Shadowing Practice: 8 Years of Data Analytics Advice in 12 Minutes - Learn English Speaking with Video

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If there's one thing I'm really good at, it's data analytics.

For the past seven years, I've analyzed data as some of the fastest growing tech startups

and I've built a community of over 350 ,000 data professionals just like you.

People ask me every day to be their mentor

and people have even offered me thousands of dollars for just one day of coaching, but I'm giving it all to you for free here on YouTube.

So sit back, relax, and get ready to learn everything you need to know to start your data journey, grow your skills, advance your dream career, and land a six -figure data analytics job.

can't sit through this 10 minute video without clicking away, you might not be cut out for a job in data.

So stay to the end and don't forget to subscribe because I know you want to.

But before we jump in, grab my free gift for you, which is a three -step data analytics roadmap.

The link is below and it tells you everything you need to do to land your first job in data.

These are the top five lessons that I've learned throughout my career in data

and I'm going to save you years of frustrations and help you grow your career more quickly.

Number one, we're starting out pretty you promoted.

And it's true, being really good at SQL, Python, or Tableau, or being really good at some technical skill, it will make you a good analyst,

but it's probably not going to make you a senior analyst.

And it's most definitely not going to make you a lead or staff analyst.

Because technical skills are great.

It's how you actually get your hands dirty and get the job done.

But unfortunately, it's not who's the best at their job or who knows the most who gets promoted.

In data analytics, promotions happen when you're a multiplier.

It's not just about

work but it's about multiplying that work and turning it into business outcomes mentorship

and driving leadership so it's not just about the code you write

or the cool insights you find it's about how you use those

and really impact the company i spent so long taking more courses

and just trying to level up my technical skills

so somebody would notice me but i realized business stakeholders

and managers don't care about technical skills those are things

that you can always look up along the way they care about other things that'll actually get you promoted

and here they are number being proactive.

You have to anticipate problems before they happen and already have recommendations and ideas to move forward.

Number two, relationships.

You have to play the politics.

If nobody's buying what you're selling, it doesn't matter how good what you're selling is.

It's not just about creating good insights and doing a good analysis.

It's about getting buy -in from leadership, getting them to understand and actually make a change based on what you're presenting.

And they're not going to do that unless you have a really good relationship with them and they trust you.

And into business action.

So instead of saying conversion dropped 8%, that's a data point, that's a KPI, that's thinking like a data analyst.

We have to translate that in a way where non -technical business stakeholders can actually understand what to do about it.

So instead, we might say something like conversion dropped 8 % because all the users in segment A did this.

Here's what we can do about it and provide recommendations.

So it's not the technical skills that are actually going to get you promoted.

It's all about playing politics, making your work visible, building

All of those gross soft skills

that I know we don't want to think about as data analysts but they're necessary if you want to move up.

So learn how to play the game.

The second lesson I learned is that a lot of times the data doesn't exist.

I don't know about you, but when I first started as a data analyst, I thought that I was just going to have all the perfect data and it was just going to be beautiful, ready to go.

And I was going to have all the skills I needed to succeed.

But I was wrong.

I realized really quickly that a lot of times stakeholders were asking questions that we didn't even have data for.

And sometimes that was a problem that I could solve for.

And other times I couldn't.

that you can approach things when you don't have the data that you need and it doesn't exist.

The first way is my least favorite, but maybe it's my most favorite because it's the least amount of work for me.

But the first way is by saying, "Sorry, we don't have the data.

We can't do that.

We really wish we did, but sorry, we just can't." And sometimes

that truly is the best you can do and there's nothing you can do about it.

Another thing you can do to get data

when you need it is to work cross -functionally with different stakeholder teams to gather all the data you need.

And sometimes that'll look like logging data into HubSpot or Salesforce.

data, inaccurate data, null values, duplicates, and a lot of messy data.

But sometimes relying on imperfect data from business stakeholders is better than having no data.

So you just have to take it and run with it and do the best you can.

Another thing you can do is get super creative and transform data you already have.

So maybe you can do cool things in SQL, calculations, transformations, and just kind of engineer things together.

Maybe you're bringing in external data sources or APIs and just kind of, you know, band -aiding things together.

