• Education Bureau Registration Number:575690, 597600
Course Centre Day Date Time Hours Trainer Status Enrollment
Big Data Science Professional (module 1,2,3) Wanchai FRI 6 Jan 2023 7:00 – 10:00pm 18 Houston Ho Planned

Big Data Science Professional

Great Learning is the first Arcitura – Licensed Training Partner in Hong Kong

Course Duration:
18 hours, 6 sessions of 3 hours each, exam on the last session.

Course Fee:

Language of Delivery: Cantonese with English terms


Interested in Big Data Scientist course: Follow this link

Interested in Big Data Consultant course: Follow this link


The Big Data Science Certified Professional (BDSCP) program from the Arcitura™ Big Data Science School is dedicated to excellence in the fields of Big Data science, analysis, analytics, business intelligence, technology architecture, design and development, as well as governance.

A collection of courses establishes a set of vendor-neutral industry certifications with different areas of specialization. Founded by best-selling author, Thomas Erl, this curriculum enables IT professionals to develop real-world Big Data science proficiency. Because of the vendor-neutral focus of the course materials, the skills acquired by attaining certifications are applicable to any vendor or open-source platform.


A Certified Big Data Science Professional has demonstrated proficiency in the analysis practices and technology concepts and mechanisms that comprise and are featured in contemporary Big Data environments and tools.

Module 1: Fundamental Big Data (duration: 6 hours)

It provides the high-level overview of essential Big Data topic areas. A basic understanding of Big Data from business and technology perspectives is provided, along with an overview of common benefits, challenges, and adoption issues. It is divided into a series of modular sections, each of which is accompanied by one or more hands-on exercises.

Module 2: Big Data Analysis & Technology Concepts (duration: 6 hours)

This module explores a range of the most relevant topics that pertain to contemporary analysis practices, technologies and tools for Big Data environments. The course content does not get into implementation or programming details, but instead keeps coverage at a conceptual level, focusing on topics that enable participants to develop a comprehensive understanding of the common analysis functions and features offered by Big Data solutions, as well as a high-level understanding of the back-end components that enable these functions.

Module 3: Big Data Analysis & Technology Lab (6 hours)

This course module presents participants with a series of exercises and problems designed to test their ability to apply knowledge of topics covered previously in course modules 1 and 2. Completing this lab will help highlight areas that require further attention, and will further prove hands-on proficiency in Big Data analysis and technology and practices as they are applied and combined to solve real-world problems.

  • 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.

Any IT professional who wants to obtain a formal accreditation to recognize a range of Big Data proficiency

Certified Big Data Science Professional examination

Comprised of BDSCP Modules 1, 2 and 3

Exam fee: HKD3,200

Exam Duration: 2 hours

Exam Format:  Paper-based, 57 questions (including standard multiple-choice questions & lab-style questions)

Passing Score: 68%

Exam Location: Great Learning Education Centre Limited


Official course workbook

  • Great Learning is the first education centre delivered Arcitura Bid Data training in Hong Kong.
  • Unlimited re-sit within 2 years. All lessons can be re-sit, refreshing your knowledge all the way.
  • We are the IT soft skill specialist, highly experienced in delivering complicated and conceptual knowledge in an effective way.
  • We have successfully delivered more than 30 classes of Big Data courses.