關於 data science study group ，我們在網路上蒐集到這些相關的討論、資訊與評價

82 WEBSITE GIÚP BẠN HỌC MỌI THỨ TRÊN ĐỜI

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I. HỌC VỀ KINH DOANH

1. edX - Học các khóa học online từ các trường đại học tốt nhất trên thế giới.

2. TED Talks - Tổng hợp các bài diễn thuyết chia sẻ những ý tưởng đột phá nhất về khoa học, giáo dục, thiết kế (nhiều video có sub tiếng Việt)

3. Khan Academy - Tổng hợp các khóa học Toán, Lý, Hóa, Kinh tế, Kinh doanh,... hoàn toàn miễn phí với giao diện và trải nghiệm tuyệt vời

4. ALISON - Học các khóa online miễn phí từ các trường đại học Anh, Mỹ và các chuyên gia từ Google, Microsoft,...

5. MIT Opencourseware - Các khóa học về mọi lĩnh vực được giảng dạy tại Học viện công nghệ Massachusetts (MIT), cung cấp sách, tài liệu bản mềm và video bài giảng

6. Open Yale Courses — Các khóa học về mọi lĩnh vực được giảng dạy tại Đại học Yale

7. Coursmos — Học khóa học vi mô (thời lượng ngắn) ở bất kỳ đâu, trên bất kỳ thiết bị nào

8. Coursera - Học các khóa online miễn phí từ các trường đại học hàng đầu thế giới với nhiều khóa có subtitle tiếng Việt. Bạn có thể chọn gói học miễn phí (vẫn được xem đầy đủ tài liệu, video học) hoặc trả phí (để lấy chứng nhận từ các trường đại học danh tiếng khi hoàn thành khóa học)

9. Highbrow — Nhận các khóa học được chia nhỏ gửi tới hòm mail của bạn hàng ngày (miễn phí)

10. Skillshare — Các khóa học và dự án online mở ra sự sáng tạo của bạn với mức giá chỉ $12/tháng để truy cập vào kho học liệu khổng lồ các kỹ năng hot nhất cho công việc hiện nay

11. Curious — Phát triển kỹ năng với các bài học video online trên giao diện (cả web và app) cực đẹp

12. [lynda.com]— Học công nghệ, kĩ năng sáng tạo và kinh doanh

13. CreativeLive — Học các khóa học sáng tạo miễn phí từ các chuyên gia hàng đầu thế giới

14. Udemy — Học mọi kỹ năng hot nhất cho công việc, từ thiết kế, phát triển web/app, marketing hay kinh doanh với hàng nghìn khóa học miễn phí và trả phí từ các chuyên gia trong ngành

15. Open Learn — Tổng hợp các khóa học miễn phí về mọi lĩnh vực cho mọi người

16. How to start a startup — Tổng hợp các bài học (qua video và tài liệu đọc) được truyền dạy trong vườn ươm khởi nghiệp hàng đầu thế giới Y Combinator

17. Guides.co — Các bài chỉ dẫn chi tiết về mọi thứ từ viết content marketing cho đến khởi nghiệp

18. Inc.edu- Website hữu ích cho những người khởi nghiệp.

19. Reddit Lectures - Bộ sưu tập những bài giảng hàng đầu đến từ các chuyên gia, học viện, chính phủ và các nhà lãnh đạo.

20. Fast Company's 30-Second MBA: Đây là nguồn dữ liệu các đoạn clip ngắn do các giám đốc điều hành thực hiện. Bạn sẽ học được nhiều từ những lời khuyên kinh doanh, bài học cuộc sống tuyệt vời và thực sự nhanh chóng.

21. HubSpot Academy - Cổng thông tin về marketing, SEO, bán hàng, quảng cáo... cho bất cứ ai quan tâm.

22. University of the People - Tổ chức phi lợi nhuận với các khóa học miễn phí về quản trị kinh doanh, khoa học máy tính và y tế.

23. Platzi — Học trực tuyến về thiết kế, marketing và code miễn phí từ các chuyên gia trong giới startup công nghệ tại Mỹ

24. FutureLearn - Các khóa học trực tuyến miễn phí đến từ hơn 40 trường đại học.

25. Investopedia: Đây là nguồn thông tin mà bạn muốn tìm hiểu về thế giới đầu tư, thị trường và tài chính cá nhân.

26. Learnvest - Các doanh nhân thành công nhất biết cách quản lý tiền bạc trong doanh nghiệp cũng như đời sống cá nhân của mình. Ngoài các lớp học về tài chính với mức giá cực kỳ phải chăng, LearnVest cũng cung cấp một số lớp học miễn phí, chẳng hạn như "Xây dựng thói quen tiêu tiền tốt hơn" hay "Làm thế nào để lập ngân sách."

