關於 medium web design ，我們在網路上蒐集到這些相關的討論、資訊與評價

五分鐘讓矽谷獵頭找到你（下）

越來越多的矽谷獵頭只用Linkedin找候選人，甚至略過履歷，但要怎麼樣容易被獵頭找到，昨天跟大家談了如何把標題變成關鍵字的秘訣，不過除了標題，還有一個被找到的關鍵：技能Skills & Endorsements。

常常有人請我幫他們介紹工作，但一打開他們Linkedin上的技能，明明是個名校mba，首要技能卻是什麼打字、游泳、Outlook！或是想轉職做軟體產品經理，但關鍵字上全是過去經驗的硬體採購。

獵頭或人資把職缺刊登到Linkedin上後，他們會填該職位期待的首要技能，這些技能，通常是招聘經理在職缺說明上寫的關鍵字，Linkedin則會把你的技能和職缺要的技能做比對，推薦獵頭聯繫配對率比較高的候選人！（真的，我曾經上傳職缺說明，結果Linkedin一直寫信給我，推薦我看一個候選人，打開是我自己！哈哈哈！氣得害我差點叫Linkedin退我刊登職缺的錢。）

🔥不知道該寫什麼技能？打開職缺說明，部分的職缺說明旁邊Linkedin會跟你說你少了什麼技能，如果那是你會的技能，把它們補上就對啦！不用五分鐘！

❗️切記，只有最上面三個技能會在其他人不點開展的情況下顯示，因此最上面三個一定要是跟你想找的職缺最有相關的技能。特別是沒有多年經驗的人，最好放硬技能（不要放什麼領導能力、溝通之類的）

❗️LESS IS MORE！
如果你去一家小小的義大利餐廳，看到菜單上有義大利麵和比薩，你覺得怎麼樣？很好。如果你在那家餐廳，看到菜單上還有滷肉飯、海南雞、日式銅鑼燒、泰式酸辣湯、美式大漢堡，你是不是覺得很不妙？

很多人會害怕刪掉技能，即使那些技能跟要找的工作毫無相關，尤其是以前很多人推薦的技能，感覺刪掉好可惜。可是，如果你看到一個只有兩三年經驗的履歷，卻寫說自己會專案管理、產品管理、行銷、設計、業務、財務、招募團隊、寫code，明明要找的其實是數據分析師，你是不是覺得這個人不專業？

🔥不知道該寫什麼技能，職缺說明旁邊又沒跟你說缺什麼
1. 把職缺說明打開看一下關鍵字
2. 有時候小公司貼職缺的是招募經理，直接看招募經理會的技能是什麼
3. 搜尋一下你覺得有同樣職稱的人，看看他們都有什麼關鍵字。比如說職缺是臉書的數據科學家，查一下擔任這個職務的人都有什麼技能。
4. 真的想不到，下面這些參考看看囉！

✅ 軟體產品經理：Product Management, Roadmap Planning, Software Development, A/B Testing, App, Software as a Service (SaaS), Strategy, User Experience (UX), Technical Product Management, Computer Science, User Stories, Product Strategy, Technologist
✅ 專案經理：Project Management, Program Management, Scum, Agile Software Development, Change Management, Process Improvement, PMP, PMO, Resource Management, Project Portfolio Management, Cross-functional Team Leadership
✅ 軟體設計師：User Experience Design, Visual Design, Interaction Design, User Interface, Content Strategy, Prototype, Animation, Motion Design, User Testing, Illustration, Wireframes, Web Design, Usability, User Research, Persona, User Journey
✅ 軟體工程師：Python, JavaScript, NodeJS, Java, AI, AR, VR, Computer Science. Web Development, C++, Mobile Application, iOS Development, HTML, Backbone.js, React.js, SQL, Objective-C, Ruby, CSS
✅ 數據分析師：Web Analytics, A/B Testing, Adobe Analytics, Google Analytics, Marketing Analytics, Product Analytics, Big Data, SPSS, SAS, Tableau, SQL, Modeling, R, Data Analytics, Data Science
✅ 行銷經理：Digital Marketing, SEO, PPC, Programmatic Advertising, Content Marketing, Email Marketing, Online Marketing, Marketing Strategy, Advertising, Online Advertising, Social Media Marketing, Marketing Research
✅ 通用：B2B, B2C, B2B2C, Leadership, Analytical Skills, Organization Skills, E-commerce/產業名

