Shadowing Practice: How to Think Like a Data Analyst | Step-by-Step Guide - Learn English Speaking with Video
בחר מצב תרגול
כל מצב פותח חדר תרגול במסך מלא.
84 משפטים
אם המשפטים קצרים או ארוכים מדי, לחץ על ערוך כדי להתאים אותם.
1
We live in a world that runs on data.
2
It's how Amazon and Netflix know which movies and products to recommend, how Starbucks manages a global supply chain,
3
and how Uber connects drivers with passengers in real time.
4
But the thing is, data skills aren't just for tech companies or professional analysts anymore.
5
Everyone works with data to some degree, and everyone can benefit from data literacy skills.
6
In this video, we're covering an important topic that will help you take your data literacy to the next level.
7
When it comes to transforming raw data into insight and ultimately action, you need to think like an analyst.
8
That means identifying the problem, setting clear expectations, collecting and analyzing the exact information you need,
9
and leveraging insights and findings to influence decisions and real-world outcomes.
10
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.
11
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,
12
data-driven decisions, analyst or otherwise.
13
Now, keep in mind that there's still a time and place for more unguided, open-ended exploratory analysis, but this approach works very,
14
very well, especially for explanatory analysis, where your primary goal is to deliver actionable insights and recommendations.
15
So it all starts by clearly identifying the problem you're trying to solve.
16
And the key here is that before you start thinking like an analyst, you need to think like a business owner.
17
And this involves asking yourself some key questions.
18
What specific problem are you trying to solve?
19
And which business outcomes are you trying to impact?
20
Who are the key stakeholders and how exactly will this help them?
21
And does your approach align with the bigger picture priorities and business strategy?
22
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.
23
The second step in the framework is all about defining success.
24
And this is where things like measurement planning come into play.
25
It's about asking yourself what exactly does a successful outcome look like for the business.
26
Is it driving more revenue, increasing employee retention rates, driving better marketing ROI?
27
And then once you define success, which specific metrics or KPIs will help you quantify it
28
and what data will you need to capture to track those key metrics?
29
That will set you up with a crystal clear roadmap for measuring the success of your efforts.
30
We see so many people, even professional data analysts, skip these first two steps
31
and jump straight into steps three and four because they're
32
so eager to roll up their sleeves and start playing with the data
33
which often leads to disaster wasted time for both you
34
and stakeholders lack of focus and clarity false hopes and expectations
35
and the list goes on so if your goal is to work smart not hard
36
and make a real tangible impact make sure you don't skip these steps.
37
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.
38
Now is the time to start collecting and preparing your data.
39
At this stage, the types of questions you're asking are things like where is the data stored
40
and how can you access it?
41
Are there data quality issues that might skew the analysis?
42
And is the data in the proper format?
43
Or will it need to be transformed, modeled, or restructured to support your analysis.
44
This step can be one of the more challenging and time-consuming stages in the workflow, and typically involves a mix of QA,
45
profiling, data cleaning, and enrichment like adding new fields or data sources
46
but if you do this well it will create a rock solid foundation for your analysis
47
and ensure that you're working with clean high quality data because as they say garbage in garbage out.
48
Step four is about finally getting your hands dirty and starting to explore and analyze the data.
49
For most people this is the fun part because it involves slicing
50
and dicing the data uncovering interesting patterns and trends
51
and ideally discovering some meaningful actionable insights
52
that could directly impact the metrics you're trying to move
53
so the types of questions you'll likely be asking at this point
54
which types of views of the data can help support your analysis
55
which types of patterns and trends are beginning to emerge as you explore the data
56
and are you finding any nuggets any actionable insights that again could help drive those success outcomes.
57
Once you've wrapped the analysis phase, it's time to communicate your findings.
58
This is another mission-critical step, especially for explanatory analysis, because success hinges on your ability to clearly communicate what you've found,
59
why it's important, and how it can impact the business.
60
One of the most common missteps I see with young analysts is that they over-index on the technical skills.
61
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,
62
especially when they're communicating with non-technical or senior level audiences.
63
At this stage, it's really important to remember that people respond to stories, not data points.
64
So you should be asking yourself how you can craft a narrative to clearly summarize the key takeaways from your analysis.
65
This is a great time to recap your problem statement and measurement plan, summarize your approach, highlight your most compelling insights,
66
and tie it all together into a clear and concise presentation.
67
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,
68
and confirming that your insights are clear, compelling, and supported by the data.
