ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: How Modern Data Teams Work (Engineer, Analyst, Scientist, Architect, ML)

Alright, now first I would like you to understand the role of that data analyst.

So what is a data analyst?

Well, it is very simple.

You answer the business questions using data.

Think about yourself as a bridge between the raw data and the real business decisions.

So now I can say that I have joined like around seven data teams and projects.

And now I would like you to understand exactly what data analysts do in real companies.

So now it is a story time.

Let's go.

Okay, so now in companies in business, we have managers, stakeholders, project leads,

and their whole job is to make smart decisions by asking critical questions.

So for example, which region is underperforming?

Why are profits down?

And should we invest here?

So there are many challenges in the business and they need quick and smart decisions.

So now without using data, they are just guessing.

They rely on opinions, gut feelings, and many outdated informations left and right.

And there is famous quote from William Deming, I really like and use a lot.

Without data, you are just another person with an opinion.

So that means if your decision process is based on opinions, this has high chance to lead to confusion, wasted resources, and making bad decisions for the business.

I think that pretty much sums it up.

So now instead of guessing, they have to decide based on data.

So now in companies, the data are scattered everywhere.

Your data is stored in many different databases.

There are a lot of spreadsheets and maybe as well APIs that provides data.

So they are everywhere and the managers of course don't have the time

or the skills in order to dive into all those places.

So that's why they go and hire an expert specialist.

They hire the data analyst.

So think about it like you are a detective and you start gathering data from different sources.

So you go and query the different databases

and as well pull the data from the spreadsheets

and the different apis so you have to go

and get the data wherever they lives

so of course all those data are messy and you have to go

and clean and structure those data so maybe you're gonna go

and put the data everything in one spreadsheet like in excel

and you start doing a lot of stuff on the data

cleaning up the data organizing it doing a lot of calculations

transformations to get the data ready to answer the business question

now after you find something meaningful from the data you start turning the data into insights into a visual report

because it is way easier to communicate the result to the stack holders

and managers using visuals so that means once you are ready you go to the managers

and you start presenting the result as a story using the report

and visuals that you have prepared now the managers got facts

and this time they have answers to their questions using data

so now they are more confident and they make better

and smarter decisions for the business

so now we can see clearly what the data analyst does he

or she is the bridge between the raw data on the left side

and the business on the right side

and all what he is doing is answering the critical business questions using the raw data

and as well it is very clear which skills this analyst needs

so as we understood first you will be querying the databases in order to pull the data

and in order to talk to the data

and the databases you use sql or sometimes we call it SQL

and we have understood as well once you collect all the

data you will be using spreadsheet like Excel in order to clean, sort, filter and do calculations on the data

so they have to master Excel

and after analyzing the data using the spreadsheet you need data visualization skills using tools like maybe barbi

or tableau to create charts and clear visuals to be reported to the managers

and of course one very important skill

and crucial you must clearly understand the business questions from the managers

and communicate the results and the findings very effectively

so you have to master this skill you have to tell a story behind the data

so you need really very good communication skills so that means

if you master those skills you will be an amazing data analyst

that can help the company and the managers making the smart decisions

and now my friends we have an issue this might work for a small company

but it will not work for a modern big companies

and here's why now big companies generates big data

and it's gonna be really hard for the data analyst to manually extract the data for analyzers

because you're gonna need a lot of data

and your request gonna take like hours and even days to get the data

and it might crash and it's gonna be sometimes impossible to get the data

so it's gonna be for you as a data analyst really hard to answer any questions

and your process gonna take weeks until you give any answers

and And of course managers cannot wait for weeks in order to get like an answer.

So my friend, this approach will not work.

This is simply does not scale.

So now we have to go and scale things and hire new people.

Now a data architect, it's like an architect of a building.

You have to design the blueprint of a scalable data system.

And you have to go and organize the data into multiple layers.

Like for example, the Medellin architecture.

You have a bronze layer for the raw data.

And a silver layer for a clean data.

And a gold and very important layer for clean, structured and optimized data.

So all what the data architect is doing is designing this scalable data system.

So now as you can see, the data architect is designing this scalable data system.

