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在data analysis example這個產品中，有20篇Facebook貼文，粉絲數超過52萬的網紅เกมถูกบอกด้วย v.2，也在其Facebook貼文中提到， https://www.humblebundle.com/books/ultimate-python-bookshelf-packt-books?partner=ggcp แพ็คใหม่จาก Humble นะครัช ครั้งนี้คือ HUMBLE BOOK BUNDLE: THE UL...
　同時也有1部Youtube影片，追蹤數超過115萬的網紅Rayner Teo，也在其Youtube影片中提到，Rayner: 00:00 Today, I have a question from Kara who asks, "Hey, Rayner, if I want to use multiple timeframes in my trading, for example, the price is...

data analysis example 在 เกมถูกบอกด้วย v.2 Facebook 的精選貼文

เกมถูกบอกด้วย v.2 By เกมถูกบอกด้วย v.2

2021-04-06 14:16:55 有 197 人按讚
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https://www.humblebundle.com/books/ultimate-python-bookshelf-packt-books?partner=ggcp
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ดีลนี้หมดเวลาในอีก 20 วันกว่าๆ
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เกมถูกบอกด้วย v.2

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data analysis example 在 經濟部中小企業處 Facebook 的精選貼文

經濟部中小企業處 By 經濟部中小企業處

2020-09-09 10:00:47 有 33 人按讚
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【政策簡單說】透過Dropshipping輕鬆經營海外市場🌎🌍🌏🔥

有聽過 #Dropshipping嗎🤔 知道它的功能嗎🤔

Dropshipping又稱直運或一件代發，對於時間與預算有限的經營者而言，不僅不須承擔庫存風險，更可以降低跨境經營國際市場的風險‼

通常Dropshipper會選擇一站式的電商平台開店，例如經營歐美市場會選擇將產品上架至Shopify，並讓此平台協助進行訂單管理、金流、物流、客戶管理、蒐集與分析銷售數據分析，而Dropshipper本身只需要設計網頁、導購行銷，並將確認的訂單轉給工廠即可，工廠直接出貨給客戶，提升產品物流的效率👍

🔎若想知道更多Dropshipping內容請參考

https://www.bigcommerce.com/blog/dropshipping/

🔎資料參考
https://www.shopify.com/blog/dropshipping-guide

Have you heard Dropshipping and what it’s capable of?
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Once a customer buys a product, Dropshippers will be able to fulfill the order by passing on the sales order to a third-party supplier, who then ships the order to the customer.

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經濟部中小企業處

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data analysis example 在 โปรแกรมเมอร์ไทย Thai programmer Facebook 的精選貼文