69
The final step in our framework is all about advocating for action.
70
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
71
and then leaving it up to your audience to decide what to do from there.
72
Don't make your stakeholders connect the dots on their own.
73
Think about what actionable, data-driven recommendations you can propose
74
and make sure they directly tie to the success outcomes that you care most about.
75
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.
76
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.
77
Plus, if all goes to plan, now you have an amazing success story to add to the resume.
78
So that's a quick summary of our data analysis framework.
79
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.
80
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.
81
You can check it out at mavenanalytics.io.
82
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,
83
this is the course for you.
84
We've got a lot to cover, so let's dive in.
הורד אפליקציה
כל מה שאתה צריך כדי לדבר בשטף
הגיית AIציון לכל משפט
תרגול IPAשלוט בכל צליל
אוצר מיליםבנה את מאגר המילים שלך
משחק אוצר מיליםלמד תוך כדי משחק
אותו ערוץ
FOR YOU
המלץ על סרטון
C1239 משפטים
Learn how to SOLVE a data analytics case study problem
Business EnglishInterview EnglishUniversity Students
C11
154 משפטים
Video
Business EnglishUniversity StudentsWorking Adults
C11
120 משפטים
Learn Data Modeling in 8 minutes: Dimensional Data Modeling, Data Vault, and One Big Table
Business EnglishUniversity StudentsWorking Adults
C14
128 משפטים
Financial Analysis Fundamentals: How to Analyze Financial Statements Like a Pro Part 1 | CFI Course
Business EnglishUniversity StudentsWorking Adults
C11
141 משפטים
How AI is Changing Data Scientist's Workflow
Business EnglishUniversity StudentsWorking Adults
C12
129 משפטים
How to Become a Great Software Developer — Best Advice from Top-Notch Engineers
Business EnglishInterview EnglishUniversity Students
C18
41 משפטים
Design Thinking 101
Business EnglishUniversity StudentsWorking Adults
C1480 משפטים
Everything Valuable from an MBA in 25 minutes
Business EnglishUniversity StudentsWorking Adults
B2189
8 משפטים
Cách Business Analyst tư duy để đạt mục tiêu - Lillian Chiu #businessanalyst #ielts
IELTS PrepBusiness EnglishUniversity Students
C114
20 משפטים
Characteristics of a Data and Analytics Leader
Business EnglishUniversity StudentsWorking Adults
C12
224 משפטים
What is Statistics? A Beginner's Guide to Statistics (Data Analytics)!
Business EnglishUniversity StudentsWorking Adults
C13
80 משפטים
How to think like a lawyer | Zero-L from Harvard Law School
Business EnglishUniversity StudentsWorking Adults
אוצר מילים והערות הגייה לשיעור הזה
שיעור הדיבור הזה ברמה C1 מבוסס על הסרטון “How to Think Like a Data Analyst”. המילים שחוזרות הכי הרבה: analysis, step, success, analyst, insight. בסרטון הזה יש 84 משפטים ו-1295 מילים לתרגול Shadowing. הדיבור נמשך 7:15. הדובר מדבר בקצב טבעי של כ-178 מילים בדקה, קרוב לשיחה יומיומית. 84% מהמילים נמצאות בין 3,000 המילים הנפוצות ביותר באנגלית; כדאי ללמוד את השאר לפני שמתחילים.
