쉐도잉 연습: think like a business analyst in tech (3 core skills + solving case study) - 영상으로 영어 말하기 배우기
레슨 만드는 중...
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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.
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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.
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If you're interviewing, these are also the key skills that companies are evaluating you on for a BA interview.
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Before we get started, Thank you GlassesUSA.com for sponsoring today's video.
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I've worn glasses since elementary school and I break them all the time.
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Bbecause I have this really bad habit of putting it in near my pillow, causing the glasses to get bent when I wake up so I'm very priced conscious when it comes to buying glasses.
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By cutting out the middleman, glassesUSA.com offers over 9,000 prescription glasses and sunglasses including in house brands like Muse and Amelia e, and designer brands like Rayband, Gucci and many more, at up to 70% of retail prices.
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You can shop for all your eyewear needs online at affordable prices started at only 30 dollars with your basic prescription lenses included without leaving your home!
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GlassUSA.com also offers a collection of advanced blue light blocking lenses as a business analyst, my eyes are basically glued to SQL and Google Sheets all day, so blue light glasses are a must for reducing my eye strain and headache.
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Their blue light lenses also include clear prevention and UV block they also offer a risk free shopping experience with free shipping and returns, 100% money back guarantee, full refund within 14 days of delivery, no questions asked, and a 365 day product warranty.
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If you can't decide what glasses look good for you, they also have a virtual try on tool on the website which really helps you find the right pair.
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I literally used it to find mine.
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You can upload a picture or take a photo where you can see what the glasses will look like on you.
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If you're shopping for your glasses, definitely check out GlassesUSA.com!
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And I'll leave the links of the glasses I'm wearing in the description box.
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So, let's get it started!
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Skill no. 1, your technical skills.
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Especially if you want to work in tech with an analyst title, SQL is the most important than Excel Google Sheets.
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SQL is a language that enables you to pull millions and billions of user information stored in databases to answer business questions.
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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?
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Or specifically, how many monthly active users do we have per region that have finished watching Squid Game?
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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.
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How can you learn SQL?
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So I'm going to talk about if you don't have any experience with SQL and if you have some experience.
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If you have your experience with SQL, I would recommend starting with datacamp's class on intro to SQL.
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This is free and you get to learn the basic SQL logics and command lines@.
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I also love how it's super interactive so everything feels very real when you're coding on it.
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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.
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On these websites, you will see all the SQL teg questions from companies like Facebook, Google that were actually asked during the interviews.
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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.
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I would definitely be sure to ace the easy question and get comfortable with medium questions.
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So aside from SQL, data wrangling skills in Excel and Google Sheets is another essential.
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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.
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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.
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And no, you don't have to know all these formulas out there!
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In my day to day, I mainly use pivot tables, sumifs, vlookups, index/match.
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and if anything it would just be more (sumifs vlookup pivot table index/match) advanced variations of them.
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I think what's more important here is knowing how to solve the problem.
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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.
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I Googled exactly that and I realized sumif can help me do that.
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So Google is going to be your best friend when it comes to data wrangling.
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How do you learn data wrangling skills?
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My biggest tip to learn it is to play around with the data yourself and practice.
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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.
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When I was doing my digital marketing internship at Roc Nation, my internship had almost nothing to do with data analysis.
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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.
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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.
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And try to see how people wrangle and structure their data.
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What I used to do was making a copy of the sheet and try to do it myself.
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And last tip is practice.
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The more you do it, the more you will see the pattern building your own systemic way of visualizing things.
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Skill no. 2, problem solving skills.
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This is about how you can break down ambiguous questions into smaller pieces in a structured way.
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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.
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A classic question is a root cause analysis which I get asked all the time at work too.
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So get ready for this question.
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Imagine you work at Netflix We're seeing monthly active users declining on the Netflix app.
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Can you root cause the issue?
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What recommendations do you have?
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To approach any ambiguous problem, there's a general structure to problem solving that I learned from my mentors.
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Break down the problem into smaller questions or pieces, then use data to answer your questions.
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After that, you can consolidate your learnings into insights, and based on your insights you provide your business recommendations.
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So the first question I'll think about is impact size.
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How long have we been seeing this MAU decline, and what is the size of this user decline?
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Also look at geography.
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Are we seeing this on a global level or in a specific market?
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And seasonality too.
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Did we see MAU decline last year around the same time too or did it only happen this year?
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Maybe it's a recurring pattern and related to seasonality?
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And these are some other questions I would look into I then dive into the data to find insights for each of these questions.
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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.
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Let's say after you analyze data, and you may be analyzing 10 to 15 metric.
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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.
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So I will say it like this.
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Top-down communication is when you start with your key message and then go into your supporting details.
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There's also a video about top-down communication like this one, and I highly recommend you to check it out.
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How do you learn problem solving skills?
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First tip is to watch mock case interviews.
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I'll link some good ones down in the description box.
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I also love seeing product managers do their mock interviews.
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Especially these two on the Exponent channel since it's more tech driven.
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In terms of what jobs or internship experiences that are more relevant, consulting is a big one.
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And the secret is that's where most tech companies recruit their BA's from.
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Internship wise, I highly recommend insights internship, analytics, or market research internships.