But as usual, they are not the one that is building it.

For that, we need another expert.

And we call them the data engineers.

So now as a data engineer you're gonna go and build something called data pipelines Well, it's all about to connect to multiple source systems and start automatically

Moving the data from the sources to the new data system

and as well very important the data must move quickly

So the pipeline is gonna bring the data to the first

layer to the province layer as a raw data Nothing fancy

gonna happen here Then the data engineer gonna move the data to the server layer

100

and there the data get cleaned structured and prepared for the final layer.

101

Now in the final layer the data engineer can go

102

and build a data model which is highly organized and optimized model

103

that is perfectly made for quick analyzes and we have with

104

that a process that runs automatically from the source systems until the final layer

105

and this runs every day very fast and in some scenarios as a stream like a real life data.

106

So remember now our data analyst friend so now thanks to the data architect

107

and the data engineer analyst's life now is way easier no

108

more nightmare of pulling the data manually from the source system no need anymore for the spreadsheets

109

and the excel lists now everything is prepared for the data analyst to be fast

110

so now again as a data analyst once you get a question from the business you can go quickly

111

and straight away to the GodLayer where everything is prepared.

112

And now using SQL, you will be querying the data model in the GodLayer in order to explore

113

and try to find answers for the question.

114

So once you find it, you will go and build again like a visual report and tell a story for the managers.

115

And with this setup, you only focus on what matters when finding answers for the business.

116

And this can speed the whole process for you and you will be ready to answer quickly the questions.

117

Now I'm really sorry we are not done yet.

118

There is still an issue in this story now all

119

that you are doing here as a data analyst is

120

that You are answering ad hoc questions

121

and your answers are always like one -time reports

122

But what can happen for sure is

123

that one of your reports can deliver really an incredible value

124

And it is not just useful for once

125

and only for the managers Many people across the company can be interested in this report

126

So you can have a new user groups that can ask you one question I want your report every day.

127

No, God, please, no, no!

128

And the nightmare gonna come back, where you have every day to run the same query and generate the same reports.

129

And you're gonna daily send this report to the users.

130

And this is gonna be total waste of your time and gonna build huge stress on you.

131

And that's why companies gonna bring another person to the data team.

132

And this time we're gonna have the Business Intelligence Developer or BI Developer.

133

So once the BI developer joins you as a data is gonna say hey go

134

and automate this report and make it visible for Many users

135

so first the BI developer gonna build an environment like a server where the dashboards

136

and reports can live This must be secure

137

and has an access management Sometimes you cannot give all the data for everyone in the company

138

and as well this server must be accessible 24 -7

139

so the report and dashboard must be always available

140

So once the reporting server is there the bi developer can take everything the report

141

and the logic and built from it something called Interactive dashboards

142

and what is very important this dashboard gonna be automatically Connected to the gold layer to the data model

143

So that's every day and this is important the dashboard

144

and the reports can I get fresh data from the data system from the gold layer?

145

And once everything is live the user is gonna go

146

and start requesting access to the reports and start accessing the dashboards

147

and the data everything is highly automated and scalable

148

so the data gonna flow from the sources into the different layers of our data system

149

and as well automatically flowing to the reporting server

150

and refreshing the data there so this is the big differences between the data analyst

151

and the bi developer data analyst is the one

152

that is bringing the first report and insights and once a visual

153

or a report gets very important the bi My developer is the one that is responsible of making this report accessible,

154

scalable and every day available for many users.

155

Now so far we have held the business and the managers with questions that are about the past and the present.

156

What happened?

157

How much did we sell?

158

Why did sales drop?

159

But at some point the managers can ask bigger questions.

160

Questions about the future what will happen next month?

161

Can we predict which customers might leave us?

162

What can happen if we change our pricing strategy?

163

So they are advanced questions about the future

164

and to be honest the data analysts don't have the tools in order to answer them

165

and for that we need another expert called the data scientist.

166

This is Sheldor the conqueror we are about to enter Atsul's fortress.

167

The data scientist is the one that can use the data in order to build and train models.