II. HỌC LẬP TRÌNH

27. Codecademy — Học code miễn phí qua các bài học tương tác thú vị, được thực hành trực tiếp

28. Microsoft Virtual Academy — Học thiết kế web, game, app, phát triển nền tảng cloud, dữ liệu lớn,... miễn phí cùng các chuyên gia của Microsoft. Công ty thậm chí còn cho ra mắt một khóa lập trình cơ bản dành riêng cho người Việt, xem ở đây.

29. Udacity — Học code và data science từ A đến Z qua video trực quan tuyệt vời từ các chuyên gia của Google, Facebook. Tương tự như Coursera và edX, bạn có thể chọn gói miễn phí (không lấy bằng) hoặc trả phí (để lấy bằng nanodegree làm đòn bẩy cho sự nghiệp).

30. CodeCombat — Học lập trình qua game

31. Code School — Học code thực hành

32. Code4Startup — Học lập trình nhanh chóng cho startup qua hướng dẫn code lại các website, ứng dụng nổi tiếng như Airbnb, Product Hunt, Tinder,...

33. Thinkful — Nâng cao trình độ với chuyên gia kèm 1-1

34. Free Code Camp — Học code miễn phí để giúp đỡ cộng đồng

35. [Code.org]— Bắt đầu học từ hôm nay với các bài giảng cơ bản

36. BaseRails — Luyện Ruby on Rails và các kỹ năng công nghệ khác

37. Treehouse —Học HTML, CSS, ứng dụng iPhone và hơn thế nữa

38. One Month — Học code và xây dựng ứng dụng, website trong vòng 1 tháng

39. Dash — Học các kỹ thuật thiết kế web mới nhất

III. HỌC THIẾT KẾ - LĨNH VỰC ĐANG RẤT CÓ TIỀM NĂNG TRONG TƯƠNG LAI

40. Alison Online - Học các kỹ thuật thiết kế đa dạng và cấp chứng chỉ dựa trên thử nghiệm.

41. Udemy: Introduction to Graphic Design - Học thiết kế với lớp học mang tính cạnh tranh, các lớp được xếp hạng và chất lượng của lớp được chiếu theo xếp hạng giống như trên Yelp.

42. Massachusetts Institute of Technology - Học trực tuyến như một cơ sở dữ liệu miễn phí của các khóa học thông qua giáo viên chuyên nghiệp và giáo viên khóa học.

43. A Brief History of Typography - Chìa khóa cho công việc của một nhà thiết kế đồ họa, dành cho bất kỳ nhà thiết kế đồ họa mới bắt đầu tham vọng nào.

44. Teach Yourself Graphic Design: A Self-Study Course Outline - Liẹt kê các nguồn lực bạn cần để tạo ra một khóa học tự học về thiết kế đồ họa

45. Veerle’s Graphic Design Blog - Học các hướng dẫn, mẹo và thủ thuật, thông tin chi tiết hữu ích về làm việc với khách hàng; phát triển danh mục đầu tư; cũng như các giải pháp đơn giản cho các vấn đề mà nhiều nhà thiết kế đồ họa có thể phải đối mặt

46. Canva Design School - Học thông tin cơ bản về phông chữ; màu sắc, hình ảnh, hình nền, bố cục và hình dạng

47. Envato Tuts+ Illustration and Design Courses - Học bất kỳ phần mềm và quy trình thiết kế nào.

48. Creative Pro - Học các kỹ năng sâu và khó, dành cho nhà thiết kế muốn mở rộng kỹ năng.

49. CreativeLive - Khóa học trực tuyến hướng tới các nhà thiết kế và nghệ sĩ

IV. HỌC DATA SCIENCE - LĨNH VỰC ĐANG CỰC HOT HIỆN NAY

50. DataCamp — Các bài giảng và khoa học dữ liệu

51. DataQuest — Học data science ngay trên trình duyệt

52. DataMonkey — Phát triển kĩ năng phân tích dữ liệu theo cách đơn giản nhưng thú vị

V. HỌC NGOẠI NGỮ

53. Duolingo — Học nhiều ngoại ngữ miễn phí

54. Lingvist — Học ngoại ngữ trong 200 giờ

55. Busuu — Cộng đồng học ngoại ngữ miễn phí

56. Memrise — Sử dụng flashcards để học từ vựng

57. Freerice: Giúp bạn mở rộng vốn từ vựng nhanh chóng như việc bạn ăn khi đói. Đây là cách tốt nhất để bạn tự cảm nhận về bản thân và học những từ vựng bạn có thể sử dụng trong phần còn lại cuộc đời.