My very favorite exist when it doesn't is to track front end events and create events in the database.

There's a lot of tools that can help you do this, but a lot of times you're working with engineers on your team, data engineers, front end engineers to kind of design these events.

So basically every time some sort of event happens in the product, maybe it's a click or a scroll that gets logged in the database.

So you can work with engineers to kind of define what you want that event to look like.

So the schema, the properties, the event names, the fields you're logging, and then you can actually create data by tracking it and logging it in the database.

So yeah, that was a hard lesson for me to learn because I was like, wow, it's actually a lot of work just to get the data I need to do my job.

If you want to start your journey to become a data analyst today and you're super confused and overwhelmed, don't worry, I've got you.

Grab my three -step data analytics roadmap in my description below.

It'll tell you all the steps you need to take to your first data analytics job

and help you cut through the noise and ignore all the fluff.

It's not easy, but I promise it is very rewarding.

One of my students just landed her first $1 ,000 salary increase from her previous job.

The third big lesson I learned is before doing, ask why.

It depends on the data culture at your company, but a lot of companies have analysts working like reporting vending machines.

If you know, you know, that's when stakeholders are like, oh my gosh, emergency, everything's on fire.

I need this report right now.

You stop what you're doing and you do the report.

And that's a very reactive data analytics team because you're just keeping up with all the demands, all the requests and doing exactly as you're asked, which is a more level of data analytics.

A more senior level data analytics team is going to be more proactive rather than reactive.

Based on leadership goals, they're going to anticipate different projects and demands instead of just reacting to last minute stakeholder emergencies.

So they're not just going to take in these requests and do them.

They're going to ask why.

Why do you need to know?

Why is this important to you and your team?

How is it going to impact the business?

And instead of asking questions like what product do you want?

What company do you want?

What time period do you want?

They're going to ask deeper and more meaningful questions like, what business question do you hope to answer?

What would signal to you that this project was a success?

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What would signal to you that this project was not a success and we should roll it back?

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Is there anything you're really worried about with this project?

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What's the risk involved here?

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What types of question does leadership and your manager ask about this project?

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By actually taking a step back and asking why

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and asking about the business goals and what they want to learn and what they want to find out, you're going to learn way more and be able to do a way more meaningful analysis

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level questions or no questions at all.

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And you just do it without thinking.

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When I first started my career, I was a people pleaser.

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I was a data order taker.

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I would say, yes, what do you want?

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Got it.

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I'm on it.

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And I would do everything I was asked immediately because I was scared to say no and push back.

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But I've learned that managing stakeholders and pushing back

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and asking questions professionally is actually what makes you a really good data analyst.

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So don't be a data order taker, be a data advisor.

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The fourth lesson I've learned is that data is not always black and white.

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huge gray area in data analytics, and sometimes you just have to make the best decision that you can make based on the information you have.

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Everyone wants clear, confident, objective answers from data that say yes or no and point in an exact direction, but that's not always the case.

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A lot of times we're living in this weird gray area where we're not 100 % sure what to do.

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The reason why is a lot of things actually.

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Data can be incomplete.

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It can be missing.

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It can be messy.

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It can be unreliable, inaccurate.

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or not set up super well.

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Sometimes you can have multiple metrics telling different stories

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and you just have to evaluate the risk and the trade -off the best you can and provide a recommendation.

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And you'll do this a lot of times by working with different business stakeholders.

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You'll say, here's what I see.

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What are the pros and cons of choosing this?

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What are the pros and cons of choosing this?

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And you try to come up with the best decision based on what you have.

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So an example, what if you do some sort of product change and conversion rates go up,

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but also you see a large that more people are converting and buying the product, but you're also having a higher rate of people requesting refunds.

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So you have to think, is our main goal to increase conversion?

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Is it to increase revenue?

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Or maybe it's something else.

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You have to think, what is our main metric that we're going to use to make a decision on?

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And you have to be able to explain all of these trade -offs to stakeholders and say, yay, conversion went up, but so did refund rates.

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And that's not good.

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Because a lot of times stakeholders get so distracted by the shiny objects and they see

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ready to report that huge success to their leadership to make themselves look good.

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But as a data analyst, you would have to kind of put that into track and also explain the tradeoffs.

