Luyện nói tiếng Anh bằng Shadowing qua video: How I use ChatGPT as a Data Analyst

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Data nerds!

I've been using ChatGPT to analyze data for my job.

And it's been saving me a lot of time.

Let me show you what I mean.

So here we have a spreadsheet with some data we want to analyze.

First, I asked it to connect to the data by providing it with a link to the spreadsheet.

It gives me the first few reads of the data so I know it's connected.

So next, I ask it, what's this dataset even about?

And it looks like it's a bunch of job postings specifically for the role of data analyst.

It even provides some basic stats about the columns in the spreadsheet.

Let's do some quick EDA to see what are the 10 most common jobs.

And it provides me this bar graph with data analyst as the most common job title, followed by senior data analysts.

Let's wrap this analysis up and check out what the median salary is for these 10 most common jobs.

With this calculation, let's actually time it.

And in just over a minute, we're able to generate both these visualizations.

You can see that data scientists have some of the highest, while data analysts have some of the lowest.

I don't like this.

So I did this entire analysis before filming this without ChatGPT, and it took me almost an hour.

And here, in less than a few minutes, we've generated all of these different visualizations and insights without even coding one bit.

Now that we've shown ChatGPT's capabilities, let's move on to an even bigger problem that we're going to solve.

All right, so I stayed up a little bit too late last night.

How's my audio?

My audio good?

And I put together this PowerPoint of how we're going to solve this problem

and there's a lot of hype around AI lately

and whether it's going to actually automate and take away jobs and is it going to take over the world

probably but more importantly we want to focus on how is AI impacting data jobs

and we'll look at two main aspects first from the people's aspect from the person

that has the job how satisfied are they with AI impacting

their jobs the next one is businesses is there going to be an actual impact such as financial

or they're going to be actually be looking for those with skills of this AI

and how we're going to accomplish this all well here's the game plan

I have a two -step approach the first is using chat gpt for data research

we're going to be using different plugins

and internet searching methods in order to find research papers data whatever

it may be in order to find insights into those two main areas

that we're trying to look at from there we're going to move into data analysis

and we're going to once again use chat gpt to actually analyze this data

that we found in order to find insights and maybe prove one way

or another whether how bad ai is impacting data science jobs

but before we just dive into this analysis we need to understand the capabilities of this tool chat gbt

so um brb so what the heck is chat gpt well i asked it

and come to find out it's an artificial intelligence that's running on one of the models from OpenAI.

Specifically, this one right here, I'm using model GPT -4, which is their newest and greatest one.

And in a recent survey from Stack Overflow, over 50 ,000 respondents reported that they prefer ChatGPT over any other AI chatbot at the moment.

I've also tried all these other options and done a cold comparison video,

and I've found that ChatGPT is the best at performing data analysis at the moment.

But there is a catch with ChatGPT, and that comes to pricing.

So you can use the free version of ChatGPT and go in and prompt it and get different insights from it,

but you're really going to be limited in what you get.

Now, if you go with the pro version, not only do you have access to an improved model of GPT -4,

but also you have access to plugins.

And plugins are really the money.

Plugins allow these chatbots to connect to different applications.

So recently I've been using it with Wolfram in order to connect

and do analysis on the backend and then feed it back into ChatGPT so I can get my numbers crunched.

All right, enough with this green screen break.

Let's get back to the video.

All right, I made some progress on using ChatGPT to go

and search the internet to find things around AI impacting data science jobs.

And first up is one of the limitations that I found with ChatGPT,

and that's that the model itself was only trained on data up to September of 2021.

This has been a really big problem

because you can't actually ask the model directly about things that have happened right now because it won't know.

That's where plugins come in.

The first main one that I've been using to search the internet is their Bing plugin

that allows it to go in and search and find different articles, research papers, and publications, and I can find out what's most impacting.

Now, ChatGPT isn't really that great.

I also tested out

Bing and also the Google competitor, Bard, and they're not really search engines, right?

So I still rely on Google itself to go in and search, but I was able to find some recent articles on this,

specifically this one from McKinsey talking about the economic impact of generative AI.

Now this bad boy is almost a hundred pages long.

