跟读练习: How AI is Changing Data Scientist's Workflow - 通过视频学习英语口语
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Data scientist is the top job title referencing generative AI.
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So that means that companies don't just need data talent.
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They need data talent that knows AI.
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So what does know AI actually mean?
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Well, I found that it's actually twofold.
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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,
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but it's more about rethinking the way you get things done from generating or collecting data, exploratory data analysis, modeling, reporting, and communication.
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Secondly, you know how to build AI systems.
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For example, creating and deploying tools and agents to automate part of or an entire process or workflow for your team.
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Now, let's first talk about different ways to leverage AI to speed up a data science workflow.
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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.
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What matters is knowing what's now possible so you can think about how to adapt and rethink the way you're working.
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So the first major way data scientists can leverage AI is in EDA and data pre-processing.
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If you've worked in a real data science project before, you probably already experienced this.
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Too much time spent preparing data, not enough time analyzing it.
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You got the job thinking you spent all day finding data patterns, coming up with hypothesis and interesting analysis.
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In reality, you spent 60 to 70% of your time cleaning data, writing repetitive code to check data types,
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handling missing values, or creating standard visualizations.
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Here are two open source tools I've come across that can make your work a little bit easier and more fun.
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The first one is Pandas AI.
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It's a Python library that basically allows you to ask questions to your data frame in natural language.
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For example, what's the total sales per coffee type?
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Depending on your question, it can return different kinds of responses like a string, a data frame, a chart, or a number.
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It's totally open source and you can hook it up with any LLM you like.
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And I'm connecting Pandas AI with an LLM from OpenAI here.
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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.
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So your question along with the column names, a few sample rows, and some metadata.
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The LLM reads that context
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and writes a small Python script to answer your question pandas AI then checks
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that the script is safe to run
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and then it runs the script locally on your actual data using pandas
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so if you want to get away with writing pandas
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and matplotlib code yourself you can use this library to do quick analysis data cleaning
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and data manipulation but for more complex tasks like joining data frames
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or creating correlation matrix you should still use pandas the second tool
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that I think is pretty cool is data formulator this one comes out of microsoft research
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and the easiest way to think about it is the combination of excel formulas power query
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and modern ai it also allows you to interact with your data in natural language
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but the nice part is the tool shows you exactly how the data is being transformed
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and visualized step by step nothing hidden which means the workflow stays transparent and reusable.
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Give it a broad prompt like show me interesting trends in this data set and we'll start transforming the data,
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generating visualizations and suggesting directions you can explore further.
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This tool is also open source and you can install it locally.
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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.
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Now with these kind of tools you still need to understand your data
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but they can drastically speed up the exploration phase where you're
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just trying to understand what you're working with before you get into building the actual pipeline
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and the real modeling work now as a top one percent data scientist you don't just limit yourself to tools
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that are already available you also know how to build ai tools
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and systems to support your team
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so i want to quickly shout out to datacamp who sponsors this part of the video I used Datacam back
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when I was learning Python for work.
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What I really like is that it's hands-on.
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You actually write code, solve exercises, and build projects right in your browser.
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They're offering some solid tracks for data scientists to level up your skills.
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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,
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and the MLObs basics to help you take models into production.
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If you're more on application building side, their associate AI engineer for developers track covers the more practical stack so OpenAI API,
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prompt engineering, embeddings, PyCon and LangChain.
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They also offer an AI engineer for data scientist certification if you want a credential you can put on LinkedIn.
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If you want to level up your skills in AI with these tracks check the links in the description.
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Okay the second way data scientists can leverage AI is creating synthetic data sets for prototyping.
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Getting access to real and high quality data is the problem every data scientist has faced.
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Probably the data is locked behind privacy constraints, doesn't exist yet, or you just want to test an idea quickly.
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So instead of waiting, many data scientists use AI to create synthetic data sets, whether they are structured or unstructured data.
