加入 Systematic Facebook 擁躉群  

這個頁面上的內容需要較新版本的 Adobe Flash Player。

取得 Adobe Flash Player


想定期知道最新課程及優惠嗎?
免費訂閱本中心的課程通訊!

課堂錄影隨時睇 10 大優點之免費試讀:無條件免費試讀,讓您毋須擔心錄影課程及導師質素,信心保證!

Big Data Professional + Big Data Science Professional 國際認可證書課程

  • 課程時間
  • 課程簡介
  • 考試須知
  • 課程內容
  • 詳細內容

傳統服務:課程上堂時間表 (地點:旺角   總費用:$5,980)
編號 日期 (dd/mm) 星期 時間 費用 導師  
BG1070DM  28/10 - 11/11
28/10, 4/11, 11/11
 下載詳細上課日期
10:30am - 5:30pm (lunch: 1:30pm-2:30pm) $5,980 Franco 按此報名

*** 質素保證:免費於任何地點試睇首 1 小時課堂錄影,從而可預先了解導師及教材的質素,才報讀課程來上堂。***
請致電與本中心職員預約。 查看各地點電話
旺角 2332-6544
觀塘 3563-8425
北角 3580-1893
沙田 2151-9360
屯門 3523-1560

免費補堂: 學員可於任何地點補看課堂錄影,從而可銜接往後的課堂!
免費重讀: 學員可於課程結束後三個月內於任何地點不限次數地重看課堂錄影,從而可反覆重溫整個課程!
課時: 18 小時
課堂導師: Franco (任教課程清單)

傳統服務的免費補堂或免費重讀,若選擇旺角或觀塘的閒日星期一至四,便需於 6:30p.m. 或之前完成觀看課堂錄影。


推介服務:課堂錄影隨時睇
編號 地點 可預約星期及時間 學費低至 85 折  
BG1812MV 旺角 一至五:11:30 - 22:30   六及日:10:30 - 21:30   (公眾假期休息) 95 折後只需 $5,681 按此報名
BG1812OV 觀塘 一至五:13:30 - 22:00   六及日:12:30 - 21:00   (星期三及公眾假期休息) 9 折後只需 $5,382 按此報名
BG1812PV 北角 一至五:13:30 - 22:00   六及日:12:30 - 21:00   (星期三及公眾假期休息) 9 折後只需 $5,382 按此報名
BG1812SV 沙田 一至五:13:30 - 22:00   六及日:12:30 - 21:00   (星期三及公眾假期休息) 85 折後只需 $5,083 按此報名
BG1812YV 屯門 一至五:13:30 - 22:00   六及日:12:30 - 21:00   (星期三及公眾假期休息) 85 折後只需 $5,083 按此報名
* 各政府部門可使用 P Card 付款  
免費試睇: 首 1 小時,請致電與本中心職員預約。 查看各地點電話
旺角 2332-6544
觀塘 3563-8425
北角 3580-1893
沙田 2151-9360
屯門 3523-1560
免費重睇: 學員可於享用時期內於報讀地點不限次數地重看課堂錄影,從而可反覆重溫整個課程!
導師解答: 學員可於觀看某一課堂錄影後提出課堂直接相關的問題,課程導師會樂意為學員以單對單的形式解答!
課時: 18 小時
享用時期: 6 星期 (可於報讀日至 4 星期內觀看整個課程,另加 2 星期備用時期)。進度由您控制,可快可慢。
課堂錄影導師: Franco (任教課程清單)
課堂錄影隨時睇: 詳情及示範片段


地區 地址 電話 教育局註冊編號
旺角 九龍旺角亞皆老街 109 號,皆旺商業大廈 18 樓 2332-6544 533459
觀塘 九龍觀塘成業街 7 號寧晉中心 12 樓 G2 室 3563-8425 588571
北角 香港北角馬寶道 41-47 號華寶商業大廈 3 樓 01-02 號舖 3580-1893 591262
沙田 新界沙田石門安群街 3 號京瑞廣場 1 期 10 樓 M 室 2151-9360 604488
屯門 新界屯門屯喜路 2 號屯門柏麗廣場 17 樓 1708 室 3523-1560 592552
注意! 客戶必須查問報讀學校的教育局註冊編號,以確認該校為註冊學校,以免蒙受不必要的損失!


