シャドーイング練習: How to Think Like a Data Analyst | Step-by-Step Guide - 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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We live in a world that runs on data.
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It's how Amazon and Netflix know which movies and products to recommend, how Starbucks manages a global supply chain,
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and how Uber connects drivers with passengers in real time.
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But the thing is, data skills aren't just for tech companies or professional analysts anymore.
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Everyone works with data to some degree, and everyone can benefit from data literacy skills.
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In this video, we're covering an important topic that will help you take your data literacy to the next level.
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When it comes to transforming raw data into insight and ultimately action, you need to think like an analyst.
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That means identifying the problem, setting clear expectations, collecting and analyzing the exact information you need,
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and leveraging insights and findings to influence decisions and real-world outcomes.
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So here's a great analytical thinking framework that we often teach here at Maven, which will help you take a more thoughtful and strategic approach to your analysis.
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We cover this in much more depth in our Thinking Like an Analyst course, but let's review it at a high level here as well, because it's extremely relevant to anyone trying to make smart,
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data-driven decisions, analyst or otherwise.
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Now, keep in mind that there's still a time and place for more unguided, open-ended exploratory analysis, but this approach works very,
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very well, especially for explanatory analysis, where your primary goal is to deliver actionable insights and recommendations.
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So it all starts by clearly identifying the problem you're trying to solve.
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And the key here is that before you start thinking like an analyst, you need to think like a business owner.
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And this involves asking yourself some key questions.
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What specific problem are you trying to solve?
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And which business outcomes are you trying to impact?
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Who are the key stakeholders and how exactly will this help them?
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And does your approach align with the bigger picture priorities and business strategy?
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Answering these questions first, which many people fail to do, will help ensure that you align on the project scope and desired outcome from day one.
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The second step in the framework is all about defining success.
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And this is where things like measurement planning come into play.
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It's about asking yourself what exactly does a successful outcome look like for the business.
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Is it driving more revenue, increasing employee retention rates, driving better marketing ROI?
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And then once you define success, which specific metrics or KPIs will help you quantify it
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and what data will you need to capture to track those key metrics?
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That will set you up with a crystal clear roadmap for measuring the success of your efforts.
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We see so many people, even professional data analysts, skip these first two steps
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and jump straight into steps three and four because they're
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so eager to roll up their sleeves and start playing with the data
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which often leads to disaster wasted time for both you
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and stakeholders lack of focus and clarity false hopes and expectations
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and the list goes on so if your goal is to work smart not hard
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and make a real tangible impact make sure you don't skip these steps.
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By the time you've reached step three, you should have a clear picture of what success metrics you're trying to impact and the data you'll need to quantify them.
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Now is the time to start collecting and preparing your data.
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At this stage, the types of questions you're asking are things like where is the data stored
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and how can you access it?
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Are there data quality issues that might skew the analysis?
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And is the data in the proper format?
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Or will it need to be transformed, modeled, or restructured to support your analysis.
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This step can be one of the more challenging and time-consuming stages in the workflow, and typically involves a mix of QA,
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profiling, data cleaning, and enrichment like adding new fields or data sources
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but if you do this well it will create a rock solid foundation for your analysis
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and ensure that you're working with clean high quality data because as they say garbage in garbage out.
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Step four is about finally getting your hands dirty and starting to explore and analyze the data.
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For most people this is the fun part because it involves slicing
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and dicing the data uncovering interesting patterns and trends
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and ideally discovering some meaningful actionable insights
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that could directly impact the metrics you're trying to move
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so the types of questions you'll likely be asking at this point
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which types of views of the data can help support your analysis
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which types of patterns and trends are beginning to emerge as you explore the data
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and are you finding any nuggets any actionable insights that again could help drive those success outcomes.
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Once you've wrapped the analysis phase, it's time to communicate your findings.
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This is another mission-critical step, especially for explanatory analysis, because success hinges on your ability to clearly communicate what you've found,
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why it's important, and how it can impact the business.
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One of the most common missteps I see with young analysts is that they over-index on the technical skills.
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They spend too much time getting fired up about the analysis itself, then completely drop the ball when it comes time to share their findings,
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especially when they're communicating with non-technical or senior level audiences.
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At this stage, it's really important to remember that people respond to stories, not data points.
