Hong Kong Management Association

Executive Diploma in Big Data, Data Analytics & AI

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Course Information

Schedules
  • 30 May 2025 (Fri) - 5 Sep 2025 (Fri) 7:00 PM - 10:00 PM
Registration period
25 Feb 2025 (Tue) - 25 May 2025 (Sun)
Price
HKD 10,200 CEF Reimbursable
(Tuition fee for Members:HK$9,600
Tuition fee for Non-members:HK$10,200)
Study Mode
Duration
4 Month(s)
Language
Cantonese
Location
VENUE The Hong Kong Management Association 2-4/F Pico Tower, 66 Gloucester Road Wanchai, HONG KON
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Course Overview

In the era of Big Data and Data Analytics, companies hold more data and information about their business environments than ever before. Increasingly, these companies are recognising the role of data science in leveraging current and historical data and out-thinking competitors. As a result, there is a growing demand for executives who have advanced Big Data, Data Analytics and AI skills and the ability to make informed decisions that drive organisational success. This course will explore those essential tools and techniques on Big Data & Data Analytics. Advanced topics like Machine Learning and AI will be introduced as well. The course comprises theories, case studies, exercise and experience sharing that would certainly help executives and employees to polish and improve their Big Data Analytics skills in ever changing business world. Students can also learn the latest Big Data and Data Analytics tools like: Python, Power BI, Azure AI and AWS AI cloud technologies etc.

What You’ll Learn

Module 1: Big Data & Data Analytics

大數據及數據分析

  1. Introduction to Big Data
  2. Introduction to Data Analytics
  3. Application of Big Data and Data Analytics in Business
  4. Big Data as Digital Asset
  5. Data analytics in actions

Module 2: Data Driven Story Telling & Reporting

運用數據講故事的技巧及制作報告

  1. Introduction to Data Driven Story Telling
  2. Preparation of Quality Data
  3. Extract, Load and Transform
  4. Descriptive Analytics on Datasets
  5. Reporting and Dashboards

Module 3: Machine Learning and AI 機械學習及人工智能

  1. Predictive Analytics in action
  2. Machine Learning and AI Models
  3. AI in actions
  4. Project and Case Studies


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