Luyện nói tiếng Anh bằng Shadowing qua video: How AI is Changing Data Scientist's Workflow

Data scientist is the top job title referencing generative AI.

So that means that companies don't just need data talent.

They need data talent that knows AI.

So what does know AI actually mean?

Well, I found that it's actually twofold.

Firstly, you know how to leverage AI to speed up your existing workflow, not just in a way of prompting ChatGBT to write Python code kind of way,

but it's more about rethinking the way you get things done from generating or collecting data, exploratory data analysis, modeling, reporting, and communication.

Secondly, you know how to build AI systems.

For example, creating and deploying tools and agents to automate part of or an entire process or workflow for your team.

Now, let's first talk about different ways to leverage AI to speed up a data science workflow.

I'll talk about a few specific tools in this video, but I want to be clear that tools isn't the point because tools can change.

What matters is knowing what's now possible so you can think about how to adapt and rethink the way you're working.

So the first major way data scientists can leverage AI is in EDA and data pre-processing.

If you've worked in a real data science project before, you probably already experienced this.

Too much time spent preparing data, not enough time analyzing it.

You got the job thinking you spent all day finding data patterns, coming up with hypothesis and interesting analysis.

In reality, you spent 60 to 70% of your time cleaning data, writing repetitive code to check data types,

handling missing values, or creating standard visualizations.

Here are two open source tools I've come across that can make your work a little bit easier and more fun.

The first one is Pandas AI.

It's a Python library that basically allows you to ask questions to your data frame in natural language.

For example, what's the total sales per coffee type?

Depending on your question, it can return different kinds of responses like a string, a data frame, a chart, or a number.

It's totally open source and you can hook it up with any LLM you like.

And I'm connecting Pandas AI with an LLM from OpenAI here.

Now, if you're wondering if your whole data set would be sent to an LLM or not, the answer is no. When you ask Pandas AI a question, it actually sends just enough context.

So your question along with the column names, a few sample rows, and some metadata.

The LLM reads that context

and writes a small Python script to answer your question pandas AI then checks

that the script is safe to run

and then it runs the script locally on your actual data using pandas

so if you want to get away with writing pandas

and matplotlib code yourself you can use this library to do quick analysis data cleaning

and data manipulation but for more complex tasks like joining data frames

or creating correlation matrix you should still use pandas the second tool

that I think is pretty cool is data formulator this one comes out of microsoft research

and the easiest way to think about it is the combination of excel formulas power query

and modern ai it also allows you to interact with your data in natural language

but the nice part is the tool shows you exactly how the data is being transformed

and visualized step by step nothing hidden which means the workflow stays transparent and reusable.

Give it a broad prompt like show me interesting trends in this data set and we'll start transforming the data,

generating visualizations and suggesting directions you can explore further.

This tool is also open source and you can install it locally.

So in your workflow if you happen to spend a lot of time in the Microsoft ecosystem like Excel, Power Query, Power BI, it's definitely worth experimenting with this tool.

Now with these kind of tools you still need to understand your data

but they can drastically speed up the exploration phase where you're

just trying to understand what you're working with before you get into building the actual pipeline

and the real modeling work now as a top one percent data scientist you don't just limit yourself to tools

that are already available you also know how to build ai tools

and systems to support your team

so i want to quickly shout out to datacamp who sponsors this part of the video I used Datacam back

when I was learning Python for work.

What I really like is that it's hands-on.

You actually write code, solve exercises, and build projects right in your browser.

They're offering some solid tracks for data scientists to level up your skills.

The first one I'd recommend is the Associate AI Engineer for Data Scientist track that covers scikit-learn, PyTorch, Hugging Face, building LLM apps with Lanchain,

and the MLObs basics to help you take models into production.

If you're more on application building side, their associate AI engineer for developers track covers the more practical stack so OpenAI API,

prompt engineering, embeddings, PyCon and LangChain.

They also offer an AI engineer for data scientist certification if you want a credential you can put on LinkedIn.

If you want to level up your skills in AI with these tracks check the links in the description.

Okay the second way data scientists can leverage AI is creating synthetic data sets for prototyping.

Getting access to real and high quality data is the problem every data scientist has faced.

Probably the data is locked behind privacy constraints, doesn't exist yet, or you just want to test an idea quickly.

So instead of waiting, many data scientists use AI to create synthetic data sets, whether they are structured or unstructured data.