168

They gonna do a lot of experiments

169

so at the end the scientists gonna use this model in order to give answers for the future so with

170

that the managers are not just informed they are as well

171

prepared for the future they are proactively making decisions they stay ahead of the competition

172

and as well they tackle challenges before they even appear now

173

this type of work is very important thing not only for the managers

174

and projects leads this is important for everyone else now this

175

model this work from the data scientist must be visible for other users

176

and this is the same thing that happens before right with the bi developer

177

but now we need another type we have the machine learning developer ml developer

178

so now what they're gonna do they're gonna as well prepare a server

179

and environments to run and deploy the model from the data scientist

180

so they're gonna go and put it in productive environments

181

and everything gonna be now automatically secure scalable and available 24 7

182

and the result of this model could be presented in like

183

an application to show the result of their predictions for everyone

184

or as well sometimes they show it in dashboards and reports

185

so their job is to make the result of this model available in many services at the company

186

and accessible by other users and build by the ML engineer

187

and with that everyone from the managers who teams across the company benefits from advanced predictions every day and anytime

188

so now by looking to this everything is highly automated the

189

data can flow from the sources to the data system

190

and then flows to the reports and dashboards

191

and as well automatically flow to our model

192

that is trained from the data analyst and built from the ml engineer

193

so this is what a modern big company built they build scalable data system now

194

if you look to this you can see there is a

195

pattern you can see the data architect is working closely to

196

the data engineer to build the data system the data analyst

197

works as well closely to the bi developer in order to build this reporting system

198

and the data scientist together with the ml engineer they work on building an advanced analytical system

199

and there is actually another pattern you can see in those

200

roles there is always somebody is like doing a design like the data architect is like designing the data system

201

and the data analyst is like building the first version of

202

the reports same thing for the data scientist he is the one

203

that is experimenting and training the first version of the model

204

so those one are always like trying to discover trying to put the blueprint

205

and now in the other side we have the other category you have the persons

206

that are building it bringing it to a productive scalable environment

207

and making things live like the data engineer is the one

208

that builds the data pipelines and this data system The PI developer is bringing their reports

209

and dashboards into scalable and productive

210

Reporting system and the machine learning engineer is the one

211

that is bringing the model and deploy it into a productive system And offering the results in different services.

212

So they are the engineers.

213

They are the developers So this is what a real modern data team looks like All right, my friends one more last thing

214

if you really enjoyed this type of tutorials where it's free

215

and as well I sketch those complex concepts

216

and you want more content like this then support the channel

217

by subscribing liking commenting this can really help with the algorithm

218

and can support the channel by reaching others like you

219

and as well it can help me to make more content like this

220

and of course thank you so much for watching and I will see you in the next video

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คำศัพท์และข้อสังเกตด้านการพูดสำหรับบทเรียนนี้

วิดีโอนี้มี 220 ประโยค และ 2644 คำ สำหรับฝึกพูดตาม ช่วงที่มีเสียงพูดยาว 13:50 ผู้พูดพูดเร็ว ประมาณ 191 คำต่อนาที จึงมีการเชื่อมเสียงและลดเสียงค่อนข้างมาก 87% ของคำอยู่ใน 3,000 คำที่ใช้บ่อยที่สุดในภาษาอังกฤษ คำศัพท์จึงตามได้ง่าย

คำศัพท์สำคัญในวิดีโอนี้

คำที่ควรเรียนรู้ 15 คำจากวิดีโอ พร้อมคำอ่านและความหมาย:

คำศัพท์	คำอ่าน	ความหมาย
layer คำนาม	/ˈleɪ̯ɚ/	ชั้น
scientist คำนาม	/ˈsaɪəntəst/	นักวิทยาศาสตร์
architect คำนาม	/ˈɑɹ.kɪˌtɛkt/	สถาปนิก
server คำนาม	/ˈsɝvɚ/	เซิร์ฟเวอร์
database คำนาม	/ˈdeɪtəˌbeɪs/	ฐานข้อมูล
query คำนาม	/ˈkwɪɹ.i/	สอบถาม
hire คำกริยา	/ˈhaɪɹ/	จ้าง
experiment คำนาม	/ɪkˈspɛɹ.ə.mənt/	การทดลอง
nightmare คำนาม	/ˈnaɪt.mɛɚ/	ฝันร้าย
analyze คำกริยา	/ˈæn.əˌlaɪz/	วิเคราะห์
calculation คำนาม	/ˌkælkjuˈleɪʃn̩/	การคำนวณ
chart คำนาม	/t͡ʃɑɹt/	ผัง, แผนผัง
province คำนาม	/ˈpɹɔv.ɪns/	จังหวัด, แขวง
architecture คำนาม	/ˈɑː.kɪˌtɛk.t͡ʃə/	สถาปัตยกรรม
discover คำกริยา	/dɪˈskʌvə/	ค้นพบ