VI. HỌC CÁC LĨNH VỰC KHÁC

58. Pianu — Cách mới để học chơi piano online

59. Yousician — Gia sư dạy ghita riêng cho thời đại công nghệ

60. Digital Photography School: Học chụp ảnh

61. Factsie: Tìm ra những sự thật thú vị, bất thường về lịch sử, khoa học, cùng với các nguồn liên kết khác.

62. Today I Found Out – Website tổng hợp các sự thật thú vị

63. Gibbon: Đây là nơi tổng hợp danh sách nguồn học tập. Người dùng thu thập các bài viết, video giúp ích cho việc học mọi thứ từ chương trình iOS cho đến những câu chuyện kể hiệu quả.

64. Instructables: Bạn có thể học làm bất cứ thứ gì, từ bệ phóng bóng tennis đến pháo đài ngay sân sau nhà.

65. Lumosity: Trang web này đào tạo bộ não của bạn với những trò chơi thiết kế thú vị, khoa học. Bạn có thể để cải thiện trí nhớ và khả năng tập trung của mình.

66. Powersearching with Google: Học cách tìm kiếm bất cứ thứ gì bạn muốn bằng việc cải thiện kỹ năng tìm kiếm Google của mình.

67. Quora: Bạn có thể học bất kỳ điều gì, từ thủ thuật tăng hiệu quả làm việc đến danh sách những thực phẩm tốt nhất mọi thời đại. Những câu hỏi dù ngớ ngẩn đến đâu cũng được những người thông minh và có tiếng tăm trả lời tử tế.

68. Recipe Puppy : Nhập tất cả những nguyên liệu bạn có trong bếp và công cụ tuyệt vời này sẽ đem đến cho bạn danh sách những món ăn mà bạn có thể tạo ra với chúng.

69. Spreeder: Phần mềm đọc trực tuyến miễn phí giúp cải thiện tốc độ đọc hiểu của bạn.

70. StackOverflow: Trang web hỏi đáp dành cho các lập trình viên, về cơ bản nó là người bạn tốt nhất đối với các coder.

71. Unplug The TV: Nội dung video tại đây khá phong phú, bao gồm các chủ đề như tìm hiểu về con đường tơ lụa, lịch sử chiến tranh, khoa học..

72. Internet Sacred Text Archive - Hàng loạt đầu sách miễn phí về tôn giáo, tín ngưỡng, văn học dân gian, thần thoại, thuật giả kim…

73. Vsauce: Đây là một kênh YouTube cung cấp các sự thật thú vị tốt nhất internet, nơi bạn sẽ nhận ra thế giới của chúng ta kỳ lạ đến thế nào.
Chuyện gì sẽ xảy ra nếu thế giới ngừng quay? Tại sao chúng ta lại cảm thấy buồn chán? Hãy theo dõi các video và tìm ra đáp án cho những thắc mắc của bạn.

74. Squareknot — Tương tự như Wikihow, Guides.co cung cấp các bài hướng dẫn sinh động và đẹp mắt về mọi thứ trong cuộc sống

75. Google World Wonders - Khám phá thế giới cổ đại và hiện đại với rất nhiều tài nguyên hữu ích.

76. Lifehacker - Trang web giúp bạn tìm hiểu mọi thứ dưới nhiều góc độ.

77. Library of Congress - Thư viện kiến thức trực tuyến.

78. Boundless - Thư viện sách trực tuyến, miễn phí.

79. MeetUp - Học hỏi kinh nghiệm, chia sẻ những gì bạn biến và xem xét vấn đề trên nhiều khía cạnh mới.

80. Trivium Education - Nơi bạn học tập để vận dụng các phép tu từ, ngữ pháp và phán đoán logic.

81. PBS Video - Các bộ phim tài liệu chuyên sâu, miễn phí.

82. Project Gutenberg - Website cung cấp hơn 50.000 tác phẩm văn học.

Nguon: Kynangmoi
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【International Live Webinar Series】Strategies for Applying Clinical Trials in Europe歐洲臨床試驗申請策略與佈局

🌍此次學苑特別推出國際線上同步遠距課程，並與德國講師合作，提供學員最新的國際臨床試驗實務課程!
此次課程為系列課程，首堂為”歐洲臨床試驗申請策略與佈局”。對於想往歐洲發展、合作或與歐洲從事臨床試驗的相關公司廠商有很大的幫助!