אוצר מילים מרכזי בסרטון הזה
15 המילים המתקדמות ביותר בסרטון, עם ההגייה והמשמעות שלהן:
| מילה | הגייה | משמעות |
|---|---|---|
| metric שם תואר | /ˈmɛt.ɹɪk/ | מֶטְרִי |
| literacy שם עצם | /ˈlɪtəɹəsi/ | אוֹרְיָנוּת |
| summarize פועל | /ˈsʌməˌɹaɪz/ | סיכם |
| transform פועל | /tɹænsˈfɔɹm/ | לשנות |
| measurement שם עצם | /ˈmɛʒ.ə.mənt/ | מְדִידָה |
| recommendation שם עצם | /ˌɹɛkəmɛnˈdeɪʃən/ | הַמְלָצָה |
| concise שם תואר | /kənˈsaɪs/ | תַּמְצִיתִי |
| takeaway שם עצם | /ˈteɪkəweɪ/ | טֵיְיק אֶוֶוי, אֲרוּחַת קַח וָלֵךְ |
| tangible שם תואר | /ˈtæn.d͡ʒə.bəl/ | מוחשי |
| leverage שם עצם | /ˈlɛv.(ə.)ɹɪd͡ʒ/ | הנפה |
| consume פועל | /kənˈsum/ | צרך |
| emerge פועל | /ɪˈmɝd͡ʒ/ | צץ, התגלה |
| clarity שם עצם | /ˈklæɹ.ɪ.ti/ | בהירות |
| sleeve שם עצם | /sliv/ | שַׁרְוּול |
| slice שם עצם | /slaɪs/ | פְּרוּסָה, נֵתַח |
דקדוק בסרטון הזה
המבנים שהדובר משתמש בהם הכי הרבה, עם המילים המדויקות מהסרטון:
| מבנה | בסרטון |
|---|---|
| הווה מושלם (Present perfect) have/has + past participle — פעולה שקרתה בעבר ועדיין יש לה חשיבות או השפעה עכשיו | you've reached · you've wrapped · you've found |
| משפטי זיקה (Relative clauses) who / which + clause — מידע נוסף על אדם או דבר | Maven, which will · visualization, which is · top, which will |
הגייה ששווה לשים לב אליה
הדובר משתמש ב-26 קיצורים וצורות מקוצרות, כמו you're, don't, you'll. אמרו אותם בצורה הקצרה, כפי שאתם שומעים אותם.
- הצלילים של “sh” ו-“zh”: actionable /ˈæk.ʃə.nə.bəl/, expectation /ˌɛk.spɛkˈteɪ.ʃən/, measurement /ˈmɛʒ.ə.mənt/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/
- מילים ארוכות — שימו לב להטעמה: actionable /ˈæk.ʃə.nə.bəl/, literacy /ˈlɪtəɹəsi/, explanatory /ɪkˈsplæn.ə.t(ɔː.)ɹi/, analytical /ˌæn.əˈlɪt.ɪ.kəl/, expectation /ˌɛk.spɛkˈteɪ.ʃən/
צלילים שדוברי עברית מתקשים בהם:
- /w/ — שפתיים מעוגלות, לא /v/: quantify /ˈkwɑn.tɪˌfaɪ/, takeaway /ˈteɪkəweɪ/, workflow /ˈwɝkfloʊ/
- /æ/ — פתוח יותר מה-“אֶ” העברית, לא כמו “a” ב-“cup”: analyst /ˈænəlɪst/, actionable /ˈæk.ʃə.nə.bəl/, explanatory /ɪkˈsplæn.ə.t(ɔː.)ɹi/, analytical /ˌæn.əˈlɪt.ɪ.kəl/, analyze /ˈæn.əˌlaɪz/
- /r/ — הלשון לא נוגעת בשום מקום, לא ה-“ר” הגרונית: metric /ˈmɛt.ɹɪk/, literacy /ˈlɪtəɹəsi/, propose /pɹəˈpəʊz/, explanatory /ɪkˈsplæn.ə.t(ɔː.)ɹi/, summarize /ˈsʌməˌɹaɪz/
איך לתרגל עם הסרטון הזה
- הקשיבו לכל הסרטון פעם אחת בלי לדבר, ורשמו את המילים שאתם לא מכירים.
- התחילו במהירות 0.75×, תרגלו Shadowing משפט אחר משפט, וחזרו למהירות רגילה כשזה מרגיש קל.
- הקליטו את עצמכם והשוו למקור, תוך תשומת לב למילים כמו metric, literacy, summarize.
מהי טכניקת השאדואינג?
שאדואינג היא טכניקת למידת שפה מבוססת מדע שפותחה במקור להכשרת מתורגמנים מקצועיים ופופולרית על ידי הפוליגלוט ד"ר אלכסנדר ארגואלס. השיטה פשוטה אך עוצמתית: אתה מקשיב לאודיו באנגלית ילידית וחוזר עליו בקול רם מיד — כמו צל העוקב אחרי הדובר עם עיכוב של 1–2 שניות בלבד. בניגוד להאזנה פסיבית או תרגילי דקדוק, שאדואינג מכריח את המוח והשרירים בפה לעבד ולשחזר דפוסי דיבור אמיתיים בו זמנית. מחקרים מראים שזה משפר משמעותית את דיוק ההגייה, האינטונציה, הקצב, הדיבור המחובר, הבנת השמיעה ושטף הדיבור — מה שהופך אותו לאחת השיטות היעילות ביותר להכנה לIELTS Speaking ולתקשורת באנגלית בעולם האמיתי.