โปรแกรมเมอร์ไทย Thai programmer By โปรแกรมเมอร์ไทย Thai programmer

2020-07-17 22:17:51 有 134 人按讚
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🤓 หลายคนอาจเคยบ่น "เรียนเลขไปทำไม ไม่เห็นได้ใช้เลย"
อันนี้เป็นแค่ตัวอย่าง เพื่อให้รู้ว่าเลขที่เราเรียนตอนม.ปลาย
ไม่ควรทิ้งถ้าคิดจะเรียนคอมพิวเตอร์ ในระดับสูง
.
👉 1) สมการเชิงเส้น
เริ่มต้นจากสมการเส้นตรง ที่มีหน้าตาดังนี้ y=mx+c เรียกว่ารูปมาตรฐาน
- เมื่อ m เป็นความชัน
-ส่วน c เป็นจุดตัดแกน y
.
สมการเชิงเส้นเราจะได้เรียนในระดับ ม 4
พอในม.5 วิชา วิทยาการคำนวณ
ก็จะเห็นประโยชน์ของสมการเส้นตรงถูกนำไปใช้ในงาน data science (วิทยาการข้อมูล)
นำไปใช้วิเคราะห์ข้อมูลแบบ linear regression
.
กล่าวคือเมื่อเรามีข้อมูลย้อนหลังในอดีต
แล้วสามารถนำไปพล็อตลงบนกราฟแกน x กับ y
ผลปรากฏว่าข้อมูลมีความสัมพันธ์เป็นเส้นตรง
ในกรณีเราสามารถหาสมการเส้นตรงที่เหมาะสมสุด (optimize)
นำมาใช้พยากรณ์ข้อมูลล่วงหน้าในอนาคตได้
.
แต่ในกรณีที่ความสัมพันธ์ของข้อมูลพบว่าไม่ใช่เส้นตรง
เราสามารถใช้สมการที่ไม่ใช่เส้นตรง มาใช้พยากรณ์ข้อมูลก็ได้เช่นกัน
.
👉 2) เมทริกซ์
คือกลุ่มของจำนวนตัวเลข ที่เขียนเรียงกันเป็นรูปสี่เหลี่ยมผืนผ้าหรือจัตุรัส
นอกจากใช้แก้สมการหลายตัวแปรแล้ว
จะมีประโยชน์เวลานำไปประมวลภาพ (Image processing)
หรืองานพวกคอมพิวเตอร์วิชั่น (computer vision)
.
ต้องบอกอย่างนี้ว่า รูปภาพดิจิตอลที่เราเห็นเป็นสีสันสวยงาม
แต่ทว่าคอมไม่ได้มองเห็นเหมือนคน
มันมองเห็นเป็นเมทริกซ์ โดยข้างในเมทริกซ์ก็คือตัวเลขของค่าสี
และเราสามารถกระทำการคณิตศาสตร์กับรูปภาพได้
เช่น บวกลบ คูณหาร กับรูปภาพดิจิตอล ในมุมของเมทริกซ์
.
👉 3) ความน่าจะเป็น
ยกตัวอย่างเช่น ทฤษฏี Bayes' theorem
ทฤษฏีหนึงของความน่าจะเป็น
จะใช้หาว่าสมมติฐานใดน่าจะถูกต้องที่สุด โดยใช้ความรู้ก่อนหน้า (Prior Knowledge)
.
ทฤษีนี้ถูกนำไปใช้ในงานวิเคราะห์ข้อมูล รวมทั้งการเรียนรู้ของเครื่อง
เช่น จงหาความน่าจะเป็นที่ชาเขียวขวดนั้นจะผลิตจากโรงงานจากประเทศไทย
จงหาความน่าจะเป็นว่าผู้ป่วยจะเป็นโรคมะเร็ง เมื่อหายจากการติดเชื้อไวรัสโคโรนา
เป็นต้น
.
👉 4) แคลคูลัส
ตัวอย่างเช่น ถูกนำมาใช้ใน neural network
ซึ่งก็เครือข่ายประสาทเทียมที่เลียนแบบเซลล์สมอง
แต่จริงๆ ข้างในเครือข่ายจะประกอบไปด้วยน้ำหนัก
.
น้ำหนักที่ว่านี้มันก็คือตัวเลขจำนวนจริง ที่เริ่มต้นสุ่มขึ้นมา
แล้วเวลาจะหาค่าน้ำหนักที่เหมาะสม (optimize)
มันจะถูกปรับทีละเล็กทีละน้อย
โดยอาศัยหลักการเรื่องอนุพันธ์ หรือดิฟนั่นแหละ
.
👉 5) ตรรกศาสตร์
วิชานี้พูดถึง "ประพจน์" หมายถึงประโยคที่ให้ค่าออกมาเป็น True หรืด False
รวมถึงการใช้ตัวเชื่อมประพจน์แบบต่างๆ ไม่ว่าจะเป็น "และ" "หรือ" "ก็ต่อเมื่อ" เป็นต้น
.
ศาสตร์ด้านนี้เป็นพื้นฐานของระบบคอมพิวเตอร์
เพราะวงจรคอมพิวเตอร์พื้นฐาน มีแต่ตัวเลข 0 หรือ 1
จึงสามารถแทนด้วย False หรือ True ในทางตรรกศาสตร์
ไม่เพียงเท่านั้นวงจรอิเลคทรอนิกส์ ก็มีการดำเนินทางตรรกศาสตร์อีกด้วย
ไม่ว่าจะเป็น "และ" "หรือ" "ไม่" เป็นต้น
.
ยิ่งการเขียนโปรแกรม ยิ่งใช้เยอะ
เพราะต้องเปรียบเทียบเงื่อนไข True หรือ False
ในการควบคุมเส้นทางการทำงานของโปรแกรม
.
👉 6) ฟังก์ชัน
ฟังก์ชันคือความสัมพันธ์ จากเซตหนึ่งที่เรียกว่า 'โดเมน' ไปยังอีกเซตหนึ่งที่เรียกว่า 'เรนจ์' โดยที่สมาชิกตัวหน้าไม่ซ้ำกัน
ซึ่งคอนเซปต์ฟังก์ชันในทางคณิตศาสตร์
ก็ถูกนำไปใช้ในการเขียนโปรแกรมแบบ functional programming
.
👉 7) เรขาคณิตวิเคราะห์
ถูกนำไปใช้ในวิชาคอมกราฟิก หรือเกมส์
ในมุมมองของคนที่ใช้โปรแกรมวาดรูปต่างๆ หรือโปรแกรมสร้างแอนนิมเชั่นต่างๆ
เราก็แค่คลิกๆ ลากๆ ก็สร้างเสร็จแล้วใช่มั๊ยล่ะ
.
แต่หารู้หรือไม่ว่า เบื้องเวลาโปรแกรมจะวาดรูปทรง เช่น สี่เหลี่ยม วงรี ภาพตัดกรวยต่างๆ
ล้วนอาศัย เรขาคณิตวิเคราะห์ พล็อตวาดรูปทีละจุดออกมาให้เราใช้งาน
.
👉 8) ปีทาโกรัส
ทฤษฏีสามเหลี่ยมอันโด่งดังถูกนำไปใช้วัดระยะทางระหว่างจุดได้
