跟讀練習: How to Think Like a Data Analyst | Step-by-Step Guide - 通過影片學習英語口語

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We live in a world that runs on data.

It's how Amazon and Netflix know which movies and products to recommend, how Starbucks manages a global supply chain,

and how Uber connects drivers with passengers in real time.

But the thing is, data skills aren't just for tech companies or professional analysts anymore.

Everyone works with data to some degree, and everyone can benefit from data literacy skills.

In this video, we're covering an important topic that will help you take your data literacy to the next level.

When it comes to transforming raw data into insight and ultimately action, you need to think like an analyst.

That means identifying the problem, setting clear expectations, collecting and analyzing the exact information you need,

and leveraging insights and findings to influence decisions and real-world outcomes.

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.

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,

data-driven decisions, analyst or otherwise.

Now, keep in mind that there's still a time and place for more unguided, open-ended exploratory analysis, but this approach works very,

very well, especially for explanatory analysis, where your primary goal is to deliver actionable insights and recommendations.

So it all starts by clearly identifying the problem you're trying to solve.

And the key here is that before you start thinking like an analyst, you need to think like a business owner.

And this involves asking yourself some key questions.

What specific problem are you trying to solve?

And which business outcomes are you trying to impact?

Who are the key stakeholders and how exactly will this help them?

And does your approach align with the bigger picture priorities and business strategy?

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.

The second step in the framework is all about defining success.

And this is where things like measurement planning come into play.

It's about asking yourself what exactly does a successful outcome look like for the business.

Is it driving more revenue, increasing employee retention rates, driving better marketing ROI?

And then once you define success, which specific metrics or KPIs will help you quantify it

and what data will you need to capture to track those key metrics?

That will set you up with a crystal clear roadmap for measuring the success of your efforts.

We see so many people, even professional data analysts, skip these first two steps

and jump straight into steps three and four because they're

so eager to roll up their sleeves and start playing with the data

which often leads to disaster wasted time for both you

and stakeholders lack of focus and clarity false hopes and expectations

and the list goes on so if your goal is to work smart not hard

and make a real tangible impact make sure you don't skip these steps.

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.

Now is the time to start collecting and preparing your data.

At this stage, the types of questions you're asking are things like where is the data stored

and how can you access it?

Are there data quality issues that might skew the analysis?

And is the data in the proper format?

Or will it need to be transformed, modeled, or restructured to support your analysis.

This step can be one of the more challenging and time-consuming stages in the workflow, and typically involves a mix of QA,

profiling, data cleaning, and enrichment like adding new fields or data sources

but if you do this well it will create a rock solid foundation for your analysis

and ensure that you're working with clean high quality data because as they say garbage in garbage out.

Step four is about finally getting your hands dirty and starting to explore and analyze the data.

For most people this is the fun part because it involves slicing

and dicing the data uncovering interesting patterns and trends

and ideally discovering some meaningful actionable insights

that could directly impact the metrics you're trying to move

so the types of questions you'll likely be asking at this point

which types of views of the data can help support your analysis

which types of patterns and trends are beginning to emerge as you explore the data

and are you finding any nuggets any actionable insights that again could help drive those success outcomes.

Once you've wrapped the analysis phase, it's time to communicate your findings.

This is another mission-critical step, especially for explanatory analysis, because success hinges on your ability to clearly communicate what you've found,

why it's important, and how it can impact the business.

One of the most common missteps I see with young analysts is that they over-index on the technical skills.

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,

especially when they're communicating with non-technical or senior level audiences.

At this stage, it's really important to remember that people respond to stories, not data points.

So you should be asking yourself how you can craft a narrative to clearly summarize the key takeaways from your analysis.

This is a great time to recap your problem statement and measurement plan, summarize your approach, highlight your most compelling insights,

and tie it all together into a clear and concise presentation.

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,

and confirming that your insights are clear, compelling, and supported by the data.

The final step in our framework is all about advocating for action.

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

and then leaving it up to your audience to decide what to do from there.

Don't make your stakeholders connect the dots on their own.

Think about what actionable, data-driven recommendations you can propose

and make sure they directly tie to the success outcomes that you care most about.

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.

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.

Plus, if all goes to plan, now you have an amazing success story to add to the resume.

So that's a quick summary of our data analysis framework.

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.

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.

You can check it out at mavenanalytics.io.

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,

this is the course for you.

We've got a lot to cover, so let's dive in.

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流利口語所需的一切

AI 發音評分逐句打分

音標練習掌握每個音

詞彙學習建立你的詞庫

詞彙遊戲邊玩邊學

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Database vs Data Warehouse vs Data Lake | Explaining the Differences

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本課的詞彙與口語要點

這段影片共有 84 個句子、1295 個單詞可供跟讀。講話部分時長為 7:15。說話人語速自然，每分鐘約 178 個詞，接近日常對話。 84% 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。

影片中的重點詞彙

影片中 15 個值得學習的單詞，附發音和釋義：

單詞	發音	釋義
insight 名詞	/ˈɪnsaɪ̯t/	見地 /見地
framework 名詞	/ˈfɹeɪm.wɜːk/	骨架, 框架
metric 形容詞	/ˈmɛt.ɹɪk/	公制
skill 名詞	/skɪl/	技巧, 技能
propose 動詞	/pɹəˈpəʊz/	提議 /提議
literacy 名詞	/ˈlɪtəɹəsi/	認字 /認字, 識字 /識字
solve 動詞	/sɒlv/	解決 /解決
connect 動詞	/kəˈnɛkt/	連線 /連線
collect 動詞	/ˈkɑlɪkt/	庋藏, 收藏
define 動詞	/dɪˈfaɪn/	界定
explore 動詞	/ɪkˈsploɹ/	探索, 探險 /探險
skip 動詞	/skɪp/	跳
garbage 名詞	/ˈɡɑɹ.bɪd͡ʒ/	垃圾, 廢物 /廢物
recommendation 名詞	/ˌɹɛkəmɛnˈdeɪʃən/	建議 /建議, 勸告 /勸告
measurement 名詞	/ˈmɛʒ.ə.mənt/	尺寸

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
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	you've reached · you've wrapped · you've found
定語從句 who / which + 從句 — 補充說明人或事物	Maven, which will · visualization, which is · top, which will

需要注意的發音

說話人用了 26 次縮略和弱讀形式，例如 you're, don't, you'll。請按聽到的簡短形式來說。

“sh” 和 “zh” 音: recommendation /ˌɹɛkəmɛnˈdeɪʃən/, measurement /ˈmɛʒ.ə.mənt/, expectation /ˌɛk.spɛkˈteɪ.ʃən/, capture /ˈkæp.(t)ʃɚ/, essentially /ɪˈsɛnʃəli/
長單詞——注意重音位置: communicate /kəˈmjuːnɪkeɪt/, literacy /ˈlɪtəɹəsi/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, expectation /ˌɛk.spɛkˈteɪ.ʃən/, analytical /ˌæn.əˈlɪt.ɪ.kəl/

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, framework, metric 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: How to Think Like a Data Analyst | Step-by-Step Guide - 通過影片學習英語口語

選擇練習模式

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