LLMs have become so good that they can actually generate artificial data that mimics real world patterns. In fact,

synthetic data is widely used in privacy sensitive industries like healthcare

finance where real data can't always be shared freely for example

i used claw to generate this whole e-commerce data set of

5 000 rows with customers products order items you just describe what you need the structure the number of rows

and it gives you something you can actually work with in

minutes similarly you can also generate unstructured text data for testing

a model training pipeline now here's another interesting way you can use ai

that a lot of data scientists are sleeping on

that is using foundation models foundation models those are trained on massive

and varied data sets they are different from traditional machine learning models

that are custom built for specific tasks today foundation models are no longer just for chatbots

and image generation they're starting to show up in core data science work like time series forecasting tabular prediction

and even recommendation systems let's say you're building a time series forecasting model you usually collect your data

and clean it and engineer your features and then pick a model

and train it and fine-tune it and evaluate it now the problem is

if your company wants forecasts across 200 product lines you would normally have to repeat

that process over and over but nowadays you can use foundation models like chronos by amazon

and time gpt

so here's an example where i tried one of those models you just feed your data

and get a forecast no feature engineering no model selection no

training loop this approach is really changing the whole modeling workflow

netflix recently replaced a whole stack of separate recommendation models with one foundation model trained on billions of user interactions

and now every team just fine-tunes on top of it the

data scientists pulling ahead right now are the ones who know

when to build from scratch and when to build on top of something

that already works next let's talk about how a data scientist

can automate workflows with mcp model context protocol think about your

typical day you're jumping between five different apps you're querying databases

pushing code to github sharing updates on slack pulling some data

that your colleague shared on google drive that constant back

and forth burns your energy so the idea of using mcp is

that it lets you connect all of those tools to an ai assistant like cloud

so instead of switching between apps you're doing everything from one

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place for example i connected cloud desktop with my postgres sql database through mcp

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and now instead of opening a sql client writing a query

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running it copying results i just ask cloud for example show me the top 10 customers my revenue this month

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and i get my answer right there.

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Now, quick heads up, not every employer allows tools like Claude Desktop or Claude Code due to security policies.

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But even if you can't use it at work yet, it's useful, I think, to exploit personally because I think sooner or later,

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this kind of integrated tools will become the standard because it removes a lot of friction in the day-to-day work.

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Now, beyond just using AI tools to speed up your work

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the data scientists who are really standing out right now are

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the ones who can actually build things with AI systems

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that solve real business problems for their teams

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and companies from my conversations with all of my data scientist friends their jobs are less

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and less about prototyping a model in a notebook it's more

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and more about delivering something people can actually use for example

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imagine your finance team processes hundreds of invoices every week manually you could build an AI system

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that read those invoices extracts the key information

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and saves it straight to your database

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or say your company wants to understand patterns across thousands of customer interactions

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or research documents you could build a knowledge graph

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that maps out how everything connects that way you uncover insights

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that are impossible to find with traditional analysis methods i also

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have a tutorial on this i'll link here somewhere in the screen these are the kinds of projects

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that make you invaluable at a company these days also there's a real gap

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that most data scientists need to close right now myself included

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a lot of data scientists can build a great prototype

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but they can't get into production they can't deploy it monitor it

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or make it reliable enough for other people to depend on

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and to close

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that gap you need to start learning things like how to containerize your application with docker

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so it runs anywhere how to deploy and serve your models on cloud platforms

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and how to monitor

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and maintain ai systems once they're alive these skills are more engineering skills ai engineering specifically

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but they're becoming essential for data scientists as well

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if you want to help your team build ai systems now we've talked about a lot of different tools

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and hard skills but i think eventually data professionals are increasingly about judgment

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and influence understanding how business works how your company makes money domain knowledge stakeholder management, trust building, communication, and data storytelling,

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these soft skills in the long term are becoming the core of what you do.

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Right now, I think one thing that's going to pay off dividends for you is to stay open to trying out new tools, learning them, using them, questioning them, adapting them for your own needs.

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I run a free newsletter where I share my latest insights and experiments in data science and AI.

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So if you're interested, check it out in the description below.

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Thank you for watching.

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Bye-bye.

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Từ vựng và ghi chú luyện nói cho bài học này

Bài luyện nói trình độ C1 này dựa trên video “How AI is Changing Data Scientist's Workflow”. Những từ được nhắc lại nhiều nhất trong bài: tool, scientist, model, build. Video này có 141 câu và 1957 từ để luyện shadowing. Phần lời nói dài 10:46. Người nói nói nhanh, khoảng 182 từ mỗi phút, nên sẽ có nhiều chỗ nối âm và nuốt âm. Chỉ 81% số từ nằm trong 3.000 từ tiếng Anh thông dụng nhất, nên từ vựng khá khó.