And if you think I'm going to go through and read it, you're probably wrong.

So what's good about ChatGPT is they have plugins in order to read these type of PDFs.

I just give it the link or the.. pdf itself

and it will go in and actually read it summarize it

and provide me with insights now generative ai has to deal with ais

that produce new content so basically what chat chpt is anyway it went through

and found this pretty interesting stat on generative ai and it showed

that it had really high potential for automating data related tasks specifically around processing and collecting data.

I mean, this is all sort of meta, right?

I'm using ChatGPT to analyze this PDF and provide data insights to me on data insights of generative AI.

So pretty crazy.

Another key source of information was this website right here from Stack Overflow,

where they actually surveyed a bunch of developers around 90 ,000 on what their sentiment was towards AI.

One of the key findings from this was

that 77 % of those surveyed had a favorable opinion towards using AI in their workflow.

That's four out of five.

That's pretty high considering how much negativity you were towards AI recently.

Now, the results of this report are pretty damn long.

And so once again, I can use ChatGBT to go in

and analyze it and actually asked it to summarize it better around data science professionals

and how they felt AI was impacting their job.

And ChatGPT was able to point out a pretty interesting insight.

Compared to developers, data analysts and scientists and engineers had some of the most positive sentiment towards this technology.

Now, the document itself has a lot of numbers around how these different job titles feel around AI.

So I asked it to analyze these numbers, provide a comparison.

But I ran into some issues where it started hallucinating

100

and making up a lot of the values that it saw in this document right here.

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So I was really confused with this, especially since I had provided it with the source document.

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I don't know why I was fabricating these numbers.

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I couldn't figure that out.

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But anyway, nonetheless, I was able to find the actual table that it was referring to.

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And with another plugin, I was actually able to go in,

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pull these numbers out, and then visualize it in order to see how it compared to other developers.

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All right, we're back to the green screen.

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So we discovered that data nerds have some of the most favorable opinions about using AI tools in their job.

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And next slide.

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We also, this also satisfies what we needed here in understanding what people's thoughts were towards this technology.

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And overall, what we're seeing, four out of five prefer.

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I mean, I'd say that that's a pretty good score.

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And frankly, I think this is good.

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But what about this side for the businesses?

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What are their thoughts about it?

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I mean, they're the ones

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that are actually going to be driving this type of change within organizations and ensure people are adapting to this new technology.

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The McKinsey study that I referenced at the beginning of my research

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basically confirmed that these AI tools are going to increase our economic output.

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So it's a win -win situation for companies to actually utilize this for their employees.

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Yes, I do understand that there's different privacy concerns around what can be put into these type of tools,

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but that's something that we need to start thinking about now.

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So with the research we did with ChatGPT, we weren't able to find any data that related to how companies are implementing these AI tools.

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But I have an idea.

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So I've been collecting job postings since last year, and I have more details about it in this video right here.

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Anyway, I've collected over a million jobs for data science job postings.

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This should show indirectly how companies feel about implementing these AI tools, whether they're even asking new employees to have these skills.

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Now, this data set includes things like job title, salary, and even job description.

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So what we're going to do is we're going to use ChatGPT to perform some NLP

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and go in and extract out any data related to AI being used in these jobs.

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So let's get into it.

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All right, it's been a few days and I've made a lot of progress using ChatGPT and specifically using the Notable plugin.

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And not only is it providing visualizations through the front end, but you can also see the code behind how this all worked.

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And that's pretty cool that you can double check it with this.

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One quick note is that ChatGPT will be releasing the Code Interpreter plugin in the near future.

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And this plugin will be as powerful or even more powerful than this Notable plugin.

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So getting into the analysis, All of the data is located in a database that has a secure connection.

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So I asked ChatGPT how do I go about giving these credentials so that way it can access this data.

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And at first, it tried to give me code to perform this, but I re -prompted and told it to use the installed plugin.

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And with that, it gave me a link that I could input in the credentials of the database.

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So to make sure that it was connected to the database, I asked it how many entries it had in this table,

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which it told me it was about a million, so I knew we were connected.

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Next, I prompted ChatGPT to provide me some descriptive statistics about the data set.

