? of knowing the data point.

131

Why does the CEO need to know this data and what can the CEO do about it?

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So your insights are strategic versus just plain numbers.

133

For a business sense, this is probably the harder one for me to learn, but a good tip is to have regular conversations with people outside your team, to learn how things work at your company.

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For instance, ask the user research team, if you want to start a user research, what do you need and what is intell?

135

Ask the marketing team what tactics have been done for the engagement strategy and what worked well?

136

Talk to the product team and see how they evaluate the strength of a new feature?

137

As you become more senior in your role, you will then be the one implementing these recommendations end to end with cross functional stakeholders.

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So it's better to know how things work from their end.

139

Hopefully this was helpful in shedding some light on the course skills of a business analysit's in tech.

140

And again, thank you glassesusa.com for sponsoring today's video.

141

You can shop my glasses in the link in the description box.

142

Thank you all for watching and I'll see you in my next video.

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89 câu

Từ vựng và ghi chú luyện nói cho bài học này

Bài luyện nói trình độ B2 này dựa trên video “think like a business analyst in tech”. Những từ được nhắc lại nhiều nhất trong bài: user, Sql, skill, insights, learn. Video này có 142 câu và 2611 từ để luyện shadowing. Phần lời nói dài 12:12. Người nói nói nhanh, khoảng 214 từ mỗi phút, nên sẽ có nhiều chỗ nối âm và nuốt âm. 83% số từ nằm trong 3.000 từ tiếng Anh thông dụng nhất; phần còn lại nên xem trước khi luyện. 32 trong 142 câu là câu hỏi, nên bài này hợp để luyện ngữ điệu câu hỏi và cách trả lời ngắn.

Từ vựng quan trọng trong video

15 từ khó nhất trong video, kèm phiên âm và nghĩa:

Từ	Phiên âm	Nghĩa
insight danh từ	/ˈɪnsaɪ̯t/	sự sáng suốt
internship danh từ	/ˈɪntɜːnʃɪp/	thực tập
recommendation danh từ	/ˌɹɛkəmɛnˈdeɪʃən/	sự giới thiệu, sự tiến cử
analyze động từ	/ˈæn.əˌlaɪz/	phân tích
ambiguous tính từ	/æmˈbɪɡ.ju.əs/	tối nghĩa
lens danh từ	/lɛnz/	thấu kính
competitor danh từ	/kəmˈpɛt.ɪ.tɚ/	đối thủ
metric tính từ	/ˈmɛt.ɹɪk/	mét
geography danh từ	/d͡ʒiˈɑɡɹəfi/	địa lý học
prescription danh từ	/pɹəˈskɹɪp.ʃən/	toa thuốc
dive động từ	/daɪv/	lặn
consolidate động từ	/kənˈsɒl.ɪ.deɪt/	củng cố
criterion danh từ	/kɹaɪˈtɪɹ.i.ən/	tiêu chuẩn
dashboard danh từ	/ˈdæʃˌboɹd/	bảng đồng hồ, bảng điều khiển
macroeconomics danh từ	/ˌmæk.ɹoʊˌiː.kəˈnɒ.mɪks/	kinh tế học vĩ mô

Cụm động từ bạn sẽ nghe

Từ	Phiên âm	Nghĩa
break down động từ		hỏng, hỏng hóc
check out động từ		tính tiền, trả phòng
take away động từ	/ˌteɪk əˈweɪ/	lấy đi, mang đi
wake up động từ	/ˌweɪk ˈʌp/	thức dậy, dậy lên

Những câu đáng luyện lại

Các câu ngắn, trọn ý trong video mà bạn có thể dùng lại khi giao tiếp hằng ngày:

How do you learn data wrangling skills?
Can you root cause the issue?
What recommendations do you have?
How do you learn problem solving skills?
Can you recall the issue?