Từ vựng quan trọng trong video

15 từ khó nhất trong video, kèm phiên âm và nghĩa:

Từ	Phiên âm	Nghĩa
forecast danh từ	/ˈfɔːkɑːst/	dự báo, dự đoán
prototype danh từ	/ˈpɹəʊtətaɪp/	nguyên mẫu
invoice danh từ	/ˈɪnˌvɔɪs/	hóa đơn
rethink động từ	/ɹiːˈθɪŋk/	nghĩ lại
desktop danh từ	/ˈdɛsktɒp/	máy tính để bàn
stack danh từ	/stæk/	chồng
recommendation danh từ	/ˌɹɛkəmɛnˈdeɪʃən/	sự giới thiệu, sự tiến cử
credential danh từ	/kɹɪˈdɛnʃəl/	giấy chứng nhận
metadata danh từ	/ˈmɛt.əˌdeɪ.tə/	siêu dữ liệu
claw danh từ	/klɔ/	móng, vuốt
friction danh từ	/ˈfɹɪkʃən/	ma sát
firstly trạng từ	/ˈfɜɹstli/	trước hết, trước nhất
notebook danh từ	/ˈnoʊtˌbʊk/	tập, vở
newsletter danh từ	/ˈn(j)uzˌlɛtɚ/	bản tin
ecosystem danh từ	/ˈikoʊˌsɪstəm/	hệ sinh thái

Ngữ pháp trong video

Những cấu trúc người nói dùng nhiều nhất, kèm đúng cụm từ trong video:

Cấu trúc	Trong video
Câu bị động be + quá khứ phân từ — nhấn vào việc xảy ra, không phải người làm	would be sent · being transformed · is locked
Thì hiện tại hoàn thành have/has + quá khứ phân từ — việc đã xảy ra nhưng còn liên quan đến hiện tại	you've worked · I've come · has faced
Mệnh đề quan hệ who / which + mệnh đề — thêm thông tin về người hoặc vật	hidden which means · ones who know · ones who can

Phát âm cần chú ý

Người nói dùng 30 dạng rút gọn, ví dụ you're, can't, they're. Hãy nói theo dạng ngắn đúng như bạn nghe.

Âm “th”: synthetic /sɪnˈθɛtɪk/, rethink /ɹiːˈθɪŋk/, hypothesis /haɪˈpɒθɪsɪs/
Âm “sh” và “zh”: visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, credential /kɹɪˈdɛnʃəl/, visualize /ˈvɪʒuəˌlaɪz/, friction /ˈfɹɪkʃən/
Từ dài — đặt trọng âm cho đúng: visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, exploratory /ɛkˈsplɒɹ.ə.tə.ɹi/, generative /ˈd͡ʒɛnəɹətɪv/, metadata /ˈmɛt.əˌdeɪ.tə/

Những âm người Việt thường gặp khó:

Cụm phụ âm cuối — đọc đủ từng âm, đừng nuốt âm cuối: forecast /ˈfɔːkɑːst/, prompt /pɹɑmpt/, rethink /ɹiːˈθɪŋk/, adapt /əˈdæpt/, transform /tɹænsˈfɔɹm/
Âm /tʃ/, /dʒ/ và /ʒ/ — tiếng Việt không có, đừng đọc thành “ch” hay “gi”: leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/, chatbot /ˈtʃætbɑt/, generative /ˈd͡ʒɛnəɹətɪv/, visualize /ˈvɪʒuəˌlaɪz/
Âm /æ/ — mở miệng rộng hơn âm “e”: adapt /əˈdæpt/, transform /tɹænsˈfɔɹm/, stack /stæk/, chatbot /ˈtʃætbɑt/, tabular /ˈtæbjʊləʴ/

Cách luyện với video này

Nghe hết video một lần, chưa cần nói, và ghi lại những từ bạn chưa biết.
Bắt đầu ở tốc độ 0,75×, nói đuổi từng câu, rồi quay lại tốc độ bình thường khi đã quen.
Ghi âm giọng mình rồi so với bản gốc, chú ý các từ như forecast, prototype, invoice.

Phương Pháp Shadowing Là Gì?

Shadowing là kỹ thuật học ngôn ngữ có cơ sở khoa học, ban đầu được phát triển cho chương trình đào tạo phiên dịch viên chuyên nghiệp và được phổ biến rộng rãi bởi nhà đa ngôn ngữ học Dr. Alexander Arguelles. Nguyên lý cốt lõi đơn giản nhưng cực kỳ hiệu quả: bạn nghe tiếng Anh của người bản xứ và lặp lại to ngay lập tức — như một "cái bóng" (shadow) đuổi theo người nói với độ trễ chỉ 1–2 giây. Khác với luyện ngữ pháp hay học từ vựng bị động, Shadowing buộc não bộ và cơ miệng phải đồng thời xử lý và tái tạo ngôn ngữ thực tế. Các nghiên cứu khoa học xác nhận phương pháp này cải thiện đáng kể phát âm, ngữ điệu, nhịp điệu, nối âm, kỹ năng nghe và độ lưu loát khi nói — đặc biệt hiệu quả cho người luyện IELTS Speaking và muốn giao tiếp tiếng Anh tự nhiên như người bản ngữ.

Phương pháp shadowing: đọc hướng dẫn từng bước đầy đủ →

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Từ vựng và ghi chú luyện nói cho bài học này

Từ vựng quan trọng trong video

Ngữ pháp trong video

Phát âm cần chú ý

Cách luyện với video này

Phương Pháp Shadowing Là Gì?