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Executive Certificate in Big Data & Analytics for Business

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| Introduction | Programme Structure | Course Schedule & Fees | Lecturers & Speakers Application Procedure | 

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Programme Structure

Application Procedure

This programme consists of five modules:


Module 1: Understanding of Big Data

This module provides students with a basic understanding of Big Data, the upcoming trends in data economy, and introduces different sources of data available in the market.

Unit 1: Understanding of Big Data

- Fundamental concepts of “Big Data”

“Big Data”: why most people just talk about it rather than using it?

Revolution of Big Data in different industries

Case studies from real life

Unit 2: New Source of Data: Open Data

- Open data revolution (Why? What? How?)

Key trends of open data you should pay attention to

Open data comparisons by cities

- Group discussion exercise

Unit 3: From Structured Data to Unstructured Data

- Introduction to image and video processing

What is computer vision?

Applications of video analytics 
(People Counting/ Object Recognition/ Facial Expression/ Emotion Detection)


Module 2: Data Collection & Prediction

In Module 2, students will understand the whole process in developing a Big Data strategy and transforming it from a problem to a well-defined objective. Students will be equipped with the key concepts of prediction models and some popular models that are being used in Sales and Marketing. 

Unit 4: Formulation of a Big Data Strategy

- What are and what are not Big Data problems?

How to develop a Big Data Strategy

- How to integrate all sources of data for better insights

How to define a correct Big Data question

Objective-setting for data analytics

Unit 5: Fundamentals of Prediction Models 

- What is prediction model?

- Business Intelligence vs Advanced Data Analytics

- 3 of the most popular prediction models in Sales and Marketing

  • Clustering (Segmentation) 
  • Propensity (Tendency to Buy and Churn) 
  • Collaborative Filtering (Product Upsell/ Cross-sell) 

- Application of prediction to solve society's problems 


Module 3: Project Kick-off

In this module, students can build an understanding on data engineering, and the popular programming tools as well as languages that are commonly used for Big Data projects and data visualisation. We will also explore on how to set a KPI in order to kick-start a successful Big Data Project.

Unit 6: Data Engineering & Pre-processing

- What is data engineering?

- Introduction of data cleansing, ETL and feature engineering

- Tools for traditional BI and Big Data: SQL, HIVE, PIG, Spark, Scoop

- Big Data platform and integration with existing infrastructure

- The rise of data science language: R, Python, SQL. How are they fit-in the big data world?


Unit 7: The Importance of Data Visualisation

- What is data visualisation?

How could data visualisation help business and the best practice

Comparison of different tools: Tableau, QlikView, Microsoft Power BI

- Drive insights from visualisation to action

- Hands-on exercise with Tableau

  • Data preparation
  • Exercise (Map, Crosstab, Time Series, Data Blending, Dashboard)
  • Tableau connects with Python

Unit 8: How to set KPI for a Big Data Project?

- What is a good prediction model?

How to turn the insights from analysis into action?

How to evaluate the prediction power and accuracy of your models?

How to mesaure the success of data analytics?


Module 4: From Roadmap to Implementation (Optional)

Module 4 is the final stage of the course. This module is designed to guide students to implement a Big Data project with a full check-list of dos and don’ts. Students will be given a real Big Data project and will be required to present their ideas to a team of Big Data experts from the industry. The purpose of this group presentation is to strengthen students' knowledge on Big Data in a practical situtaion.

Unit 9: Implementation Check-List 1: Why Big Data? Do we need big data? Are we ready for Big Data?

- Assessment check-list

Real case study: A lesson learnt from others

Required environment – People and Physical environment

Potential challenges – Legal, Culture, Organisation

Financial Assessment

Unit 10: Implementation Check-List 2: Plan for Implementation

- Roadmap development

Development strategy: In-house vs Outsource / Organic vs In-organic / Buy or Build

Vendor selection

Risk assessment

Team building and roll-out plan

Unit 11: Group Presentation, Guest Speakers Sharing and Award Presentation

- A number of guest speakers will be invited to share their stories on Big Data application

- Site visit to the Data Studio at Hong Kong Science Park

- Group presentation by students

- The Best Big Data Application Idea will be awarded to the winning team after presentation


Module 5: Technical Workshop (Optional)

This optional module is specially designed for students who are interested in further developing technical skills in Big Data, and would like to update their knowledge on emerging programming languages. A solid uderstanding of programming and statistics are expected for students attending this module and programming classwork will be included.

Expected Learning Outcomes

Upon completion of this module, students will be able to:

- develop a basic understanding of the concepts with examples in database, algorithms, statistics and machine learning in a Big Data environment; and 

apply R and Python programming languages to answer Big Data questions in business operations.


Unit 12: Introduction to SQL and Algorithms with Python (I)

- Understand database language: SQL

- Be familiar with commonly used SQL statementsclauses, functions, and keywords

- SQL in Python environment

Python overview and Python community introduction

- Working with numbers in Python

- Using Loops to automate repeat code

- Creating functions with Python

Unit 13: Introduction to SQL and Algorithms with Python (II)

- Introduction to basic algorithms with Python:

  • Sorting
  • Searching
  • Cryptography

- Optimisation with Newton’s Method in Python

- AI Concepts: problem solving as searching

- 10 popular algorithms used in data science for big data

- Computer vision with Python in Tensorflow

Unit 14: Introduction of using API (Application Programming Interface)

- Concepts of APIs

- APIs as data services

  • Open data overview in worldwide and HK
  • How to use datasets from open data portal via API
  • How to create datasets to open data portal via API
  • Social media data from APIs, Twitter and Facebook case study

- APIs as cognitive services

  • IBM visual recognition API and Personality Insight API with Python
  • Microsoft Face API and Text Analytics API with Python

- APIs as corporate services

  • World Bank API case study
  • PayPal case study
  • Quandl case study

Unit 15: Introduction to Statistics and Machine-Learning with R (I)

- Introduction to R language, community, and packages

​​Data analysis fundamentals: mathematical expectations, standard deviation, estimation, hypothesis testing, linear regression, central limit theorem

- Probability fundamentals: frequency and histogram, binomial distribution, normal distribution, Poisson distribution, exponential distribution, gamma distribution

- Using "rattle" package for data mining

Unit 16: Introduction to Statistics and Machine-Learning with R (II)

- Marketing concept example: churn analysis on AT&T

​​- Binary classification for customer retention

​​R packages for supervised classification: rpart, e1071, randomForest, nnet, xgboost

​​- ​Prediction result evaluation: confusion matrix, training-testing regime, cross validation, and ROC curve


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Tel: 3762 6188