Date:
Part 1: 2020/12/01 (Tues) 16:30 ~ 18:00 (GMT+8)
Part 2: 2020/12/03 (Thurs) 16:30 ~ 18:00 (GMT+8)
Location：Online Webinar Training
Instructor：Anika Staack, Founder of ARC-TRAICOA / EU-QPPV
【Course Outline】
Part 1 2020/12/01 (Tues) 16:30 ~ 18:00 (GMT+8)
A. Europe – One Union with differences

B. European Clinical Trial Directive
1.Role of national competent authorities
2.Role of ethics committees (central / local)
3.Role of investigator
4.Role of sponsor
5.Role of EMA

C. Planning clinical trials in Europe
1.Analysis of product
a.Indication
b.Patient group
2.Analysis of end points
3 .Analysis of protocol
4.Preparing feasibility
5.Choosing Key Opinion Leaders
6.Sponsor or IIT?
7.Similar studies already running?

Part 2 2020/12/03 (Thurs) 16:30 ~ 18:00 (GMT+8)
A. Applying clinical trial
1.Collecting information you need
2.Establishing study team

B. Required entry into EudraCT

C. Required approval from national HAs and ethics

D. Required fulfilment of national data protection laws

E. Considerations
1.Doing it by yourselves
2.Contracting CRO
3.Auditing

F. Upcoming issues: Site and patient recruitment, site resources, patients withdrawal, protocol amendments

G. Final presentation of study reports

Online Course Fees include 2 Webinars, 90 minutes each：
Special Price $160 USD per Person; Original Price $180 USD
(*1) Certificate of Attendance will be issued only if participants attend both part 1 & 2 webinar
(*2) Certificate of Completed Assessment will be issued only if participant pass the assessment

Register here 👉 https://forms.gle/Kj9yMVynsq7yzSzs8

Organizer：ARC-TRAICOA
Co-Organizer：Salt and Light Institute

【Target Audience】
(1.) Anyone who is interested in clinical trials in Europe
(2.) Anyone who has experience in working in clinical trials related field such as PI,PM,RA,RD,MA,DM,ST,CRA, CRC,QC,QA, etc)

【Instructor CV】
Anika Staack

Current Position:
Founder of ARC-TRAICOA
EU-Qualified Person for Pharmacovigilance (EU-QPPV)
Local German QPPV Consultant and Speaker

Previous Experience:
EU-QPPV / Stufenplanbeauftragte & Group Leader PV at Medice
Senior Drug Safety Manager at ICON
Lead Site Management Associate at PRA
Clinical Research Associate at SKM Oncology

Expertise:
Expertise Databases: European Medicine Agency EudraVigilance and xEVMPD
Quality Assurance: Audits & Inspections, SOP Writing and QA documentation, recalls and product quality
Clinical Trial Management: Feasibility, monitoring, eCRF set-up, database reconciliation, site selection, contract management, patient recruitment, study reports
Authorization process: PSMF, RMP & PSUR Writing, answering authority requests, Risk Management, overseeing product life-cycle

Education Background:
Master of Science (Biology)

Email: bioschool@biotech-edu.com Tel: (+886) 02-2545-9721 ext.18

🤓 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

Trong phần 2 này Trang sẽ chia sẻ nhiều hơn về bản chất công việc khoa học dữ liệu của Trang - các công cụ sử dụng, các điểm nhấn, các hiểu lầm, và vì sao nên học thạc sĩ để tiến thân trong ngành khoa học dữ liệu.
Nếu muốn nghe về các hiểu lầm, tua đến phút 17. Để nghe về vì sao học thạc sĩ, tua đến phút 27.

Phần 3 sẽ nói về học Toán ở Mỹ, hay là cơn ác mộng của đời mình, và khác với học Toán ở Việt Nam như thế nào. Nó không dễ như bạn nghĩ đâu
Các tài liệu mà Trang nhắc đến giúp chuẩn bị vào ngành Khoa học dữ liệu:

- Khóa Deep Learning Coursera: https://www.coursera.org/specializations/deep-learning
- Andrew Ng: tất cả các khóa về khoa học dữ liệu
- Towards Data Science Medium: https://towardsdatascience.com/

#NỘIDUNG:
2:00 - Tổng quan công việc ở 2 công ty khoa học dữ liệu của Trang
11:50 -Các công cụ và quy trình làm việc khoa học dữ liệu
11:00 - Active Learning là gì
14:30 - Testing trong khoa học dữ liệu
16:00 - Không biết công cụ công ty yêu cầu có phải vấn đề không?
18:20 - Hiểu lầm 1 về khoa học dữ liệu - cần giỏi Toán và Tin để làm
22:30 - Hiểu lầm thứ 2 về khoa học dữ liệu
27:48 - Học thạc sĩ Machine Learning và Computational DS - càng đi làm càng thấy khác biệt giữa cử nhân và thạc sĩ/tiến sĩ trong công việc
31:30 - Tổng quan các chương trình thạc sĩ Data Science ở Mỹ

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