🙌 從花五分鐘挑三個技能開始吧！

快改好你的標題，加阿雅為好友吧（順便幫我的技能按讚喔）！https://www.linkedin.com/in/anyacheng/

Medium 好讀版👇
五分鐘讓矽谷獵頭找到你（下）https://bit.ly/3afc5d2
五分鐘讓矽谷獵頭找到你（上）https://bit.ly/2vuP5Is

五分鐘讓矽谷獵頭找到你（上）

經常有粉絲請我幫忙介紹矽谷工作，第一件事情我不是請他們寄履歷給我，而是請他們傳給我他們的Linkedin連結，因為在美國，越來越多獵頭只用Linkedin找候選人，先看Linkedin背景、直接拿Linkedin連結給面試官們，我還遇過連面試都完了，最後要給offer純粹是為了建檔才跟我要履歷的。

不過，要怎麼樣在獵頭在Linkedin上找到你，最簡單第一步是你名字下面的標題，那裡寫的不是你是誰，而是讓獵頭找到你的搜尋關鍵字，以及吸引他們聯絡你的專業摘要。

你的Linkedin標題是獵頭搜尋的關鍵字！

想想，如果你現在的工作是「某某小公司專案經理」，可是你想找的工作是「科技業行銷經理」，那獵頭一用關鍵字搜尋，根本找不到你，就算找到，一看到你的標題，大概也直接跳過，而且你的「某某小公司」公司名稱，如果不是像臉書谷歌這樣的公司，獵頭也不會搜尋你公司名字。

所以，標題到底要寫什麼呢？標題要寫你要找的工作的關鍵字，包括職稱、產業、專業技能。當然，這些一定要是你曾經有過的職稱、待過的產業、會的專業。

重點不是你做過，是「你要找的！」

1. 職稱：
✅ 軟體產品經理：Product Manager最常見。其他還有Product Owner, Product Specialist, Technical Product Manager等。
✅ 專案經理：Program Manager最常見。其他還有Project Manager, Scrum Master, Technical Program Manager等。
✅ 軟體設計師：UX/UI Designer最常見。其他還有Product Designer, Visual Designer等。
✅ 軟體工程師：Software Engineer最常見。其他還有Tech Lead, Front-end Engineer, Back-end Engineer, Engineer Manager, Machine Learning Engineer等。
✅ 數據分析師：Analyst最常見。其他還有Web Analytics Manager, Product Analyst, Marketing Analyst, Data Scientist等。
✅ 數據工程師：Data Engineer
✅ 行銷經理：Marketing Manager最常見。其他還有Product Marketing Manager, Search Marketing Manager, Media Buyer, Social Media Manager, Email Marketing Manager等。

2. 產業：看你工作或實習的公司是什麼產業
✅eCommerce, Retail, Technology, Software, Hardware, Consumer Electronics, Healthcare, Fitness/Wellness, Fashion, Insurance, Transportation, Fin-tech, Automobile, Hospitality, Gaming, Media, Food, Food Service, Semiconductor 等。

3. 技能：
這個部分很多，我稍稍舉幾個例子，但你可以依照你的背景做調整。
✅ 軟體產品經理：Product Management, Roadmap Planning, Software Development, A/B Testing, App
✅ 專案經理：Agile-Certified, Scum Master, Agile Software Development
✅ 軟體設計師：Content Strategy, Prototype, Animation, Motion Design, User Testing, Invision, Illustration
✅ 軟體工程師：Python, JavaScript, NodeJS, Java, AI, AR, VR, Computer Science
✅ 數據分析師：Big Data, SPSS, SAS, Tableau, SQL, Modeling, R
✅ 行銷經理：SEO, PPC, Programmatic Advertising, Content Marketing

好啦，全部加起來，舉個例子👇

背景：在學校學過電商，在Amazon實習擔任過行銷經理，現任電子公司專案經理，上過谷歌搜尋引擎優化課程。
想找的職缺：矽谷科技公司行銷經理
❓修改前標題：大大電子公司專員 積極找工作中
✅修改後標題：Search Marketing Manager ｜ Google SEO Certified｜Technology Industry ｜eCommerce｜Social Media Marketing｜Amazon Intern

五分鐘改完，搞定！

快改好你的標題，加阿雅為好友吧（順便幫我的技能按讚喔）！https://www.linkedin.com/in/anyacheng/

Medium 好讀版👇
https://bit.ly/3aeREx0

5分鐘讓矽谷獵頭找到你（下）
https://www.facebook.com/304518923226308/posts/1104508256560700/?d=n