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LLMs have become so good that they can actually generate artificial data that mimics real world patterns. In fact,
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synthetic data is widely used in privacy sensitive industries like healthcare
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finance where real data can't always be shared freely for example
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i used claw to generate this whole e-commerce data set of
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5 000 rows with customers products order items you just describe what you need the structure the number of rows
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and it gives you something you can actually work with in
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minutes similarly you can also generate unstructured text data for testing
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a model training pipeline now here's another interesting way you can use ai
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that a lot of data scientists are sleeping on
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that is using foundation models foundation models those are trained on massive
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and varied data sets they are different from traditional machine learning models
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that are custom built for specific tasks today foundation models are no longer just for chatbots
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and image generation they're starting to show up in core data science work like time series forecasting tabular prediction
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and even recommendation systems let's say you're building a time series forecasting model you usually collect your data
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and clean it and engineer your features and then pick a model
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and train it and fine-tune it and evaluate it now the problem is
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if your company wants forecasts across 200 product lines you would normally have to repeat
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that process over and over but nowadays you can use foundation models like chronos by amazon
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and time gpt
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so here's an example where i tried one of those models you just feed your data
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and get a forecast no feature engineering no model selection no
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training loop this approach is really changing the whole modeling workflow
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netflix recently replaced a whole stack of separate recommendation models with one foundation model trained on billions of user interactions
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and now every team just fine-tunes on top of it the
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data scientists pulling ahead right now are the ones who know
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when to build from scratch and when to build on top of something
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that already works next let's talk about how a data scientist
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can automate workflows with mcp model context protocol think about your
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typical day you're jumping between five different apps you're querying databases
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pushing code to github sharing updates on slack pulling some data
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that your colleague shared on google drive that constant back
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and forth burns your energy so the idea of using mcp is
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that it lets you connect all of those tools to an ai assistant like cloud
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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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本课的词汇与口语要点
这节 C1 级别的口语课以视频“How AI is Changing Data Scientist's Workflow”为素材。 视频中反复出现的词有：tool, scientist, model, build。 这段视频共有 141 个句子、1957 个单词可供跟读。 讲话部分时长为 10:46。 说话人语速较快，每分钟约 182 个词，会出现较多连读和弱读。 只有 81% 的单词属于英语最常用的 3,000 词，词汇难度较高。
视频中的重点词汇
视频中最难的 15 个单词，附发音和释义：
| 单词 | 发音 | 释义 |
|---|---|---|
| workflow 名词 | /ˈwɝkfloʊ/ | 工作流 |
| query 名词 | /ˈkwɪɹ.i/ | 問題 /问题, 質問 /质问 |
| leverage 名词 | /ˈlɛv.(ə.)ɹɪd͡ʒ/ | 槓桿 /杠杆 |
| forecast 名词 | /ˈfɔːkɑːst/ | 預報 /预报, 預測 /预测 |
| deploy 动词 | /dɪˈplɔɪ/ | 部署 |
| prototype 名词 | /ˈpɹəʊtətaɪp/ | 原型, 雏形 |
| automate 动词 | /ˈɔ.təˌmeɪt/ | 自動化 /自动化 |
| invoice 名词 | /ˈɪnˌvɔɪs/ | 發票 /发票, 發單 /发单 |
| rethink 动词 | /ɹiːˈθɪŋk/ | 再想, 重想 |
| visualization 名词 | /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/ | 可視化 /可视化, 視覺化 /视觉化 |
| excel 动词 | /ɪkˈsɛl/ | 勝過 /胜过, 優於 /优于 |
| desktop 名词 | /ˈdɛsktɒp/ | 桌面 |
| adapt 动词 | /əˈdæpt/ | 適應 /适应 |
| transform 动词 | /tɹænsˈfɔɹm/ | 變換 /变换, 變形 /变形 |
| pipeline 名词 | /ˈpaɪpˌlaɪn/ | 管道, 管線 /管线 |
视频中出现的短语动词
| 单词 | 发音 | 释义 |
|---|---|---|
| speed up 动词 | /spiːdˈʌp/ | 加速, 加快 |
| come up with 动词 | 想出, 提出 | |
| try out 动词 | 試用 /试用 |
视频中的语法
说话人最常用的结构，并附上视频中的原话：
| 结构 | 视频中的用法 |
|---|---|
| 被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的 | would be sent · being transformed · is locked |
| 现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事 | you've worked · I've come · has faced |
| 定语从句 who / which + 从句 — 补充说明人或事物 | hidden which means · ones who know · ones who can |
需要注意的发音
说话人用了 30 次缩略和弱读形式，例如 you're, can't, they're。请按听到的简短形式来说。
- “th” 音: synthetic /sɪnˈθɛtɪk/, rethink /ɹiːˈθɪŋk/, hypothesis /haɪˈpɒθɪsɪs/
- “sh” 和 “zh” 音: visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, credential /kɹɪˈdɛnʃəl/, visualize /ˈvɪʒuəˌlaɪz/, friction /ˈfɹɪkʃən/
- 长单词——注意重音位置: visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, exploratory /ɛkˈsplɒɹ.ə.tə.ɹi/, generative /ˈd͡ʒɛnəɹətɪv/, metadata /ˈmɛt.əˌdeɪ.tə/
中文母语者容易读错的音：
- /v/ — 不要读成 /w/，上齿轻触下唇: leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/, invoice /ˈɪnˌvɔɪs/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/, generative /ˈd͡ʒɛnəɹətɪv/, uncover /ʌnˈkʌvɚ/
- 词尾辅音 — 要读清楚，后面不要加元音: leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/, forecast /ˈfɔːkɑːst/, prototype /ˈpɹəʊtətaɪp/, prompt /pɹɑmpt/, synthetic /sɪnˈθɛtɪk/
- /r/ 和 /l/ 的区别: leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/, credential /kɹɪˈdɛnʃəl/, exploratory /ɛkˈsplɒɹ.ə.tə.ɹi/, firstly /ˈfɜɹstli/, tutorial /ˌtjuːˈtɔːɹɪəl/
如何用这段视频练习
- 先完整听一遍视频，不要开口，记下不认识的单词。
- 先用 0.75 倍速逐句跟读，熟练之后再回到正常速度。
- 录下自己的声音并与原声对比，特别注意 workflow, query, leverage 这类单词。
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。