Ngữ pháp trong video

Những cấu trúc người nói dùng nhiều nhất, kèm đúng cụm từ trong video:

Cấu trúc	Trong video
Câu điều kiện if + mệnh đề, will/would + động từ — điều kiện và kết quả	if anything it would just
“Used to” used to + động từ — thói quen hoặc tình trạng trong quá khứ nay không còn	used to do
Câu bị động be + quá khứ phân từ — nhấn vào việc xảy ra, không phải người làm	would be asked · were actually asked · be asked
Thì hiện tại hoàn thành have/has + quá khứ phân từ — việc đã xảy ra nhưng còn liên quan đến hiện tại	I've worn · have finished · has launched

Phát âm cần chú ý

Người nói dùng 30 dạng rút gọn, ví dụ you're, I'll, I'm. Hãy nói theo dạng ngắn đúng như bạn nghe.

Âm “sh” và “zh”: internship /ˈɪntɜːnʃɪp/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, actionable /ˈæk.ʃə.nə.bəl/, visualize /ˈvɪʒuəˌlaɪz/, prescription /pɹəˈskɹɪp.ʃən/
Từ dài — đặt trọng âm cho đúng: recommendation /ˌɹɛkəmɛnˈdeɪʃən/, actionable /ˈæk.ʃə.nə.bəl/, ambiguous /æmˈbɪɡ.ju.əs/, analytics /ˌæn.əˈlɪt.ɪks/, competitor /kəmˈpɛt.ɪ.tɚ/

Những âm người Việt thường gặp khó:

Cụm phụ âm cuối — đọc đủ từng âm, đừng nuốt âm cuối: analyst /ˈænəlɪst/, cohort /ˈkoʊ̯ˌhɔɹt/, lens /lɛnz/, analytics /ˌæn.əˈlɪt.ɪks/, dashboard /ˈdæʃˌboɹd/
Âm /tʃ/, /dʒ/ và /ʒ/ — tiếng Việt không có, đừng đọc thành “ch” hay “gi”: churn /t͡ʃɝn/, visualize /ˈvɪʒuəˌlaɪz/, geography /d͡ʒiˈɑɡɹəfi/, gradual /ˈɡɹæd͡ʒuəl/, scratch /skɹæt͡ʃ/
Âm /æ/ — mở miệng rộng hơn âm “e”: analyst /ˈænəlɪst/, wrangle /ˈɹæŋɡəl/, analyze /ˈæn.əˌlaɪz/, actionable /ˈæk.ʃə.nə.bəl/, ambiguous /æmˈbɪɡ.ju.əs/

Cách luyện với video này

Nghe hết video một lần, chưa cần nói, và ghi lại những từ bạn chưa biết.
Bắt đầu ở tốc độ 0,75×, nói đuổi từng câu, rồi quay lại tốc độ bình thường khi đã quen.
Ghi âm giọng mình rồi so với bản gốc, chú ý các từ như insight, internship, recommendation.

Phương Pháp Shadowing Là Gì?

Shadowing là kỹ thuật học ngôn ngữ có cơ sở khoa học, ban đầu được phát triển cho chương trình đào tạo phiên dịch viên chuyên nghiệp và được phổ biến rộng rãi bởi nhà đa ngôn ngữ học Dr. Alexander Arguelles. Nguyên lý cốt lõi đơn giản nhưng cực kỳ hiệu quả: bạn nghe tiếng Anh của người bản xứ và lặp lại to ngay lập tức — như một "cái bóng" (shadow) đuổi theo người nói với độ trễ chỉ 1–2 giây. Khác với luyện ngữ pháp hay học từ vựng bị động, Shadowing buộc não bộ và cơ miệng phải đồng thời xử lý và tái tạo ngôn ngữ thực tế. Các nghiên cứu khoa học xác nhận phương pháp này cải thiện đáng kể phát âm, ngữ điệu, nhịp điệu, nối âm, kỹ năng nghe và độ lưu loát khi nói — đặc biệt hiệu quả cho người luyện IELTS Speaking và muốn giao tiếp tiếng Anh tự nhiên như người bản ngữ.

Phương pháp shadowing: đọc hướng dẫn từng bước đầy đủ →

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