Course Information
Schedules
- 7 Jun 2024 (Fri) 7:00 PM - 10:00 PM
Registration period
2 May 2024 (Thu) - 6 Jun 2024 (Thu)
Price
HKD 6,800
(Course Duration: 18 hours, 6 sessions of 3 hours each week, exam on the last session.
Course Fee: HKD6,800)
(Course Duration: 18 hours, 6 sessions of 3 hours each week, exam on the last session.
Course Fee: HKD6,800)
Course Level
Study Mode
Duration
18 Hour(s)
Language
Cantonese, English
Location
Unit A, 20/F, Success Commercial Building, 245-251 Hennessy Road, Wanchai, Hong Kong
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Course Overview
Course Objectives
Module 1: Fundamental Big Data Science & Analytics (duration: 6 hours)
This foundational module establishes a basic understanding of fundamental data science, and explains Big Data from business and technology perspectives, including common concepts, models, benefits, challenges and adoption issues.
Module 2: Big Data Analysis & Technology Concepts (duration: 6 hours)
This course module explores contemporary data analysis practices, technologies and tools for Big Data environments at a conceptual level, focusing on common analysis approaches, functions and features of Big Data solutions. Also covered is the Big Data Analysis Lifecycle.
Module 3: Big Data Analysis & Technology Lab (6 hours)
This course module presents a series of exercises and problems that are designed to test your ability to apply your knowledge of topics covered in previous courses. The lab provides a series of real-world exercises for assessing and establishing Big Data environments, and for solving problems using common Big Data analysis techniques.
What You’ll Learn
Module 1
- Fundamental Terminology and Concepts
- A Brief History of Big Data
- Business Drivers that Have Led to Big Data Innovations
- Characteristics of Big Data
- Benefits of Adopting Big Data
- Challenges and Limitations of Big Data
- Basic Big Data Analytics
- Big Data and Traditional Business Intelligence and Data Warehouses
- Big Data Visualization
- Common Adoption Issues
- Planning for Big Data Initiatives
- New Roles Introduced by Big Data Projects
- Emerging Trends
Module 2
- The Big Data Analysis Lifecycle (from dataset identification to integration, analysis and visualization)
- Common Analysis and Analytics Techniques
- A/B testing, Regression, Correlation, Text Analytics
- Sentiment Analysis, Time Series Analysis
- Network Analysis, Spatial Analysis
- Automated Recommendation, Classification, Clustering
- Machine Language, Natural Language, Semantics
- Data Visualization and Visual Analysis
- Assessing Hierarchies, Part-to-Whole Relationships
- Plotting Connections and Relationships, Mapping Geo-Spatial Data
- Foundational Big Data Technology Mechanisms
- Big Data Storage (Query Workload, Sharding, Replication, CAP, ACID, BASE)
- Big Data Processing (Parallel Data Processing, Distributed Data Processing, Shared-Everything/Nothing Architecture, SCV)
- Big Data & Cloud Computing
Module 3
- As a hands-on lab, this course provides a set of detailed exercises that require participants to solve a number of inter-related problems, with the goal of fostering a comprehensive understanding of how Big Data environments work from both front and back-ends, and how they are used to solve real-world analysis and analytics problems.
- For instructor-led delivery of this lab course, the Certified Trainer works closely with participants to ensure that all exercises are carried out completely and accurately. Attendees can voluntarily have exercises reviewed and graded as part of the class completion. For individual completion of this course as part of the Module 3 Self-Study Kit, a number of supplements are provided to help participants carry out exercises with guidance and numerous resource references.
