---
# System prepended metadata

title: career fair 就業博覽會

---


## Dialog1
- Person A: Hey, everyone! Welcome to today's Data Science meeting. Let's kick things off by talking about our latest data analysis project. Anybody got any updates to share?

- Person B: We've finished digging into that new dataset and found some cool trends. We're digging deeper to find the real insights.

- Person C: Now, about building models, what's our next move? Are we ready to start training predictive models?

- Person D: Yup, we've collected enough data and cleaned it up. We're good to go with model training. My suggestion is to start with machine learning models and then think about diving into deep learning.

- Person A: Awesome! We'll schedule a separate meeting to get into the detail of the models and the training process. Also, let's finalize our plans for data visualization and reporting.

- Person E: For data visualization, we can create interactive dashboards to show our results. It will make the data more user-friendly.

- Person F: Speaking of reporting, we should make sure our reports are easy to understand and get our insights across clearly and precisely

- Person A: Great ideas, folks! We'll keep tabs on these tasks. Anything else on the agenda?


## 片語
- career fair, job fair 就業博覽會
    - fair 與 Exhibition 不同 Exhibition 展覽 有些買賣 can have a look
- Business English
- opening position
    - the opening position is with high qulification/ hight standard
- terms
    - terminology => 術語的縮寫
- get into
    -  是一個多義詞組，具體的意思取決於上下文。以下是一些可能的解釋：
        - 進入或穿越（入口、車輛、建築物等）：例如，"Let's get into the car  and go to the store."（讓我們上車去商店。）
        - 開始投入或參與（活動、工作等）：例如，"She decided to get into gardening as a hobby."（她決定開始從事園藝作為一個愛好。）
        - 深入研究或討論（主題、細節等）：例如，"We need to get into the details of this project."（我們需要深入研究這個項目的細節。）
        - 開始從事（某種活動、職業等）：例如，"He wanted to get into the field of artificial intelligence."（他想要進入人工智慧領域。）
        - 陷入（某種狀況、困難等）：例如，"She got into trouble with her teacher for not doing her homework."（她因沒有做作業而得罪了老師。）
- kick off 
    - to start to begin with
- dig in to 
    - spending time in something dedicate my time
- insights 
    - content informating inside
- good to go with/to(但太多to了) + N 
    - is a phrase used to describe something that complements or pairs well with something else. It indicates that one thing is a suitable match or combination for another. Here are a few examples:
        - "This sauce is good to go with pasta, adding a delicious flavor to the dish."
        - "The blue tie is good to go with your gray suit; it's a great color combination."
        - "The hiking boots are good to go with the rugged terrain you'll be exploring on your trip."
        - "Fresh strawberries are good to go with chocolate, creating a classic dessert."
        - "Her skills in marketing make her good to go with our new advertising campaign."
- dive into
    -  just like dig into
    - **Dive into** is a common English idiom that means to begin or start something with great enthusiasm and without hesitation. It's often used to describe a person's eagerness to engage in an activity or to become deeply involved in a subject. Here are some examples of its usage:
        1. "I can't wait to **dive into** this new novel I just bought."
        2. "He decided to **dive into** the world of computer programming and learn as much as he could."
        3. "The team was excited to **dive into** the project and get started on the research."
        4. "When you travel to a new country, it's a great opportunity to **dive into** the local culture and try new foods."
        5. "The teacher encouraged her students to **dive into** their studies and explore their interests."

- finalize 
    - After weeks of discussions and negotiations, the team will meet tomorrow to finalize the contract details.

## 介系詞

- 介系詞後可以加
    1. **Nouns**: Things or people, like "on the table" (table is a noun).
    2. **Pronouns**: Words like "him" or "her" (he, she).
    3. **-ing Verbs**: Actions with "ing," like "before eating."
    4. **Adjectives**: Words that describe things, like "interested in science" (interested is an adjective).
    5. **Adverbs**: Words that describe actions or qualities, like "with great care" (great is an adverb).
    6. **Phrasal Verbs**: Special verb phrases, like "look into" or "run out of."
    7. **Clauses**: Groups of words with a subject and verb, like "because of what he said."
    8. **Indirect Objects**: Refers to someone indirectly, like "for her."
    9. **Possessive Nouns**: Shows ownership, like "the book of John."
    10. **Time Expressions**: Describes when, like "at noon" or "in June."

- show off => 此時是正面的
- keep tabs on => keep working on that
- list up



- You dont think that you have enough vocabulary, to express what you want to say
   


## Dialog2

**Participant 1:** Hey everyone, morning! Let's talk about how our customer segmentation project is going. Any updates?

**Participant 2:** Yeah, I've been knee-deep in data cleaning and preprocessing. It's taking longer than expected due to some missing values, but we're making progress.

**Participant 3:** That's good to hear. I've been checking out different machine learning algorithms for segmentation. Random forests seem promising, but we should also give gradient boosting models a shot.

*start here*
**Participant 4:** I've been digging into feature engineering. We need the right variables to tell customer segments apart effectively. Also, we should think about adding some time-related features.

**Participant 1:** Great. On my end, I've whipped up some initial data visualization plots to understand data distribution better. This should guide us in feature selection and modeling.

**Participant 2:** I'm putting together a Jupyter Notebook to document our data preprocessing pipeline. It'll be handy for sharing our work and ensuring reproducibility.

**Participant 3:** Speaking of sharing, we should plan a presentation for our stakeholders to keep them in the loop about how we're finalizing the segmentation model.

**Participant 4:** Totally agree. Let's schedule a meeting to coordinate on the presentation. Also, let's discuss the evaluation metrics we'll use to measure the model's performance.

**Participant 1:** Sounds like a plan. Let's aim to wrap up data preprocessing by the end of this week and have some preliminary modeling results to discuss in our next meeting.


- "Knee-deep in" 
    - An idiom indicating deep involvement or immersion in a task or situation. For example, "knee-deep in data cleaning" means heavily involved in data cleaning.