ในวิชา "วิทยาการคำนวณ" ระดับชั้น ม. 5
ได้ดึงวิชา data science (วิทยาศาสตร์ข้อมูล)
มาปูพื้นฐานให้เด็กๆ ได้เรียนกันแล้ว นับว่าเป็นโชคดี
เพราะวิชาพวกนี้เป็นของสูง กว่าจะสัมผัสก็คงตอนป.ตรี โท เอก
...Continue ReadingIn the subject of ′′ Calculation Theology ′′ class. 5
Pulled data science (data science)
Let's master the foundation for kids to learn. It's considered lucky.
Because these subjects are high to touch. It's probably in the middle of the year. Tri To Aek

Which I will review the content to read roughly. The content is divided into 4 chapters.
.
👉 ++++ Chapter 1-Information is valuable +++++
.
Data science in the textbook. Used by Thai name as ′′ Information Science ′′
This chapter will mention Big Data or big data with lots of valuable information.
And so much role in this 4.0 s both public and private sector.
.
If you can't imagine when you played Google search network, you'll find a lot of information that you can use in our business. This is why data science plays a very important role.
.
It's not surprising that it makes the Data Scientist s' career (British name data scientist) play the most important role and charming and interesting profession of the 21th century.
.
Data science, if in the book, he defines it
′′ Study of the process, method or technique to process enormous amounts of data to process to obtain knowledge, understand phenomena, or interpret prediction or prediction, find out patterns or trends from information.
and can be analysed to advise the right choice or take decision for maximum benefit
.
For Data science work, he will have the following steps.
- Questioning my own interest.
- Collect information.
- Data Survey
- Data Analysis (analyze the data)
- Communication and Results Visualization (Communicate and visualize the results)
.
🤔 Also he talks about design thinking... but what is it?
Must say the job of a data scientist
It doesn't end just taking the data we analyzed.
Let's show people how to understand.
.
The application design process is still required.
To use data from our analytics
The word design thinking is the idea. The more good designer it is.
Which Data Scientists Should Have To Design Final Applications
Will meet user demand
.
👉 ++++ Chapter 2 Collection and Exploration +++++
.
This chapter is just going to base.
2.1 Collection of data
In this chapter, I will talk about information that is a virtual thing.
We need to use this internet.

2.2 Data preparation (data preparation)
Content will be available.
- Data Cleaning (data cleansing)
- Data Transformation (data transformation)
In the university. 5 is not much but if in college level, you will find advanced technique like PCA.
- Info Link (combining data)

2.3 Data Exploration (data exploration)
Speaking of using graphs, let's explore the information e
Histogram graph. Box plot diagram (box plot). Distributed diagram (scatter plot)
With an example of programming, pulls out the plot to graph from csv (or xls) file.

2.4 Personal Information
For this topic, if a data scientist is implementing personal data, it must be kept secret.
.
Where the issues of personal information are now available. Personal Data Protection is Done
.
.
👉 ++++ Chapter 3 Data Analysis ++++
.
Divided into 2 parts:
.
3.1 descriptive analysis (descriptive analytics)
Analyzing using the numbers we've studied since
- Proportion or percentage
- Medium measurement of data, average, popular base.
Correlation (Correlation) relationship with programming is easy.
.
.
3.2 predictive analysis (predictive analytics)
.
- numeric prediction is discussed. (numeric prediction)
- Speaking of technique linear regression, a straight line equation that will predict future information.
Including sum of squared errors
Let's see if the straight line graph is fit with the information. (with programming samples)
- Finally mentioned K-NN (K-Nearest Neighbors: K-NN) is the closest way to finding K-N-Neighborhood for classification (Category)