大數據 (Big data) 是一個近年相當流行的概念。當中涉及的概念和技術都相當多,要學習大數據技術,就由學習大數據 (Big data) 概念及考取大數據 (Big data) 的國際認證開始。

Arcitura Education 推出一系列大數據 (Big data) 的國際認證,當中包括相當流行的 Big Data Professional 和 Big Data Science Professional。考試主要考核一般大數據的概念,當中不涉及實質編寫程式和不涉及實質操作大數據平台或軟件。這表示考取國際認證的準備過程中不需要實質學習編寫程式和不需要實質學習操作大數據平台或軟件。這有助減少學習曲線 (learning curve reduction)。考取國際認證後如果有興趣學習大數據 (Big Data) 技術,大家可以考慮修讀以下的課程以豐富大家的大數據 (Big data) 實作技術。

* 以上的課程以課程名稱的英文字母作排序準則

Big Data Professional + Big Data Science Professional 國際認可證書課程時數 18 小時。課程內容主要針對考試需要為主,每條考試題目均附有標準答案以幫助學員通過考試。

為使課堂更為充實和有趣,導師會在課堂示範以下技術。

  • Cassandra

  • Collaborative Filtering

  • Correlation and Regression

  • MemSQL

  • MongoDB

  • MySQL

  • Neo4j

  • Redis

  • Web scraping (ETL) by JS browser automation libraries

  • VoltDB

 

以下是 10 月 28 日開課的 Big Data Professional + Big Data Science Professional 國際認可證書課程所新增的示範。

  • Browser Automation with web scraping by Python.

  • Classification by Python

  • Supervised Machine Learning + Outlier Detection by Python

  • Clustering (KMean) by Python

  • Sentiment Analysis (情感分析) in AWS (Amazon Web Service)

  • Key Phrase Extraction in AWS (Amazon Web Service)

  • Entity Recognition in AWS

課程時數: 課堂 18 小時 (共 6 堂)
適合人士: 對大數據 (Big Data) 概念有興趣的人士
授課語言: 以廣東話為主,輔以英語
課程筆記: 本中心導師親自編寫英文為主筆記,而部份英文字附有中文對照。

只要你於下列科目取得合格成績,便可獲 Big Data Professional 國際認可證書:

  • Exam B90.01: Fundamental Big Data
  • Any one additional exam (e.g. Exam B90.02 or B90.03)

只要你於下列科目取得合格成績,便可獲 Big Data Science Professional 國際認可證書:

  • Exam B90.01: Fundamental Big Data
  • Exam B90.02: Big Data Analysis & Technology Concepts
  • Exam B90.03: Big Data Analysis & Technology Lab

臨考試前要繳付考試費:

  • Exam B90.01: HK$1,230
  • Exam B90.02: HK$1,230
  • Exam B90.03: HK$1,435

及必須出示下列兩項有效之身份證明文件,否則考生不可進行考試,而已繳付之考試費亦不會退回:
1. 香港身份證   及
2. 附有考生姓名及簽名的證件 (如信用咭、香港特區護照、BNO 等)

考試題目由澳洲考試中心傳送到你要應考的電腦,考試時以電腦作答。所有考試題目均為英文,而大多數的考試題目為單項選擇題 (意即 O) 或多項選擇題 (意即 口),其餘則為配對題及實戰題。作答完成後會立即出現你的分數,結果即考即知!考試不合格便可重新報考,不限次數。欲知道作答時間、題目總數、合格分數等詳細考試資料,可瀏覽本中心網頁 "各科考試分數資料"。