ซึ่งจะมีประโยชน์ในการแยกแยะข้อมูล โดยใช้อัลกอริทึม
K-Nearest Neighbors (KNN)
ชื่อไทยก็คือ "ขั้นตอนวิธีการเพื่อนบ้านใกล้ที่สุด "
มันจะถูกนำไปใช้งานวิเคราะห์ข้อมูล รวมทั้งการเรียนรู้ของเครื่องอีกด้วย
ไม่ขอพูดเยอะเดี่ยว ม.5 ก็จะได้รู้จัก KNN ในวิชาวิทยาการคำนวณ
.
👉 9) ทฤษฏีกราฟเบื้องต้น
อย่างทฤษฏีกราฟออยเลอร์ (Eulerian graph)
ที่ได้เรียนกันในชั้น ม.5 จะมีประโยชน์ในวิชาคอม
เช่น ตอนเรียนในวิชา network ของคอมพิเตอร์ เพื่อหาเส้นทางที่ดี่สุดในการส่งข้อมูล
หรือจะมองโครงสร้างข้อมูลเป็นแบบกราฟก็ได้ ก็ลองนึกถึงลิงค์ต่างในเว็บไซต์ สามารถจับโยงเป็นกราฟได้ด้วยนะ
.
👉 10) เอกซ์โพเนนเชียล และลอการิทึม
เราอาจไม่เห็นการประยุกต์ใช้ตรงๆ นะครับ
แต่ในการประเมินประสิทธิภาพของอัลกอริทึม เวลาเขียนโปรแกรม
เขาจะใช้ Big O ขอไม่อธิบายเยอะแล้วกันเนอะ
เรื่องนี้มีเขียนอยู่ตำราวิทยาการคำนวณชั้นม.4 (ไปหาอ่านเอาได้)
.
ซึ่งเทอม Big O บางครั้งก็อาจเห็นอยู่ในรูปเอกซ์โพเนนเซียล หรือลอการิทึมนั่นเอง
ถ้าไม่เข้าใจว่า เอกซ์โพเนนเซียล หรือลอการิทึม คืออะไร
ก็ไม่จะอธิบายได้ว่าประสิทธิภาพของอัลอริทึมเราดีหรือแย่
.
+++++++
เป็นไงยังครับ สนใจอยากรู้ว่า เลข ม.ปลาย
สามารถนำไปใช้ศึกษาต่ออะไรอีกบ้างไหมเนี่ย
ถ้าอยากรู้ ผมเลยขอแนะนำหนังสือ (ขายของหน่อย)
.
หนังสือ "ปัญญาประดิษฐ์ (AI) ไม่ยาก"
เข้าใจได้ด้วยเลขม. ปลาย เล่ม 1 (เนื้อหาภาษาไทย)
ติดอันดับ Best seller ในหมวดหนังสือคอมพิวเตอร์ ของ MEB
.
เนื้อหาจะอธิบายปัญญาประดิษฐ์ (A) ในมุมมองเลขม.ปลาย
โดยปราศจากการโค้ดดิ้งให้มึนหัว
พร้อมภาพประกอบสีสันให้ดูอ่านง่าย
.
สนใจสั่งซ์้อได้ที่
👉 https://www.mebmarket.com/web/index.php…
.
ส่วนตัวอย่างหนังสือ ก็ดูได้ลิงค์นี้
👉 https://www.dropbox.com/s/fg8l38hc0k9b…/chapter_example.pdf…
.
ขออภัยเล่มกระดาษตอนนี้ยังไม่มี โทดทีนะครัชชช
.
✍เขียนโดย โปรแกรมเมอร์ไทย thai progammer
🤓 Many people may have complained that ′′ I have studied the number, why I haven't used it
This is just an example to know the number we studied in high school. The end.
Shouldn't leave if you think about studying computer at a high level.
.
👉 1) Linear equation
Starting from a straight line equation that looks like y=mx+c called standard photo.
- when m is steep
- c section is a y core cutting point
.
Linear equation, so we can study in level 4
Enough in the university. 5 Computational Science
You will see the benefits of a straight line equation. Used in data science (data science)
Linear regression data analysis
.
When we have data backwards in the past
Then can be taken to plot on the graph x with y
The result appears that the information has a straight line of relationships.
In case, we can find the most suitable straight line equation (optimize)
Presentation for future advance information
.
But in case the relationship of information finds it not a straight line.
We can also use equations that are not straight lines to predict information.
.
👉 2) Matrix
A group of numbers that are written in a square or square.
Apart from using to solve many variables.
It will be useful when you compilate photos. (Image processing)
Or computer vision work (computer vision)
.
This is what we have to say. The digital photos we see are beautiful.
But the computer doesn't see it as a human.
It's seen as a matrix. Inside the matrix is a number of colors.
And we can do math with pictures
For instance, subtract, multiply with digital photos in the matrix corner.
.
👉 3) Probability
For example, Bayes s' theorem theory
Theory of probability
Find out which hypothesis is most accurate using previous knowledge (Prior Knowledge)