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And it not only provided information about what columns were in the data set, but it also showed some statistics around the salary information.

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I'm not gonna really use that right now, but I thought it was interesting that I provided it nonetheless.

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Next, I wanted to get into analyzing these job postings for keywords around AI.

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So I prompted ChatGPT for a list of keywords related to this.

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And although good, it's not really what I wanted, as these are more theoretical terms and not necessarily tools.

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I've analyzed terms like AI and artificial intelligence before, and I didn't really think the insights from those keywords were really

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that great instead i want to use tools

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that relate to af for this we're going to be using those tools

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that were included in

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that stack overflow survey not only are we going to focus on search tools

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but also on developer tools so i gave chat gpt this list of tools

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and ask it to analyze whether these keywords were included in the job descriptions

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this is where i ran into a limitation of this notable plugin

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because this data set is over a million rows

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and tried to extract those million rows of data and import it into a pandas data frame within the notebook.

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I did find an option to upgrade my small ass computer, but it looked like it was behind a wait list.

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So I reached out to Notable on Twitter and asked them if I could access these larger computers.

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And they told me to wait.

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So we're only limited around 100 ,000 jobs for this, and so we used a random sampling for this analysis.

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And double checking all the results that I got at the end of this, it doesn't look like the results really change or fluctuate that much.

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So I think we're fine with using this sampling.

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So the first thing I jumped into was telling ChatGPT that each one of these rows was a job posting.

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And I was curious to find out what percentage of these jobs contain one of those tools.

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And less than 2 % of jobs is even requesting the skill of AI tools in the job description.

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So we're not really off to a good start.

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I thought next, let's actually look into what are the actual tools that are appearing in those job descriptions.

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And I had to reprompt ChatGPT several times, getting it to sort highest to low, displaying the y -axis as a percentage,

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and then formatting it to include one decimal place.

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But eventually, we got this visualization, which shows that

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Bing and u .com are some of the highest requested tools.

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I am a little confused by these results, as they don't really correlate really well to that

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Stack Overflow survey, but they are less than 1%, so maybe this does make sense.

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So next, I was curious, with this low percentage,

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are they even paying those with these type of skills a higher salary once again had to reprompt

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it to correct column names and also format the y -axis as a currency

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and we finally get this showing data scientists do get paid more

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but data engineers machine learning engineers and data analysts don't

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and such a low percentage of these jobs including these search terms

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i'm not ready to call anything conclusive off of this visualization just think

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that we maybe should explore this more in the future

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now i did want to get into one last thing looking

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at how these different job titles compared to each other for containing these AI tools.

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And it looks like machine learning engineers are leading the way, followed by data analysts and data scientists.

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So with these results, I think we've shown pretty conclusively

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that companies aren't caring as much as people are about implementing AI tools in their jobs.

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And I think this is a pretty big problem,

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that there's such a big disparity between those that want to actually implement AI tools in their jobs,

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and then the companies not really requesting it in these jobs.

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Anyway, I digress.

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So, if you got value out of this video and you like AI, smash that like button.

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If you don't like AI, like those companies, hit that dislike button.

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I'm curious to know.

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Anyway, I'll see you in the next one.

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73 câu

Từ vựng và ghi chú luyện nói cho bài học này

Bài luyện nói này dựa trên video “How I use ChatGPT as a Data Analyst”. Những từ được nhắc lại nhiều nhất trong bài: Chatgpt, tool, plugin. Video này có 196 câu và 2646 từ để luyện shadowing. Phần lời nói dài 13:39. Người nói nói nhanh, khoảng 194 từ mỗi phút, nên sẽ có nhiều chỗ nối âm và nuốt âm. 82% số từ nằm trong 3.000 từ tiếng Anh thông dụng nhất; phần còn lại nên xem trước khi luyện.

