Shadowing-Übung: 8 Years of Data Analytics Advice in 12 Minutes - Englisch Sprechen Lernen mit Video

Übungsmodus wählen

Jeder Modus öffnet einen Übungsraum im Vollbild.

181 Sätze

Wenn Sätze zu kurz oder zu lang sind, klicke auf Edit, um sie anzupassen.

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?

100

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

101

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

102

What's the risk involved here?

103

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

104

By actually taking a step back and asking why

105

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

106

level questions or no questions at all.

107

And you just do it without thinking.

108

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

109

I was a data order taker.

110

I would say, yes, what do you want?

111

Got it.

112

I'm on it.

113

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

114

But I've learned that managing stakeholders and pushing back

115

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

116

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

117

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

118

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.

119

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.

120

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

121

The reason why is a lot of things actually.

122

Data can be incomplete.

123

It can be missing.

124

It can be messy.

125

It can be unreliable, inaccurate.

126

or not set up super well.

127

Sometimes you can have multiple metrics telling different stories

128

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

129

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

130

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

131

What are the pros and cons of choosing this?

132

What are the pros and cons of choosing this?

133

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

134

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

135

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.

136

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

137

Is it to increase revenue?

138

Or maybe it's something else.

139

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

140

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.

141

And that's not good.

142

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

143

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

144

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

145

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

146

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

147

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?

148

Is it worth it to use that many company resources

149

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

150

Real data analytics decision to make.

151

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

152

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

153

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

154

The fifth lesson that I've learned is

155

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

156

and it can really impact your experience there.

157

You can be the best data analyst in the whole world

158

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.

159

Data culture in an organization flows from the top down.

160

So if your manager is not advocating for your team

161

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.

162

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.

163

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

164

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

165

But if they're just taking a ton of orders

166

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.

167

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

168

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

169

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

170

I've seen really good data culture in an organization

171

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

172

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

173

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

174

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

175

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

176

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

177

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

178

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

179

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

180

Sending you lots of big data energy your way.

181

Bye!

App herunterladen

Alles, was du zum fließenden Sprechen brauchst

KI-AussprachebewertungJeder Satz bewertet

IPA-TrainingJeden Laut meistern

WortschatzBaue deinen Wortschatz auf

VokabelspielSpielend lernen

QR scannen

Download on theApp Store Get it onGoogle Play

FOR YOU

Empfohlenes Video

145 Sätze

Wortschatz und Sprechhinweise zu dieser Lektion

Dieses Video enthält 181 Sätze und 2585 Wörter zum Nachsprechen. Der gesprochene Teil dauert 12:14. Der Sprecher spricht schnell, etwa 211 Wörter pro Minute – rechne mit verbundenen und verkürzten Lauten. 87 % der Wörter gehören zu den 3.000 häufigsten im Englischen, der Wortschatz ist also leicht zu verfolgen.

Wichtiger Wortschatz in diesem Video

15 weniger häufige Wörter aus dem Video, mit Aussprache und Bedeutung:

analytics /ˌæn.əˈlɪt.ɪks/ (Substantiv) — Analytik. The principles governing any of various forms of analysis.
skill /skɪl/ (Substantiv) — Fähigkeit, Fertigkeit. 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/ (Substantiv) — Lehrstunde, Stunde. A section of learning or teaching into which a wider learning content is divided.
conversion /kənˈvɝʒən/ (Substantiv) — Umwandlung. The act of converting something or someone.
log /lɔɡ/ (Substantiv) — Rundholz (forestial), Langholz (forestial). The trunk of a dead tree, cleared of branches.
metric /ˈmɛt.ɹɪk/ (Adjektiv) — metrisch. Of or relating to the metric system of measurement.
database /ˈdeɪtəˌbeɪs/ (Substantiv) — Datenbank. A collection of (usually) organized information in a regular structure, usually but not necessarily in a machine-readable format accessible by a computer.
insight /ˈɪnsaɪ̯t/ (Substantiv) — Einblick. A sight or view of the interior of anything; a deep inspection or view; introspection; frequently used with into.
recommendation /ˌɹɛkəmɛnˈdeɪʃən/ (Substantiv) — Empfehlung, Rekommandation. An act of recommending.
evaluate /ɪˈvaljʊeɪt/ (Verb) — evaluieren, auswerten. To draw conclusions from examining; to assess; to appraise.
gray /ɡɹeɪ/ (Adjektiv) — grau. Of a color between black and white, having neutral hue and intermediate brightness.
resource /ɹɪˈzɔːs/ (Substantiv) — Ressource, Mittel. Something that one uses to achieve an objective, e.g. raw materials or personnel.
con /kɑn/ (Verb) — betrügen, austricksen. To trick, lie or defraud, usually for personal gain.
advocate /ˈæd.və.kət/ (Substantiv) — Advokat, Advokatin. Someone whose job is to speak for someone's case in a court of law; a counsel.
meaningful /ˈmiː.nɪŋ.fl̩/ (Adjektiv) — bedeutend, bedeutungsvoll. Having meaning, significant.