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Basically internships that have something along the lines of using data to drive actionable insights.
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Can be quantitative data or qualitative data doesn't matter.
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What you're trying to learn here is to turn data into insights and tell a story.
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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.
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See how they go from point A to point B to point C.
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Think about what they did well and what you would improve on if you were the one presenting.
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Skill no. 3, strategic thinking and business sense.
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What sets great analysts apart from average analysts?
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Is their ability to turn data into actionable insights to guide the business recommendation.
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Basically, how would this specific piece of data be actionable for the business?
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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.
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So, using our example again on...
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We're seeing monthly active users declining on the Netflix app.
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Can you recall the issue?
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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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이 레슨의 어휘와 말하기 포인트
이 B2 수준 말하기 레슨은 영상 “think like a business analyst in tech”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: user, Sql, skill, insights, learn. 이 영상에는 섀도잉할 문장 142개와 단어 2611개가 있습니다. 말하는 구간의 길이는 12:12입니다. 화자는 분당 약 214단어로 빠르게 말하므로 연음과 약하게 발음되는 소리가 많습니다. 단어의 83%가 영어에서 가장 많이 쓰이는 3,000단어에 속합니다. 나머지는 연습 전에 미리 확인해 두세요. 전체 142문장 중 32문장이 질문이어서 의문문 억양과 짧은 대답을 연습하기에 좋습니다.
이 영상의 핵심 어휘
영상에서 가장 어려운 단어 15개를 발음, 뜻과 함께 정리했습니다.
| 단어 | 발음 | 뜻 |
|---|---|---|
| recommendation 명사 | /ˌɹɛkəmɛnˈdeɪʃən/ | 권고, 추천 |
| analyze 동사 | /ˈæn.əˌlaɪz/ | 분석하다 |
| ambiguous 형용사 | /æmˈbɪɡ.ju.əs/ | 모호한 |
| lens 명사 | /lɛnz/ | 렌즈 |
| pivot 명사 | /ˈpɪv.ɪt/ | 피벗 |
| analytics 명사 | /ˌæn.əˈlɪt.ɪks/ | 해석학, 애널리틱스 |
| competitor 명사 | /kəmˈpɛt.ɪ.tɚ/ | 경쟁자 |
| mentor 명사 | /ˈmɛn.tɔː/ | 멘토 |
| geography 명사 | /d͡ʒiˈɑɡɹəfi/ | 지리학 |
| prescription 명사 | /pɹəˈskɹɪp.ʃən/ | 처방, 처방전 |
| pause 명사 | /pɔːz/ | 멈춤, 휴지 |
| evaluate 동사 | /ɪˈvaljʊeɪt/ | 평가하다 |
| sponsor 명사 | /ˈspɒn.səː/ | 스폰서 |
| dive 동사 | /daɪv/ | 잠수하다 |
| criterion 명사 | /kɹaɪˈtɪɹ.i.ən/ | 표준 |
영상에 나오는 구동사
| 단어 | 발음 | 뜻 |
|---|---|---|
| wake up 동사 | /ˌweɪk ˈʌp/ | 눈뜨다, 일어나다 |
따라 말해 볼 만한 문장
영상에 나오는 짧고 완결된 문장으로, 일상 대화에서 그대로 쓸 수 있습니다:
- 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?
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 anything it would just |
| “used to” used to + 동사 — 지금은 아닌 과거의 습관이나 상태 | used to do |
| 수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점 | would be asked · were actually asked · be asked |
| 현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때 | I've worn · have finished · has launched |
주의할 발음
화자는 you're, I'll, I'm 같은 축약형과 약화된 형태를 30번 사용합니다. 들리는 대로 짧게 발음하세요.
- “sh”와 “zh” 소리: internship /ˈɪntɜːnʃɪp/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, actionable /ˈæk.ʃə.nə.bəl/, visualize /ˈvɪʒuəˌlaɪz/, prescription /pɹəˈskɹɪp.ʃən/
- 긴 단어 — 강세 위치에 주의: recommendation /ˌɹɛkəmɛnˈdeɪʃən/, actionable /ˈæk.ʃə.nə.bəl/, ambiguous /æmˈbɪɡ.ju.əs/, analytics /ˌæn.əˈlɪt.ɪks/, competitor /kəmˈpɛt.ɪ.tɚ/
한국어 화자가 어려워하는 소리:
- /f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: geography /d͡ʒiˈɑɡɹəfi/, demographic /ˌdɛm.əˈɡɹæf.ɪk/, refund /ɹɪˈfʌnd/, graph /ɡɹæf/
- /v/ — ㅂ(/b/)과 구별하기: visualize /ˈvɪʒuəˌlaɪz/, pivot /ˈpɪv.ɪt/, evaluate /ɪˈvaljʊeɪt/, dive /daɪv/, qualitative /ˈkwɑ.ləˌteɪ.tɪv/
- /z/ — ㅈ이 아니라 성대를 울리는 /s/: analyze /ˈæn.əˌlaɪz/, visualize /ˈvɪʒuəˌlaɪz/, lens /lɛnz/, pause /pɔːz/, organize /ˈɔɹɡənaɪz/
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, recommendation, analyze, ambiguous 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.