跟讀練習: think like a business analyst in tech (3 core skills + solving case study) - 通過影片學習英語口語

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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.

Before we get started, Thank you GlassesUSA.com for sponsoring today's video.

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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.

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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?

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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.

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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.

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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.

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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.

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

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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.

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External factors such as competition.

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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.

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Demographic, specifically aged group.

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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.

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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.

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

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

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

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First, I will think about understanding the competition.

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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.

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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.

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

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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?

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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?

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

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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.

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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?

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

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

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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.

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

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

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

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

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本課的詞彙與口語要點

這節 B2 級別的口語課以影片“think like a business analyst in tech”為素材。影片中反覆出現的詞有：user, Sql, skill, insights, learn。這段影片共有 142 個句子、2611 個單詞可供跟讀。講話部分時長為 12:12。說話人語速較快，每分鐘約 214 個詞，會出現較多連讀和弱讀。 83% 的單詞屬於英語最常用的 3,000 詞，其餘的詞建議在練習前先學一下。全部 142 個句子中有 32 個是問句，適合練習疑問語調和簡短回答。

影片中的重點詞彙

影片中最難的 15 個單詞，附發音和釋義：

單詞	發音	釋義
insight 名詞	/ˈɪnsaɪ̯t/	見地 /見地
internship 名詞	/ˈɪntɜːnʃɪp/	實習 /實習
recommendation 名詞	/ˌɹɛkəmɛnˈdeɪʃən/	建議 /建議, 勸告 /勸告
analyze 動詞	/ˈæn.əˌlaɪz/	分析
actionable 形容詞	/ˈæk.ʃə.nə.bəl/	可行動的 /可行動的
churn 動詞	/t͡ʃɝn/	攪 /攪
cohort 名詞	/ˈkoʊ̯ˌhɔɹt/	群組 /群組, 同期群
visualize 動詞	/ˈvɪʒuəˌlaɪz/	遙想 /遙想
ambiguous 形容詞	/æmˈbɪɡ.ju.əs/	模稜兩可 /模稜兩可, 歧義 /歧義
lens 名詞	/lɛnz/	透鏡 /透鏡, 鏡片 /鏡片
pivot 名詞	/ˈpɪv.ɪt/	樞 /樞, 樞紐 /樞紐
analytics 名詞	/ˌæn.əˈlɪt.ɪks/	分析學 /分析學
competitor 名詞	/kəmˈpɛt.ɪ.tɚ/	競爭者 /競爭者, 比賽者 /比賽者
metric 形容詞	/ˈmɛt.ɹɪk/	公制
mentor 名詞	/ˈmɛn.tɔː/	指導者 /指導者, 導師 /導師

影片中出現的短語動詞

單詞	發音	釋義
break down 動詞		壞了 /壞了, 出故障
come up with 動詞		想出, 提出
check out 動詞		退房
turn out 動詞		原來 /原來
wake up 動詞	/ˌweɪk ˈʌp/	醒, 醒來 /醒來

值得反覆跟讀的句子

影片中簡短而完整的句子，可以直接用在日常對話裡：

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?

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 anything it would just
“used to” used to + 動詞 — 過去的習慣或狀態，現在已不再如此	used to do
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	would be asked · were actually asked · be asked
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	I've worn · have finished · has launched

需要注意的發音

說話人用了 30 次縮略和弱讀形式，例如 you're, I'll, I'm。請按聽到的簡短形式來說。

“sh” 和 “zh” 音: internship /ˈɪntɜːnʃɪp/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, actionable /ˈæk.ʃə.nə.bəl/, visualize /ˈvɪʒuəˌlaɪz/, prescription /pɹəˈskɹɪp.ʃən/
長單詞——注意重音位置: recommendation /ˌɹɛkəmɛnˈdeɪʃən/, actionable /ˈæk.ʃə.nə.bəl/, ambiguous /æmˈbɪɡ.ju.əs/, analytics /ˌæn.əˈlɪt.ɪks/, competitor /kəmˈpɛt.ɪ.tɚ/

中文母語者容易讀錯的音：

/v/ — 不要讀成 /w/，上齒輕觸下唇: visualize /ˈvɪʒuəˌlaɪz/, pivot /ˈpɪv.ɪt/, evaluate /ɪˈvaljʊeɪt/, dive /daɪv/, qualitative /ˈkwɑ.ləˌteɪ.tɪv/
詞尾子音 — 要讀清楚，後面不要加母音: insight /ˈɪnsaɪ̯t/, internship /ˈɪntɜːnʃɪp/, analyst /ˈænəlɪst/, analyze /ˈæn.əˌlaɪz/, cohort /ˈkoʊ̯ˌhɔɹt/
/r/ 和 /l/ 的區別: wrangle /ˈɹæŋɡəl/, gradual /ˈɡɹæd͡ʒuəl/

如何用這段影片練習

先完整聽一遍影片，不要開口，記下不認識的單詞。
先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
錄下自己的聲音並與原聲對比，特別注意 insight, internship, recommendation 這類單詞。

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: think like a business analyst in tech (3 core skills + solving case study) - 通過影片學習英語口語

選擇練習模式

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