シャドーイング練習: think like a business analyst in tech (3 core skills + solving case study) - 動画で英語スピーキングを学ぶ
レッスンを作成中...
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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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このレッスンの語彙とスピーキングのポイント
このB2レベルのスピーキングレッスンは、動画「think like a business analyst in tech」を教材にしています。 繰り返し出てくる語は次のとおりです：user, Sql, skill, insights, learn。 この動画には、シャドーイング用の文が142文、単語が2611語あります。 音声の長さは12:12です。 話す速さは速く、1分あたり約214語です。音のつながりや弱く発音される音が多くなります。 単語の83%は英語の頻出3,000語に含まれます。残りは練習の前に確認しておきましょう。 全142文のうち32文が疑問文なので、疑問文のイントネーションや短い受け答えの練習に向いています。
この動画の重要語彙
動画の中で特に難しい単語15語を、発音と意味つきで紹介します。
| 単語 | 発音 | 意味 |
|---|---|---|
| internship 名詞 | /ˈɪntɜːnʃɪp/ | インターンシップ, 実習 |
| recommendation 名詞 | /ˌɹɛkəmɛnˈdeɪʃən/ | 勧告, 推薦 |
| analyze 動詞 | /ˈæn.əˌlaɪz/ | 分析する |
| visualize 動詞 | /ˈvɪʒuəˌlaɪz/ | 思い描く, 視覚化する |
| ambiguous 形容詞 | /æmˈbɪɡ.ju.əs/ | 曖昧な |
| lens 名詞 | /lɛnz/ | レンズ |
| pivot 名詞 | /ˈpɪv.ɪt/ | ピボット, 枢 |
| recur 動詞 | /ɹɪˈkɜː/ | ぶり返す, 再発する |
| analytics 名詞 | /ˌæn.əˈlɪt.ɪks/ | 分析論, 分析学 |
| competitor 名詞 | /kəmˈpɛt.ɪ.tɚ/ | 競争者 |
| mock 動詞 | /mɑk/ | 真似る |
| mentor 名詞 | /ˈmɛn.tɔː/ | メンター |
| geography 名詞 | /d͡ʒiˈɑɡɹəfi/ | 地理学 |
| prescription 名詞 | /pɹəˈskɹɪp.ʃən/ | 処方, 処方箋 |
| pause 名詞 | /pɔːz/ | 休止, 中断 |
動画に出てくる句動詞
| 単語 | 発音 | 意味 |
|---|---|---|
| check out 動詞 | 試験する, 検査する | |
| wake up 動詞 | /ˌweɪk ˈʌp/ | 目覚める, 目を覚ます |
繰り返し練習したい文
動画に出てくる短く完結した文です。日常会話でそのまま使えます。
- 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?
この動画の文法
話し手がよく使っている文型を、動画の実際の表現とともに紹介します。
| 文型 | 動画での表現 |
|---|---|
| 条件文 if + 節, will/would + 動詞 — 条件とその結果 | if anything it would just |
| 「used to」 used to + 動詞 — 今はもう違う過去の習慣や状態 | used to do |
| 受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる | would be asked · were actually asked · be asked |
| 現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している | I've worn · have finished · has launched |
注意したい発音
話し手はyou're, I'll, I'mなど、短縮形や弱形を30回使っています。聞こえたとおりの短い形で発音しましょう。
- 「sh」と「zh」の音: internship /ˈɪntɜːnʃɪp/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, actionable /ˈæk.ʃə.nə.bəl/, visualize /ˈvɪʒuəˌlaɪz/, prescription /pɹəˈskɹɪp.ʃən/
- 長い単語（アクセントの位置に注意）: recommendation /ˌɹɛkəmɛnˈdeɪʃən/, actionable /ˈæk.ʃə.nə.bəl/, ambiguous /æmˈbɪɡ.ju.əs/, analytics /ˌæn.əˈlɪt.ɪks/, competitor /kəmˈpɛt.ɪ.tɚ/
日本語話者が苦手な音：
- /r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: wrangle /ˈɹæŋɡəl/, gradual /ˈɡɹæd͡ʒuəl/
- /v/ — /b/ にならないように、上の歯を下唇に当てる: visualize /ˈvɪʒuəˌlaɪz/, pivot /ˈpɪv.ɪt/, evaluate /ɪˈvaljʊeɪt/, dive /daɪv/, qualitative /ˈkwɑ.ləˌteɪ.tɪv/
- /f/ — 「フ」ではなく、上の歯と下唇で出す: geography /d͡ʒiˈɑɡɹəfi/, demographic /ˌdɛm.əˈɡɹæf.ɪk/, refund /ɹɪˈfʌnd/, graph /ɡɹæf/
この動画での練習方法
- まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
- まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
- 自分の声を録音して元の音声と比べます。internship, recommendation, analyzeなどの単語に特に注意しましょう。
シャドーイングとは？英語上達に効果的な理由
シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。