Exam B90.01: Fundamental Big Data

  • Understanding Big Data
  • Fundamental Terminology & Concepts
  • Big Data Business & Technology Drivers
  • Traditional Enterprise & Technologies Related to Big Data
  • Characteristics of Data in Big Data Environments
  • Dataset Types in Big Data Environments
  • Fundamental Analysis and Analytics
  • Machine Learning Types
  • Business Intelligence & Big Data
  • Data Visualization & Big Data
  • Big Data Adoption & Planning Considerations

Exam B90.02: Big Data Analysis & Technology Concepts

  • Big Data Analysis Lifecycle (from business case evaluation to data analysis and visualization)
  • A/B Testing, Correlation
  • Regression, Heat Maps
  • Time Series Analysis
  • Traditional Enterprise
  • Network Analysis
  • Spatial Data Analysis
  • Classification, Clustering
  • Filtering (including collaborative filtering & content-based filtering)
  • Sentiment Analysis, Text Analytics
  • Processing Workloads, Clusters
  • Cloud Computing & Big Data
  • Foundational Big Data Technology Mechanisms

Exam B90.03: Big Data Analysis & Technology Lab

  • Case discussions


為使課堂更為充實和有趣,導師會在課堂示範以下技術。

  • Cassandra

  • Collaborative Filtering

  • Correlation and Regression

  • MemSQL

  • MongoDB

  • MySQL

  • Neo4j

  • Redis

  • Web scraping (ETL) by JS browser automation libraries

  • VoltDB

 

以下是 10 月 28 日開課的 Big Data Professional + Big Data Science Professional 國際認可證書課程所新增的示範。

  • Browser Automation with web scraping by Python.

  • Classification by Python

  • Supervised Machine Learning + Outlier Detection by Python

  • Clustering (KMean) by Python

  • Sentiment Analysis (情感分析) in AWS (Amazon Web Service)

  • Key Phrase Extraction in AWS (Amazon Web Service)

  • Entity Recognition in AWS

 




1 Introduction to Big Data
1.1 Big Data characteristics, concepts and terminology
1.1.1 Statistics vs Big Data
1.1.2 Big Data solutions
1.1.3 Common Big Data terminology
1.1.3.1 Datasets
1.1.3.2 Data Analysis
1.1.3.3 Data Analytics
1.1.3.3.1 Stakeholders of data analytics
1.1.3.3.2 Descriptive, Diagnostic, Predictive, Prescriptive analytics
1.1.3.3.2.1 Descriptive analytics
1.1.3.3.2.2 Diagnostic analytics
1.1.3.3.2.3 Predictive analytics
1.1.3.3.2.4 Prescriptive analytics
1.1.3.4 Business Intelligence (BI)
1.1.3.5 Key Performance Indicators (KPI)
1.1.3.5.1 SMART
1.1.3.5.1.1 S
1.1.3.5.1.2 M
1.1.3.5.1.3 A
1.1.3.5.1.4 R
1.1.3.5.1.5 T
1.1.4 Big Data characteristics
1.1.4.1 Volume
1.1.4.1.1 Size units
1.1.4.2 Velocity
1.1.4.3 Variety
1.1.4.4 Veracity (signal vs noise)
1.1.4.5 Value (compared with veracity and time)
1.2 Types of data / data formats
1.2.1 Structured data
1.2.2 Unstructured data
1.2.3 Semi-structured data
1.2.3.1 Semi-structured data as metadata