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Another example is what if you ran an A /B test about a potential product experiment

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and it showed a tiny bit of a lift in a metric that you were evaluating.

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So a little bit of a lift is good, it's not a negative thing, but what if it would take a significant amount of resources to fully roll out that product change?

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Is it worth it to use that many company resources

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and take weeks or months to build and roll something out for only this much of a lift?

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Real data analytics decision to make.

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And that's why it was hard for me when I first started out my career.

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I thought I would be looking at metrics and it would be yes or no,

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but the real value comes in evaluating risks and trade -offs and really understanding business decisions and their impact.

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The fifth lesson that I've learned is

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that your manager can have a huge impact on the data culture at your company

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and it can really impact your experience there.

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You can be the best data analyst in the whole world

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and your team can have so many valuable insights that can actually make huge you and they don't care about the data, then it's not going to go anywhere.

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Data culture in an organization flows from the top down.

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So if your manager is not advocating for your team

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and really talking about the importance of the data with leadership for you on your behalf, they might not care about what work you're doing.

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They literally might have you running in circles, copying and pasting in Excel sheets because they don't understand the really cool things and more advanced things that you're doing.

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Like a lot of things, it's really all about politics.

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So if your that data in front of leadership, you're probably in a pretty good spot.

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But if they're just taking a ton of orders

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and pushback from leadership and dumping it on you and turning you into a data order taker, you're probably not going to have a great time as a data analyst.

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I truly believe one of the best skills a data manager can have is stakeholder management.

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Not only should your manager be advocating for your team to get visibility in front of leadership, but they should also be managing all the crazy stakeholder requests and helping push back and just be like a

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and push back on all the nonsense so you can actually put your head down and work and get things done.

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I've seen really good data culture in an organization

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and I've seen really bad data culture in an organization

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and you truly don't understand what is good and what is bad until you've seen and experienced both.

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But I've learned that a lot of it comes down to your manager and leadership and their relationship.

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These lessons took me years to learn, but they completely changed my entire career.

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If you're serious about breaking into data analytics or advancing into a more serious data role, are not the technical skills.

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It's how to apply them to real business problems and play the politics.

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Don't forget to grab my three -step data analytics roadmap today in the description.

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It'll give you clarity on your next steps and how to get started today.

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If this video helped you, hit that subscribe button for more real -world data analytics advice.

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Sending you lots of big data energy your way.

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Bye!

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145 cümleler

Bu dersin kelimeleri ve konuşma notları

Bu videoda gölgeleme çalışması için 181 cümle ve 2585 kelime var. Konuşma bölümü 12:14 sürüyor. Konuşmacı hızlı konuşuyor, dakikada yaklaşık 211 kelime; bu yüzden birbirine bağlanan ve zayıflayan sesler duyacaksınız. Kelimelerin %87’i İngilizcede en sık kullanılan 3.000 kelime arasında, bu yüzden kelime dağarcığını takip etmek kolay.

Bu videodaki önemli kelimeler

Videodan daha az kullanılan 15 kelime, telaffuzu ve anlamıyla:

skill /skɪl/ (isim) — beceri, maharet. A capacity to do something well; a technique, an ability, usually acquired or learned, as opposed to abilities that are regarded as innate.
lesson /ˈlɛs.ən/ (isim) — ders, çimke. A section of learning or teaching into which a wider learning content is divided.
conversion /kənˈvɝʒən/ (isim) — çevirim, dönüşüm. The act of converting something or someone.
log /lɔɡ/ (isim) — kütük. The trunk of a dead tree, cleared of branches.
database /ˈdeɪtəˌbeɪs/ (isim) — veritabanı, veri tabanı. A collection of (usually) organized information in a regular structure, usually but not necessarily in a machine-readable format accessible by a computer.
recommendation /ˌɹɛkəmɛnˈdeɪʃən/ (isim) — tavsiye. An act of recommending.
evaluate /ɪˈvaljʊeɪt/ (fiil) — değerlendirmek. To draw conclusions from examining; to assess; to appraise.
gray /ɡɹeɪ/ (sıfat) — gri, boz. Of a color between black and white, having neutral hue and intermediate brightness.
resource /ɹɪˈzɔːs/ (isim) — kaynak. Something that one uses to achieve an objective, e.g. raw materials or personnel.
meaningful /ˈmiː.nɪŋ.fl̩/ (sıfat) — anlamlı, manalı. Having meaning, significant.
subscribe /səbˈskɹaɪb/ (fiil) — abone olmak. To write (one’s name) at the bottom of a document; to sign (one's name).
refund /ɹɪˈfʌnd/ (isim) — geri ödemek. An amount of money returned.
messy /ˈmɛsi/ (sıfat) — darmadağınık, karmakarışık. In a disorderly state; chaotic; disorderly.
reactive /ɹiːˈæktɪv/ (sıfat) — gerici. That reacts or responds to a stimulus.
ignore /ɪɡˈnɔː/ (fiil) — görmezden gelmek. To deliberately not listen or pay attention to.