กริยาวลีที่คุณจะได้ยิน

คำศัพท์	ความหมาย
come back คำกริยา	กลับมา

ไวยากรณ์ในวิดีโอนี้

โครงสร้างที่ผู้พูดใช้บ่อยที่สุด พร้อมคำพูดจริงจากวิดีโอ:

โครงสร้าง	ในวิดีโอ
Passive voice be + กริยาช่อง 3 — เน้นสิ่งที่เกิดขึ้น ไม่ใช่ผู้กระทำ	is based · are scattered · is stored
Present perfect have/has + กริยาช่อง 3 — เหตุการณ์ในอดีตที่ยังเกี่ยวข้องกับปัจจุบัน	have joined · have prepared · have understood

การออกเสียงที่ควรระวัง

ผู้พูดใช้รูปย่อและรูปลดเสียง 39 ครั้ง เช่น gonna, you're, they're ให้พูดแบบสั้นตามที่ได้ยิน

เสียง “th”: gather /ˈɡæðɚ/, algorithm /ˈælɡəɹɪðm̩/
เสียง “sh” และ “zh”: prediction /pɹɪˈdɪkʃən/, calculation /ˌkælkjuˈleɪʃn̩/, crucial /ˈkɹuː.ʃəl/, specialist /ˈspɛʃəlɪst/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/
คำยาว — ลงเสียงหนักให้ถูกพยางค์: developer /dɪˈvɛləpɚ/, accessible /əkˈsɛs.ɪ.bəl/, experiment /ɪkˈspɛɹ.ə.mənt/, communicate /kəˈmjuːnɪkeɪt/, manually /ˈmænj(u)əliː/

วิธีฝึกกับวิดีโอนี้

ฟังวิดีโอให้จบหนึ่งรอบโดยยังไม่ต้องพูด แล้วจดคำที่ยังไม่รู้จัก
เริ่มที่ความเร็ว 0.75× พูดตามทีละประโยค แล้วกลับไปใช้ความเร็วปกติเมื่อเริ่มคล่อง
อัดเสียงตัวเองแล้วเทียบกับต้นฉบับ โดยสังเกตคำอย่าง layer, scientist, architect เป็นพิเศษ

เทคนิค Shadowing คืออะไร?

Shadowing เป็นเทคนิคการเรียนรู้ภาษาที่ได้รับการรับรองทางวิทยาศาสตร์ พัฒนาขึ้นสำหรับการฝึกนักแปลมืออาชีพ วิธีการนี้เรียบง่ายแต่ทรงพลัง: คุณฟังเสียงภาษาอังกฤษจากเจ้าของภาษาและพูดตามทันที — เหมือนเงาที่ตามผู้พูดด้วยช่วงเวลาห่าง 1-2 วินาที การวิจัยแสดงว่าเทคนิคนี้ปรับปรุงความแม่นยำในการออกเสียง ทำนองเสียง จังหวะ การเชื่อมเสียง การฟังเข้าใจ และความคล่องแคล่วในการพูดได้อย่างมีนัยสำคัญ

เทคนิค shadowing: อ่านคู่มือฉบับเต็มทีละขั้นตอน →

ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: How Modern Data Teams Work (Engineer, Analyst, Scientist, Architect, ML)

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กริยาวลีที่คุณจะได้ยิน

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การออกเสียงที่ควรระวัง

วิธีฝึกกับวิดีโอนี้

เทคนิค Shadowing คืออะไร?