2 Big Data adoption, analytics and planning considerations
2.1 Business momentum of Big Data adoption
2.1.1 Marketplace dynamics
2.1.1.1 DIKW structure
2.1.1.1.1 D
2.1.1.1.2 I
2.1.1.1.3 K
2.1.1.1.4 W
2.1.2 Business architecture
2.1.3 Business process management
2.1.4 Information and communications technology
2.1.4.1 Digitalization
2.1.4.2 Data science and analytics
2.1.4.3 Accordable technology and commodity hardware
2.1.4.4 Social media
2.1.5 IoE / hyper-connected communities and devices
2.2 Planning considerations
2.2.1 Organization prerequisites
2.2.2 Data procurement (External data sources)
2.2.3 Governance, cybersecurity and privacy
2.2.4 Provenance
2.2.5 Realtime support
2.2.6 Performance
2.2.7 Methodology
2.2.8 Cloud computing
2.3 Big Data Analytics Lifecycle
2.3.1 Business case evaluation
2.3.2 Data identification
2.3.3 Data acquisition & filtering
2.3.4 Data extraction
2.3.5 Data validation & cleansing
2.3.6 Data aggregation & representation
2.3.7 Data analysis
2.3.7.1 Confirmatory analysis (確認分析)
2.3.7.2 Exploratory analysis (探索性分析)
2.3.8 Data visualization
2.3.9 Utilization of analysis results

3 Data science technologies and Big Data BI
3.1 OLTP (Online Transaction Processing)
3.2 OLAP (Online Analytical Processing)
3.3 ETL (Extract Transform Load)
3.3.1 Extract
3.3.2 Transform
3.3.3 Load
3.3.4 ELT in Big Data solution
3.3.4.1 ELT (Not ETL!!)
3.4 Data Warehouses
3.5 Data Marts
3.6 Traditional Business Intelligence (BI)
3.6.1 Ad-hoc reports
3.6.2 Dashboard
3.7 Big Data Business Intelligence (BI)
3.8 Data Visualization
3.8.1 Data Visualization (Traditional)
3.8.2 Data Visualization (Big Data)
3.8.2.1 Aggregation, filtering, drill-down, roll-up and what-if analysis

4 Big Data Storage
4.1 Concepts
4.1.1 Clusters
4.1.2 File Systems
4.1.3 Distributed File Systems
4.1.4 NoSQL
4.1.5 Sharding
4.1.6 Replication
4.1.6.1 Master-slave replication
4.1.6.1.1 Read inconsistency problem and its solution
4.1.6.2 Peer-to-peer replication
4.1.6.2.1 Write inconsistency problem and its solution
4.1.6.2.1.1 Pessimistic concurrency
4.1.6.2.1.2 Optimistic concurrency as its read inconsistency issue
4.1.7 Sharding + replication
4.1.7.1 Sharding + master-slave replication
4.1.7.1.1 Option 1
4.1.7.1.2 Option 2
4.1.7.2 Sharding + peer-to-peer replication
4.1.8 CAP theory
4.1.8.1 CAP
4.1.8.2 CA vs CP vs AP
4.1.9 ACID
4.1.9.1 A
4.1.9.2 C
4.1.9.3 I
4.1.9.4 D
4.1.10 BASE
4.1.10.1 BA
4.1.10.2 S
4.1.10.3 E
4.2 Technologies
4.2.1 On-disk storage
4.2.1.1 Distributed file systems
4.2.1.2 Relational database management systems (RDBMS)
4.2.1.3 NoSQL
4.2.1.3.1 Types of NoSQL
4.2.1.3.1.1 key-value
4.2.1.3.1.2 document
4.2.1.3.1.3 column-family
4.2.1.3.1.4 graph
4.2.1.4 NewSQL
4.2.2 In-memory storage
4.2.2.1 General overview of in-memory storage
4.2.2.2 In-memory storage implementations
4.2.2.2.1 In-memory data grid
4.2.2.2.1.1 Read-through
4.2.2.2.1.2 Write-through
4.2.2.2.1.3 Write-behind
4.2.2.2.1.4 Refresh-ahead
4.2.2.2.2 In-memory database