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So you should be asking yourself how you can craft a narrative to clearly summarize the key takeaways from your analysis.
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This is a great time to recap your problem statement and measurement plan, summarize your approach, highlight your most compelling insights,
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and tie it all together into a clear and concise presentation.
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You'll also be thinking about the most effective ways to communicate your findings through data visualization, which is something we'll talk more about in the next section,
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and confirming that your insights are clear, compelling, and supported by the data.
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The final step in our framework is all about advocating for action.
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When you stop short of this step, you're essentially taking the analysis to the point where you're one step away from the finish line
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and then leaving it up to your audience to decide what to do from there.
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Don't make your stakeholders connect the dots on their own.
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Think about what actionable, data-driven recommendations you can propose
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and make sure they directly tie to the success outcomes that you care most about.
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And the cherry on top, which will make stakeholders absolutely love you, would be to propose how you might measure or quantify the business impact of the changes you're proposing.
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Not only does that prove that you care about the impact to the business, but it shows that you're willing to take accountability as well.
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Plus, if all goes to plan, now you have an amazing success story to add to the resume.
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So that's a quick summary of our data analysis framework.
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And even if you don't apply every single step, hopefully it helps you at least get into a better analytical mindset that you can apply to your own work.
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If you enjoyed this content and want to see more, we've got a brand new data literacy foundations course, and it's entirely free.
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You can check it out at mavenanalytics.io.
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So whether you're an individual looking to build confidence, a leader seeking to empower and upskill your team, or a data professional just trying to stay ahead of the curve,
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this is the course for you.
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We've got a lot to cover, so let's dive in.
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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
この動画には、シャドーイング用の文が84文、単語が1295語あります。 音声の長さは7:15です。 話す速さは1分あたり約178語で、日常会話に近い自然なペースです。 単語の84%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。
この動画の重要語彙
動画に出てくる覚えておきたい単語15語を、発音と意味つきで紹介します。
| 単語 | 発音 | 意味 |
|---|---|---|
| framework 名詞 | /ˈfɹeɪm.wɜːk/ | 骨組み |
| communicate 動詞 | /kəˈmjuːnɪkeɪt/ | 伝える |
| skill 名詞 | /skɪl/ | 腕, 技 |
| propose 動詞 | /pɹəˈpəʊz/ | 申し込む |
| solve 動詞 | /sɒlv/ | 解決する |
| connect 動詞 | /kəˈnɛkt/ | 繋げる |
| collect 動詞 | /ˈkɑlɪkt/ | 集める, 収集する |
| explore 動詞 | /ɪkˈsploɹ/ | 探検する |
| skip 動詞 | /skɪp/ | はね回る |
| garbage 名詞 | /ˈɡɑɹ.bɪd͡ʒ/ | ごみ, 廃物 |
| recommendation 名詞 | /ˌɹɛkəmɛnˈdeɪʃən/ | 勧告, 推薦 |
| measurement 名詞 | /ˈmɛʒ.ə.mənt/ | 測定 |
| transform 動詞 | /tɹænsˈfɔɹm/ | 変形する, 変換する |
| expectation 名詞 | /ˌɛk.spɛkˈteɪ.ʃən/ | 期待, 予想 |
| analyze 動詞 | /ˈæn.əˌlaɪz/ | 分析する |
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 |
|---|---|
| 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 | you've reached · you've wrapped · you've found |
| 関係詞節 who / which + 節 — 人や物について情報を加える | Maven, which will · visualization, which is · top, which will |
注意したい発音
話し手はyou're, don't, you'llなど、短縮形や弱形を26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: recommendation /ˌɹɛkəmɛnˈdeɪʃən/, measurement /ˈmɛʒ.ə.mənt/, expectation /ˌɛk.spɛkˈteɪ.ʃən/, capture /ˈkæp.(t)ʃɚ/, essentially /ɪˈsɛnʃəli/
- 長い単語（アクセントの位置に注意）: communicate /kəˈmjuːnɪkeɪt/, literacy /ˈlɪtəɹəsi/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, expectation /ˌɛk.spɛkˈteɪ.ʃən/, analytical /ˌæn.əˈlɪt.ɪ.kəl/
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。framework, communicate, skill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。