Duyacağınız deyimsel fiiller

find out (fiil) — anlamak, ortaya çıkarmak. To discover, as by asking or investigating.
log in (fiil) — giriş yapmak. To gain access to a computer system, usually by providing a previously registered username and password.
look at (fiil) — bakmak. To observe or watch.
look up /ˌlʊk ˈʌp/ (fiil) — yukarı bakmak. To have better prospects.
roll out (fiil) — sunmak. To deploy or release (a new film or software, etc.); to launch (a product or service), especially in a gradual fashion across multiple regions.

Dikkat edilecek telaffuzlar

Konuşmacı you're, don't, I've gibi kısaltılmış biçimleri 72 kez kullanıyor. Bunları duyduğunuz gibi kısa söyleyin.

“sh” ve “zh” sesleri: conversion /kənˈvɝʒən/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, promotion /pɹəˈmoʊʃn̩/, sheet /ʃit/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/
Uzun kelimeler — vurguyu doğru yere koyun: analytics /ˌæn.əˈlɪt.ɪks/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, anticipate /ænˈtɪs.ɪ.peɪt/, inaccurate /ɪnˈæk.jə.ɹɪt/, experiment /ɪkˈspɛɹ.ə.mənt/

Bu videoyla nasıl çalışılır

Videonun tamamını konuşmadan bir kez dinleyin ve bilmediğiniz kelimeleri not edin.
0,75× hızla başlayın, cümle cümle tekrar edin ve kolaylaşınca normal hıza dönün.
Kendinizi kaydedin ve orijinaliyle karşılaştırın; skill, lesson, conversion gibi kelimelere özellikle dikkat edin.

Bu videodaki dil bilgisi

Konuşmacının en çok kullandığı yapılar, videodaki sözcüklerin aynısıyla:

Yapı	Videoda
Present perfect have/has + fiilin üçüncü hâli — geçmişte olan ama şimdi de önemli olan bir eylem	I've analyzed · I've built · have even offered
Edilgen yapı be + fiilin üçüncü hâli — kimin yaptığı değil, ne olduğu önemli	might not be cut · was asked

Gölgeleme Tekniği Nedir?

Gölgeleme, başlangıçta profesyonel tercüman eğitimi için geliştirilen ve çok dilli Dr. Alexander Arguelles tarafından popüler hale getirilen, bilim destekli bir dil öğrenme tekniğidir. Yöntem basit ama güçlüdür: ana dili İngilizce olan bir sesi dinler ve hemen yüksek sesle tekrar edersiniz — konuşmacıyı 1-2 saniye gecikmeyle takip eden bir gölge gibi. Pasif dinleme veya dilbilgisi alıştırmalarının aksine, gölgeleme beyninizi ve ağız kaslarınızı gerçek konuşma kalıplarını eşzamanlı olarak işlemeye ve yeniden üretmeye zorlar. Araştırmalar, telaffuz doğruluğu, tonlama, ritim, bağlı konuşma, dinleme anlama ve konuşma akıcılığını önemli ölçüde geliştirdiğini göstermektedir — bu da onu IELTS Konuşma hazırlığı ve gerçek dünya İngilizce iletişimi için en etkili yöntemlerden biri yapar.

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Shadowing Practice: 8 Years of Data Analytics Advice in 12 Minutes - Learn English Speaking with Video

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