5 Big Data processing
5.1 Parallel data processing
5.2 Distributed data processing
5.3 Types of processing workloads
5.3.1 Batch
5.3.2 Transactional / Real-time / Streaming
5.4 Hadoop
5.5 Cluster
5.5.1 Batch processing mode
5.5.1.1 MapReduce
5.5.1.1.1 Introduction to MapReduce
5.5.1.1.2 MapReduce tasks
5.5.1.1.2.1 Overview of MapReduce
5.5.1.1.2.2 Map tasks
5.5.1.1.2.2.1 Map
5.5.1.1.2.2.2 Combine
5.5.1.1.2.2.3 Partition
5.5.1.1.2.3 Reduce tasks
5.5.1.1.2.3.1 Shuffle and Sort
5.5.1.1.2.3.2 Reduce
5.5.1.1.2.4 Summarization of MapReduce tasks
5.5.1.1.3 MapReduce algorithms
5.5.1.1.3.1 Task Parallelism
5.5.1.1.3.2 Data Parallelism
5.5.1.1.3.3 Traditional algorithms vs MapReduce algorithms
5.5.1.1.3.4 Key considerations of MapReduce algorithms
5.5.2 Realtime processing mode
5.5.2.1 SCV principle
5.5.2.1.1 S
5.5.2.1.2 C
5.5.2.1.3 V
5.5.2.2 Event stream processing
5.5.2.3 Complex event processing

6 Big Data analysis
6.1 Quantitative analysis (定量分析)
6.2 Qualitative analysis (定性分析)
6.3 Data mining
6.4 Statistical analysis
6.4.1 A/B testing
6.4.2 Correlation
6.4.2.1 1
6.4.2.2 -1
6.4.2.3 0
6.4.2.4 Use cases
6.4.3 Regression
6.4.3.1 Linear regression
6.4.3.2 Non-linear regression
6.4.3.3 More than one independent variable?
6.4.3.4 Use cases
6.4.3.5 Differences between correlation vs regression
6.5 Machine Learning
6.5.1 Techniques
6.5.1.1 Classification (Supervised Machine Learnings)
6.5.1.2 Clustering (Unsupervised Machine Learning)
6.5.1.3 Outlier Detection
6.5.1.4 Filtering
6.5.1.4.1 Collaborative filtering
6.5.1.4.2 Content-based filtering
6.5.1.4.3 Recommender system and filtering
6.5.1.4.4 Use cases
6.5.2 Two fundamental laws regarding machine learning
6.5.2.1 Law of Large Numbers
6.5.2.2 Law of Diminishing Marginal Utility
6.6 Semantic Analysis (語義分析)
6.6.1 Natural Language Processing (NLP)
6.6.2 Text Analytics
6.6.3 Sentiment Analysis (情感分析)
6.7 Visual Analysis
6.7.1 Heat Maps
6.7.2 Time Series Plots
6.7.3 Network Graphs
6.7.4 Spatial Data Analysis
6.8 Mappings to analysis, analytics and machine learning
6.8.1 Quantitative analysis
6.8.2 Qualitative analysis
6.8.3 Data mining
6.8.4 Descriptive analytics
6.8.5 Diagnostic analytics
6.8.6 Predictive analytics
6.8.7 Prescriptive analytics
6.8.8 Supervised machine learning
6.8.9 Unsupervised machine learning

7 Big Data technology mechanisms
7.1 Storage devices
7.2 Processing engines
7.3 Resource manager
7.4 Data transfer engines
7.5 Query engines
7.6 Analytics engines
7.7 Workflow engines
7.8 Coordination engines