.
This theory is applied to data analytics and machine learning.
For example, find the probability that green tea will be manufactured from Thailand's factory.
Consider the probability that patients have cancer when they recover from coronavirus infection.
Etc.
.
👉 4) Calculus
For example, being used in neural network
Which is also an artificial neural network that imitates brain cells.
But really in the network, it consists of weight
.
This weight is a random number that starts randomly.
Time will find the right weight (optimize)
It will be fined little by little
By principle of derivative or derivative.
.
👉 5) Logic
This subject is referring to ′′ plural ′′ meaning a sentence that gives value to True or False.
Includes using different types of plural connectors, whether it's ′′ and or when etc.
.
This aspect of computer system is fundamental.
Because basic computer circuits are only 0 or 1 numbers.
So it can be replaced with False or True in logic.
Not only that, the electronic circuit also has a logical action.
Whether it's ′′ and or no etc.
.
The more programming, the more I use.
Because we have to compare terms True or False
In controlling the program's working path
.
👉 6) function
Function is a relationship from one set called ' domain ' to another set called ' Range ' by unique member.
Which concepts function in mathematics
It was also applied to functional programming.
.
👉 7) Geometry analysis
Being applied to Computer, Graphics or Games
In view of people who use various drawing programs or animation programs.
We just click and drag. It's done. Right?
.
But I don't know that the program time will draw shapes like a rectangle, crop of various cones.
All in Geometry. Analyse the plot. Draw one at a time. Let us use it.
.
👉 8) Year Takorus
The famous triangle theory is applied to measure distance between spots.
It will be useful to digest data using algorithm.
K-Nearest Neighbors (KNN)
Thai name is ′′ nearest neighbourhood method
It will also be implemented, analyzed data, including machine learning.
I don't want to talk too much. Single. 5 I will know KNN in Calculation Theology.
.
👉 9) Preliminary Graph Theory
Theoretical Graph Oyler (Eulerian graph)
That we have studied in high school. 5 will be useful in computer class
For example, when studying in computer network subjects, find the best way to send information.
Or you can look at data structures as graphics. Think of different links on websites. You can be connected to a graph.
.
👉 10) m & LOGARIETY
We may not see the application frankly.
But in assessing performance of programming time algorithm.
He will use Big O. I don't want to explain too much.
This story is written in the textbook. Calculating in the university. 4 (Let's find it to read)
.
Big O semester may sometimes be seen in esponical or logarithm.
If you don't understand what Exponcial or Lokarithm is.
It doesn't explain how good or bad our alitum performance is.
.
+++++++
How are you? If you are interested, I want to know the number. The end.
What else can I apply to study?
If you want to know, I recommend the book (selling)
.
′′ Artificial Intelligence (AI) is not difficult ′′ book.
It can be understood by the number. End of book 1 (Thai language content)
Best seller ranked in 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

