Pratica di Shadowing: How to Think Like a Data Analyst | Step-by-Step Guide - Impara a parlare inglese con i video
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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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Vocabolario e note di pronuncia per questa lezione
Questo video contiene 84 frasi e 1295 parole da ripetere con lo shadowing. Il parlato dura 7:15. Chi parla ha un ritmo naturale di circa 178 parole al minuto, vicino a una conversazione quotidiana. Il 84% delle parole rientra nelle 3.000 più comuni dell’inglese; conviene studiare le altre prima di iniziare.
Vocaboli chiave di questo video
15 parole del video che vale la pena imparare, con pronuncia e significato:
| Parola | Pronuncia | Significato |
|---|---|---|
| insight sostantivo | /ˈɪnsaɪ̯t/ | introspezione, approfondimento |
| outcome sostantivo | /ˈaʊtkʌm/ | risultato |
| framework sostantivo | /ˈfɹeɪm.wɜːk/ | struttura portante |
| communicate verbo | /kəˈmjuːnɪkeɪt/ | comunicare, informare |
| metric aggettivo | /ˈmɛt.ɹɪk/ | metrico |
| skill sostantivo | /skɪl/ | abilità, capacità |
| involve verbo | /ɪnˈvɑlv/ | coinvolgere |
| propose verbo | /pɹəˈpəʊz/ | proporre |
| literacy sostantivo | /ˈlɪtəɹəsi/ | alfabetismo, alfabetizzazione |
| solve verbo | /sɒlv/ | risolvere |
| connect verbo | /kəˈnɛkt/ | connettere, connettersi |
| collect verbo | /ˈkɑlɪkt/ | raccogliere, bottinare |
| define verbo | /dɪˈfaɪn/ | definire, determinare |
| explore verbo | /ɪkˈsploɹ/ | esplorare, investigare |
| skip verbo | /skɪp/ | balzare, saltellare |
I phrasal verb che sentirai
| Parola | Significato |
|---|---|
| roll up verbo | arrotolare |
La grammatica di questo video
Le strutture che chi parla usa di più, con le parole esatte del video:
| Struttura | Nel video |
|---|---|
| Present perfect have/has + participio passato — un’azione passata che conta ancora adesso | you've reached · you've wrapped · you've found |
| Frasi relative who / which + frase — un’informazione in più su una persona o una cosa | Maven, which will · visualization, which is · top, which will |
Pronuncia a cui fare attenzione
Chi parla usa 26 contrazioni e forme ridotte, come you're, don't, you'll. Pronunciale nella forma breve, così come le senti.
- I suoni “sh” e “zh”: recommendation /ˌɹɛkəmɛnˈdeɪʃən/, measurement /ˈmɛʒ.ə.mənt/, expectation /ˌɛk.spɛkˈteɪ.ʃən/, capture /ˈkæp.(t)ʃɚ/, essentially /ɪˈsɛnʃəli/
- Parole lunghe — attenzione all’accento: communicate /kəˈmjuːnɪkeɪt/, literacy /ˈlɪtəɹəsi/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, expectation /ˌɛk.spɛkˈteɪ.ʃən/, analytical /ˌæn.əˈlɪt.ɪ.kəl/
Come esercitarsi con questo video
- Ascolta tutto il video una volta senza parlare e annota le parole che non conosci.
- Inizia a velocità 0,75×, fai shadowing frase per frase e torna alla velocità normale quando diventa facile.
- Registrati e confronta con l’originale, facendo attenzione a parole come insight, outcome, framework.
Cos'è la tecnica dello Shadowing?
Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.