Preparation
Course Detail
| Key area of study | A04 - COMPUTER SCIENCE AND INFORMATION TECHNOLOGY |
| Course name (English) | Foundations of Data Analytics [Module from Master of Science in Big Data Technology] |
| Course name (Chinese) | --- |
| Course code | 34Z126908 |
| Institution name (English) | THE HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY (HKUST) |
| Institution name (Chinese) | 香港科技大學 |
| Institution code | 003 |
| Institution phone |
請點擊查詢院校電話 Please click here for Enquiry Hotline |
| Award | OTHERS |
| Course fee (HK$) | $21000 |
| QR Number | 16/000880/L6 |
| QF Level | 6 |
| Remark |
Notes / 附註: *APPLICANT PURSUING THIS COURSE WITH COURSE COMMENCEMENT DATE FALLING AFTER 31 MARCH 2024 IS NOT ELIGIBLE TO CLAIM REIMBURSEMENT FROM CEF. / 申請人報讀於二零二四年三月三十一日後開課的課程並不能申領基金發還款項。 Entry Requirements / 入學要求: (1) General Admission Requirements of the University: Applicants seeking admission to a master's degree program should have obtained a bachelor’s degree from a recognized institution, or an approved equivalent qualification. (2) English Language Admission Requirements* - Applicants have to fulfill English Language requirements with one of the following proficiency attainments: (i) TOEFL-iBT: 80 (ii) TOEFL-pBT: 550 (iii) TOEFL-Revised paper-delivered test: 60 (total scores for Reading, Listening and Writing sections) (iv) IELTS (Academic Module): Overall score: 6.5 and All sub-score: 5.5 *Applicants are not required to present TOEFL or IELTS score if their first language is English, or they obtained the bachelor's degree (or equivalent) from an institution where the medium of instruction was English. (3) Program-Specific Admission Requirements: (a) A bachelor's degree in Computer Engineering, Computer Science, Mathematics or a related area, or (b) (i) A bachelor’s degree in other disciplines and (ii) relevant work experience in IT and Mathematics related fields. Course Outline / 課程大綱: Each course is composed of 39 face-to-face contact hours (3-hour per week). 1st Lecture - Course introduction; Math review 2nd Lecture - Supervised Learning: linear models, kNN, Decision Trees 3rd Lecture - Bias-variance tradeoff; ensemble learning; overfitting 4th Lecture - Unsupervised learning: clustering; PCA; PageRank 5th Lecture - Deep Learning Workshop with Tensorflow 6th Lecture - Public holiday, no class 7th Lecture - Proposal Presentation 8th Lecture - Midterm Exam 9th Lecture - Similarity measure ; Min-hashing 10th Lecture - Data integration: record matching 11th Lecture - Data fusion; Data privacy & security 12th Lecture - Project Presentation 1 13th Lecture - Project Presentation 2 Instructors' Qualifications / 導師資歷: Instructor is the full-time faculty member of HKUST. He/she has obtained PhD degrees in related disciplines and possessed the necessary expertise in the area. Assessment / 課程評核要求: (A) Assessment Items and Their Weightings: (1) Midterm exam: 40% (2) Individual project: 20% (3) Group project: 40% (B) Completion and CEF Reimbursement Requirements: (1) Grade C, the overall passing grade of the course, equals to 50% of the overall mark of the course. (Grades range from A+ to F. The grades C- to D-, and E, are not used in postgraduate courses. Grade F is a failure grade which cannot be credited toward program graduation requirement.) (2) The applicant must have attended no less than 70% of the contactable hours Delivery Mode / 授課模式: 39 hours (FT/PT) Course Duration / 課時: 13 weeks with 3 hours per class each week Effective Date / 課程生效日期: 01/04/2020 CEF Registration Invalid From / 基金課程登記失效日期: 01/04/2024 |
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List of Reimbursable Courses