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โปรแกรมเมอร์ไทย Thai programmer

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data analysis example 在 Rayner Teo Youtube 的精選貼文

Rayner Teo By Rayner Teo

2018-07-31 17:30:02 有 10,266 人看過 有 268 人喜歡
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Rayner: 00:00 Today, I have a question from Kara who asks, "Hey, Rayner, if I want to use multiple timeframes in my trading, for example, the price is coming to a daily support, how do I use multiple timeframes to better my entry? Should I be looking at a one-minute timeframe, a five minutes timeframe, or the one-hour timeframe?" Here's my take on it when using multiple timeframes. You can think of it like let's say, for example, you're old. You're about 70 years old and you're trying to read the words on the monitor on your screen.

Rayner: 00:33 Okay, and that's pretty small, so what do you do? You get a magnifying glass and you look at the monitor. The magnifying expands the words and it's easier to read because now the font size is larger, but what if you magnify too much? Let's say you magnified to 10 times what it was and the font size expands, huge, so what happens? If you look at that now, the words will appear very large like one huge thing in front of you. You can start to see the individual grain of the pixel, and if you're looking at it, does it make sense? No.

Rayner: 01:11 You pretty much forgot what you're trying to accomplish in the first place because the thing is way too zoomed in, and this is the same as trading. If you're looking at the daily timeframe, for example, the price coming to an area of support and you zoom in down to the one-minute timeframe to better time your entry, it's way too zoomed in just like the magnifying glass example. You can see the individual bar and stuff like that, but it has no relevance to the higher timeframe because you're too zoomed in and whatever data you're looking at is pretty much noise relative to the daily timeframe. So my suggestion is if you want to find a sweet spot where you zoom in enough to see additional information at the same time using the information that is related to the higher timeframe, I suggest using a factor of four to six.

Rayner: 02:01 What I mean by this is that let's say, for example, the daily timeframe ... Price comes into support and you want to better time your entry, you can use an example of a factor of six. One day, you have 24 hours, so a factor of six means that you go down to the four timeframes to better time your entry.

Rayner: 02:27 If you want to use a factor of four, let's say you identify resistance on the four timeframes, so a factor of four would be then you go down to the one-hour timeframe, four hours divided by four which is a factor of four.

Rayner: 02:51 So this is the sweet spot to use multiple timeframes. If you use a factor of 20 or 30, I would say the information is too detailed and it has no relevance to the higher timeframe. With that said, if you want to learn proven trading strategies to get rich slow, head down to my website tradingwithrayner.com and I'll talk to you soon.

If you want more actionable trading tips and strategies, go to https://www.tradingwithrayner.com

Thanks for watching!

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post-title
Rayner Teo

About author
Hi! My name is Rayner Teo, an independent trader, and the founder of TradingwithRayner. You won't see me post pictures of Lamborghini, Ferrari, or hot chicks because it won't help you become a better trader. Instead, what you'll get are educational videos on Price Action Trading, Stocks, Forex, and Technical Analysis. If you want to learn more, go to https://www.tradingwithrayner.com/ Disclaimer: Financial trading has large potential rewards, but also large potential risk. You must be aware of the risks and be willing to accept them in order to trade the financial markets. This YouTube channel is intended for educational purposes only. No representation is being made that any account will or is likely to achieve profits or losses similar to those discussed on this channel. The past performance of any trading system or methodology is not necessarily indicative of future results.
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