*** Note *****
linear regression กับ K-NN
This is also an algorithm. One of the machine learning (machine learning, one branch of AI)
Kids in the middle of the day, I get to study.
.
.
👉 +++ Chapter 4 Making information pictured and communicating with information +++
.
This chapter doesn't matter much. Think about the scientist after analyzing what data is done. The end is showing it to other people by doing data visualization. (Better summoning)
.
In contents, it's for example using a stick chart, line chart, circular chart, distribution plan.
.
The last thing I can't do is tell a story from information (data story telling) with a message. Be careful when you present information.
.
.
.
*** this note ***
😗 Program language which textbooks mentioned and for example.
It's also python and R language
.
For R language, many people may not be familiar.
The IT graduate may be more familiar with Python.
But anyone from the record line will surely be familiar.
Because R language is very popular in statistical line
And it can be used in data science. Easy and popular. Python
.
But if people from data science move to another line of AI
It's deep learning (deep learning)
Python will be popular with eating.
.
.
#########
😓 Ending. Even I wrote a review myself, I still feel that.
- The university. 5 is it going to be hard? Can a child imagine? What did she do?
- Or was it right that I packed this course into Big Data era?
You can comment.
.
But for sure, both parents and teachers are tired.
Because it's a new content. It's real.
Keep fighting. Thai kids 4.0
.
Note in the review section of the university's textbook. 4 There will be 3 chapters. Read at.
https://www.facebook.com/programmerthai/photos/a.1406027003020480/2403432436613260/?type=3&theater
.
++++++++++++++++++++
Before leaving, let's ask for publicity.
++++++++++++++++++++
Recommend the book ′′ Artificial Intelligence (AI) is not difficult ′′
It can be understood by the number. End of book 1 (Thai language content)
Best seller ranked
In the MEB computer book category.
.
The contents will describe Artificial Intelligence (A) in view of the number. The end.
Without a code of dizzy
With colorful illustrations to see, easy to read.
.
If you are interested, you can order.
👉 https://www.mebmarket.com/web/index.php?action=BookDetails&data=YToyOntzOjc6InVzZXJfaWQiO3M6NzoiMTcyNTQ4MyI7czo3OiJib29rX2lkIjtzOjY6IjEwODI0NiI7fQ&fbclid=IwAR11zxJea0OnJy5tbfIlSxo4UQmsemh_8TuBF0ddjJQzzliMFFoFz1AtTo4
.
Personal like the book. You can see this link.
👉 https://www.dropbox.com/s/fg8l38hc0k9b0md/chapter_example.pdf?dl=0
.
Sorry, paper book. I don't have it yet. Sorry.
.
✍ Written by Thai programmer thai progammerTranslated

這個單元我們要談的是到了 2021 年的今天，要架設網站實在不是很困難的事情，相對應的架站工具也很多，那在這麼多選擇之中，我們為什麼會選擇使用 WordPress 呢？

完整章節將收錄在《建立影響力最好的時代｜運用設計思考打造 WordPress 網站》線上課程：https://sat.cool/course/intro/16/info

-

👨🏻‍💻 追蹤 Facebook 臉書動態
https://fb.me/riven.design

📱 在 Instagram 有幕後秘辛
https://www.instagram.com/scorpiusriven

🖥 發摟 Medium 設計部落格
https://riven.medium.com

💻 這是 Riven 的個人網站
https://riven.design

👍 按讚 FB 粉絲專頁 - RAR 設計攻略
https://www.facebook.com/rar.design

🎧 Podcast 廣播聊天節目收聽
https://riven.firstory.io

📡 加入 Telegram 頻道會收到最新消息
https://t.me/rar_design

完整章節將收錄在《建立影響力最好的時代｜運用設計思考打造 WordPress 網站》線上課程：https://sat.cool/course/intro/16/info

-

👨🏻‍💻 追蹤 Facebook 臉書動態
https://fb.me/riven.design

📱 在 Instagram 有幕後秘辛
https://www.instagram.com/scorpiusriven

🖥 發摟 Medium 設計部落格
https://riven.medium.com

💻 這是 Riven 的個人網站
https://riven.design

👍 按讚 FB 粉絲專頁 - RAR 設計攻略
https://www.facebook.com/rar.design

🎧 Podcast 廣播聊天節目收聽
https://riven.firstory.io

📡 加入 Telegram 頻道會收到最新消息
https://t.me/rar_design

謝謝你來到「運用設計思考打造 WordPress 網站」線上課程！
在整個課程當中呢，我們會一起來設計並製作出屬於我們的網站來。

那這是我們的第一個單元，作為一個暖身的開場呢，我想先跟大家聊聊在製作網站之前，可以先來做哪些預先準備。

這些前置作業呢，可以讓我們可以先對網站的製作有個概念、稍微有點頭緒，
各位同學平常有時間的話呢就可以先來加減進行資料預備與素材蒐集，做一個超前部署的動作。

完整章節將收錄在《建立影響力最好的時代｜運用設計思考打造 WordPress 網站》線上課程：https://sat.cool/course/intro/16/info

-

👨🏻‍💻 追蹤 Facebook 臉書動態
https://fb.me/riven.design

📱 在 Instagram 有幕後秘辛
https://www.instagram.com/scorpiusriven

🖥 發摟 Medium 設計部落格
https://riven.medium.com

💻 這是 Riven 的個人網站
https://riven.design

👍 按讚 FB 粉絲專頁 - RAR 設計攻略
https://www.facebook.com/rar.design

🎧 Podcast 廣播聊天節目收聽
https://riven.firstory.io

📡 加入 Telegram 頻道會收到最新消息
https://t.me/rar_design