Phrasal Verbs, die du hören wirst

break into (Verb) — einbrechen, eindringen. To enter illegally or by force, especially in order to commit a crime.
come in /ˌkʌm ˈɪn/ (Verb) — ins Spiel kommen. To enter.
come up with (Verb) — ausdenken. To manage to produce, deliver, or present (something) by inventing, creating, thinking of, or obtaining it.
find out (Verb) — herausfinden, erfahren. To discover, as by asking or investigating.
go up (Verb) — hinaufgehen, steigen. To move upwards.
keep up with (Verb) — mithalten, mit etwas Schritt halten. To manage to remain beside or just behind someone or something that is moving away from one.
log in (Verb) — sich anmelden, sich einloggen. To gain access to a computer system, usually by providing a previously registered username and password.
look at (Verb) — schauen. To observe or watch.

Aussprache, auf die du achten solltest

Der Sprecher verwendet 72 Kurzformen und abgeschwächte Formen, zum Beispiel you're, don't, I've. Sprich sie in der kurzen Form, so wie du sie hörst.

Die Laute „sh“ und „zh“: conversion /kənˈvɝʒən/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, promotion /pɹəˈmoʊʃn̩/, sheet /ʃit/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/
Lange Wörter – auf die Betonung achten: analytics /ˌæn.əˈlɪt.ɪks/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, anticipate /ænˈtɪs.ɪ.peɪt/, inaccurate /ɪnˈæk.jə.ɹɪt/, experiment /ɪkˈspɛɹ.ə.mənt/

So übst du mit diesem Video

Höre dir das ganze Video einmal an, ohne zu sprechen, und notiere die Wörter, die du nicht kennst.
Beginne mit 0,75-facher Geschwindigkeit, sprich Satz für Satz nach und wechsle zur normalen Geschwindigkeit, sobald es leichtfällt.
Nimm dich auf und vergleiche mit dem Original; achte dabei auf Wörter wie analytics, skill, lesson.

Grammatik in diesem Video

Die Strukturen, die der Sprecher am häufigsten verwendet, mit den genauen Worten aus dem Video:

Struktur	Im Video
Present Perfect have/has + Partizip Perfekt – eine vergangene Handlung, die jetzt noch wichtig ist	I've analyzed · I've built · have even offered
Passiv be + Partizip Perfekt – wichtig ist, was geschieht, nicht wer es tut	might not be cut · was asked

Was ist die Shadowing-Technik?

Shadowing ist eine wissenschaftlich fundierte Sprachlerntechnik, die ursprünglich für die professionelle Dolmetscherausbildung entwickelt und durch den Polyglotten Dr. Alexander Arguelles populär gemacht wurde. Die Methode ist einfach aber wirkungsvoll: Du hörst englisches Audio von Muttersprachlern und wiederholst es sofort laut — wie ein Schatten, der dem Sprecher mit nur 1–2 Sekunden Verzögerung folgt. Anders als passives Hören oder Grammatikübungen zwingt Shadowing dein Gehirn und deine Mundmuskulatur, gleichzeitig echte Sprachmuster zu verarbeiten und zu reproduzieren. Studien zeigen, dass es Aussprachegenauigkeit, Intonation, Rhythmus, verbundene Sprache, Hörverständnis und Sprechflüssigkeit signifikant verbessert — was es zu einer der effektivsten Methoden für die IELTS Speaking-Vorbereitung und reale englische Kommunikation macht.

Shadowing-Technik: die vollständige Schritt-für-Schritt-Anleitung lesen →

Shadowing-Übung: 8 Years of Data Analytics Advice in 12 Minutes - Englisch Sprechen Lernen mit Video

Übungsmodus wählen

App herunterladen

Data Science Interview Prep: 3 Key Resources You Need to Know

She learned one new skill that unlocked a $120K salary

How to Become a Great Software Developer — Best Advice from Top-Notch Engineers

Please get a job...my advice to Gen Zs and young millennials...

Tell Me About Yourself (The BEST Way to Answer this Interview Question)

Characteristics of a Data and Analytics Leader

Steven Bartlett’s Secret To Business Success (Hint: It’s Not Hard Work)

this will change how you make YouTube videos FOREVER

Learn how to SOLVE a data analytics case study problem

How To Dominate 2026 as a Software Developer

How to be a RECRUITER with no EXPERIENCE?! Explained by Recruiter

think like a business analyst in tech (3 core skills + solving case study)