8 Case study and brainstorming
8.1 Case study
8.1.1 Background
8.1.2 “Lab” and discussion

9 Examinations
9.1 Exam B90.01
9.1.1 Basic information
9.1.2 Exam topics and important points (重點精華)
9.1.2.1 Scope
9.1.2.2 Understanding Big Data
9.1.2.3 Fundamental Terminology & Concepts
9.1.2.4 Big Data Business & Technology Drivers
9.1.2.5 Traditional Enterprise & Technologies Related to Big Data
9.1.2.6 Characteristics of Data in Big Data Environments
9.1.2.7 Dataset Types / Types of Data in Big Data Environments
9.1.2.8 Fundamental Analysis, Analytics & Machine Learning Types
9.1.2.8.1 Analysis
9.1.2.8.2 Analytics
9.1.2.8.3 Machine Learning Types
9.1.2.9 Business Intelligence & Big Data
9.1.2.10 Data Visualization & Big Data
9.1.2.11 Big Data Adoption & Planning Considerations
9.2 Exam B90.02
9.2.1 Basic information
9.2.2 Exam topics and important points (重點精華)
9.2.2.1 Scope
9.2.2.2 Big Data Analysis vs Traditional Data Analysis
9.2.2.3 Big Data Analysis Lifecycle
9.2.2.4 Statistical analysis, visual analysis, machine learning and semantic analysis
9.2.2.5 A/B Testing
9.2.2.6 Correlations
9.2.2.7 Regression
9.2.2.8 Heat Maps
9.2.2.9 Time Series Analysis
9.2.2.10 Network Analysis
9.2.2.11 Spatial Data Analysis
9.2.2.12 Law of Large Numbers
9.2.2.13 Law of Diminishing Marginal Utility
9.2.2.14 Classification
9.2.2.15 Clustering
9.2.2.16 Outlier Detections
9.2.2.17 Filtering
9.2.2.17.1 Collaborative filtering
9.2.2.17.2 Content-based filtering
9.2.2.18 Natural Language Processing (NLP)
9.2.2.19 Text Analytics
9.2.2.20 Sentiment Analysis
9.2.2.21 Big Data Technology Considerations
9.2.2.21.1 Clusters
9.2.2.21.2 File Systems
9.2.2.21.3 Distributed File Systems
9.2.2.21.4 NoSQL
9.2.2.21.5 Parallel Data Processing
9.2.2.21.6 Distributed Data Processing
9.2.2.21.7 Batch
9.2.2.21.8 Transaction / Realtime
9.2.2.21.9 Cloud Computing
9.2.2.22 Big Data Technology Mechanisms
9.2.2.22.1 Minimum Requirements
9.2.2.22.2 Storage Device
9.2.2.22.3 Processing Engine
9.2.2.22.4 Resource Manager
9.2.2.22.5 Data Transfer Engine
9.2.2.22.6 Query Engine
9.2.2.22.7 Analytics Engine
9.2.2.22.8 Workflow Engine
9.2.2.22.9 Coordination Engine
9.3 Exam B90.03
9.3.1 Basic information
9.3.2 Exam topics and important points (重點精華)

10 Appendix
10.1 Tidy Data
10.1.1 Background
10.1.2 Variables and observations
10.1.3 Properties of tidy data
10.1.4 A poor example
10.1.5 A good example
10.1.6 Let’s brainstorm
10.1.6.1 What do you think about the following dataset?
10.1.6.2 How to improve?

 

更多綜合課程
  法律課程
  • 代理人的法律責任
  • 公司董事和合夥人的法律責任
  • 婚姻的法律責任
  • 遺產繼承的合法權益
  英文課程
  • IPA 拼音:級別 1 2 3 4
  普通話課程
  • 基礎普通話拼音 (免費)
  • 進階普通話拼音
  • 普通話會話:級別 1 2 3
  西班牙語文課程
  • 級別 1 2 3
  中醫課程
  • 濕疹與皮膚敏感病
  • 暗瘡與色斑 | 鼻敏感與感冒
  • 脫髮與白髮 | 從五官看健康
  攝影課程
  • 攝影初級
  • 攝影中級 (風景專題)
  風水命理課程
  • 紫微斗數:級別 1 2 3
  • 子平八字:級別 1 2 3
  • 八字風水:級別 1 2 3
  • 奇門遁甲:級別 1 2 3

這個頁面上的內容需要較新版本的 Adobe Flash Player。

取得 Adobe Flash Player