Từ vựng quan trọng trong video

15 từ đáng học trong video, kèm phiên âm và nghĩa:

Từ	Phiên âm	Nghĩa
insight danh từ	/ˈɪnsaɪ̯t/	sự sáng suốt
analyze động từ	/ˈæn.əˌlaɪz/	phân tích
connect động từ	/kəˈnɛkt/	nối
percentage danh từ	/pə(ɹ)ˈsɛntɪd͡ʒ/	số phần trăm, tỷ lệ phần trăm
salary danh từ	/ˈsæləɹi/	lương
scientist danh từ	/ˈsaɪəntəst/	nhà khoa học, khoa học gia
developer danh từ	/dɪˈvɛləpɚ/	nhà phát triển
implement động từ	/ˈɪmpləmənt/	thực hiện, chấp hành
stack danh từ	/stæk/	chồng
compare động từ	/kəmˈpɛɚ/	so sánh, so
curious tính từ	/ˈkjʊɹ.i.əs/	tò mò
collect động từ	/ˈkɑlɪkt/	thu thập, gom góp
database danh từ	/ˈdeɪtəˌbeɪs/	cơ sở dữ liệu, CSDL
audio tính từ	/ˈɔ.di.oʊ/	âm thanh, âm tần
format danh từ	/ˈfɔːɹ.mæt/	khổ (sách, giấy, bìa)

Cụm động từ bạn sẽ nghe

Từ	Phiên âm	Nghĩa
check out động từ		tính tiền, trả phòng
take away động từ	/ˌteɪk əˈweɪ/	lấy đi, mang đi

Những câu đáng luyện lại

Các câu ngắn, trọn ý trong video mà bạn có thể dùng lại khi giao tiếp hằng ngày:

With this calculation, let's actually time it.
Let's get back to the video.
I don't know why I was fabricating these numbers.
I couldn't figure that out.
All right, we're back to the green screen.

Ngữ pháp trong video

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

Cấu trúc	Trong video
Thì hiện tại hoàn thành tiếp diễn have/has been + -ing — việc bắt đầu từ trước và vẫn đang tiếp tục	I've been using · it's been saving · I've been collecting
Thì hiện tại hoàn thành have/has + quá khứ phân từ — việc đã xảy ra nhưng còn liên quan đến hiện tại	we've generated · we've shown · I've also tried
Câu bị động be + quá khứ phân từ — nhấn vào việc xảy ra, không phải người làm	was only trained · can be put · being used

Phát âm cần chú ý

Người nói dùng 57 dạng rút gọn, ví dụ we're, I've, I'm. Hãy nói theo dạng ngắn đúng như bạn nghe.

Âm “th”: nonetheless /ˌnʌnðəˈlɛs/, theoretical /ˌθi.əˈɹɛt.ɪ.kəl/
Âm “sh” và “zh”: artificial /ˌɑː.tɪˈfɪʃ.əl/, limitation /lɪmɪˈteɪʃən/, publication /ˌpʌblɪˈkeɪʃ(ə)n/, smash /smæʃ/, accomplish /əˈkɑm.plɪʃ/
Từ dài — đặt trọng âm cho đúng: developer /dɪˈvɛləpɚ/, artificial /ˌɑː.tɪˈfɪʃ.əl/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, limitation /lɪmɪˈteɪʃən/, favourable /ˈfeɪv(ə)ɹəbəl/

Những âm người Việt thường gặp khó:

Cụm phụ âm cuối — đọc đủ từng âm, đừng nuốt âm cuối: analyst /ˈænəlɪst/, connect /kəˈnɛkt/, scientist /ˈsaɪəntəst/, implement /ˈɪmpləmənt/, prompt /pɹɑmpt/
Âm /tʃ/, /dʒ/ và /ʒ/ — tiếng Việt không có, đừng đọc thành “ch” hay “gi”: percentage /pə(ɹ)ˈsɛntɪd͡ʒ/, generate /ˈd͡ʒɛn.ə.ɹeɪt/, crunch /kɹʌnt͡ʃ/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/, generative /ˈd͡ʒɛnəɹətɪv/
Âm /æ/ — mở miệng rộng hơn âm “e”: analyze /ˈæn.əˌlaɪz/, analyst /ˈænəlɪst/, salary /ˈsæləɹi/, stack /stæk/, format /ˈfɔːɹ.mæt/

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 insight, analyze, connect.

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 →

Luyện nói tiếng Anh bằng Shadowing qua video: How I use ChatGPT as a Data Analyst

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