Partnership with

Expected Learning Outcomes

Introduction

This programme aims to provide students with core concepts, real case studies, idea exchange discussion and implementation check-list to guide them on the application of Big Data and analytics in the business world. It also intends to help students to be more e�ective in communicating with their data science colleagues when necessary.

The programme is especially for those who are new to data analytics and would like to explore how Big Data applications can be useful in their business or career.

Upon completion of the course, students will be able to:

develop a basic understanding of the overall process of Big Data analytics in solving business problems;

apply appropriate Big Data strategies to achieve business objectives; and

utilise the concepts and techniques of Big Data analytics in their business operations.

A U T U M N I N T A K E 2 0 1 8

EXECUTIVE CERTIFICATEIN BIG DATA & ANALYTICS

FOR BUSINESS

MODULE 1

MODULE 2

DIFFERENT SOURCES OF DATA

DATA STRATEGY & PREDICTION

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

Unit 1 : Understanding of Big Data Unit 2 : New Source of Data: Open Data

Unit 3 : From Structured Data to Unstructured Data

Unit 4：Formulation of a Big Data Strategy

• Fundamental concepts of “Big Data”

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

• Revolution of Big Data in di�erent industries

• Case studies from real life

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

• Key trends of open data you should pay attention to

• Open data comparisons by cities

• Group discussion exercise

• Introduction to image and video processing

• What is computer vision?

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

In Module 2, students will understand the whole process of 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.

• 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 a 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

PROGRAMME STRUCTURE

• 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 measure the success of data analytics?

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

MODULE 3

MODULE 4

PROJECT KICK-OFF

FROM ROADMAP TO IMPLEMENTATION

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 Unit 7 : The Importance of Data Visualisation

Module 4 is the final stage of the course. This module is designed to guide students to implement a Big Dataproject 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 grouppresentation is to strengthen students’ knowledge on Big Data in a practical situation.

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

• 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 do they fit in the Big Data world?

• What is data visualisation?

• How data visualisation can help business and the best practice

• Comparison of di�erent 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 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

(OPTIONAL)

• 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 Data Application Idea will be awarded to the winning team after the presentation

Unit 11Group presentation, guest speakers’ sharing sessions and award presentation

MODULE 5

TECHNICAL WORKSHOP

(OPTIONAL)

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

• Understand database language: SQL• Be familiar with commonly used SQL statements, clauses, functions, and keywords• SQL in Python environment• Python overview and Python community introduction• Data Structure: • Working with numbers in Python• Using Loops to automate repeat code• Creating functions with Python

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.

EXPECTED LEARNING OUTCOMES

• Introduction to basic algorithms with Python: - Sorting - Searching - Cryptography• Optimization 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 13: Introduction to SQL and Algorithmswith Python (II)

Unit 14Introduction 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 15Introduction to Statistics and Machine-Learning with R (I)

• Introduction to R language, community and packages• Data analysis fundamentals: mathematical expectations, standard deviation, 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 16Introduction 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

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 good understanding of programming and statistics are expected for students attending this module and programming classwork will be included.

TARGET PARTICIPANTS

Module 1-4Non-technical senior management, divisional or regional levels of marketing, customer relationship management, sales, e-commerce, business leaders, logistics and supply chain management, heads of department, and entrepreneurs.

Module 5IT professionals, data analysts or anyone with a solid programming background and would like to update their knowledge of the programming languages of R and Python.

ENTRY REQUIREMENTS

ASSESSMENT

AWARD

MEDIUM OF INSTRUCTION

Module 1-4The programme will be assessed by a group presentation.

Module 1-3 / Module 5No assessment is required.

To obtain course details including course fee, programme schedule and application procedure, please refer to the separate application form

FOR MORE DETAILShttp://course.centennialcollege.hku.hk

Module 5At least two years of IT programming experience and a basic understanding of statistics.

Module 1-41. A recognised degree in any discipline; or2. A recognised Associate Degree/ a Higher Diploma or equivalent, and at least 2 years of work experience; or 3. Professional qualifications.

Module 1-4An “Executive Certificate in Big Data & Analytics for Business” will be awarded to students who attend 70% of the class and pass the group presentation assessment at the end of the course.

Module 1-3 / Module 5A Statement of Attendance will be awarded to students who have achieved full attendance of the course in Module 1-3 / Module 5.

Module 1-4The programme will be delivered in Cantonese, and the course materials will be in English. Unit 3 conducted by Dr. Efstratios Tsougenis and some other guest speakers’ sessions will be taughtin English.

Module 5The programme will be delivered in Cantonese, and the course materials will be in English.

Applicants with other qualifications and substantial senior level work experience will be considered on their individual merits.

LECTURERS

Centennial College, Room 310, 3 Wah Lam Path, Pokfulam Road, Hong Kong

Tel: 3762 6188 E-mail: course@centennialcollege.hku.hk Fax: 2551 2130 Website: http://course.centennialcollege.hku.hk

* Lecturers and guest speakers are subject to change based on availability.

MS WALLIS CHAN (UNIT 9-11)

Wallis is a Managing Director of Radica Systems Limited. She actively promotes data-driven e-marketing practice with high ROI especially for clients from the luxury brand, retail, hospitality, and entertainment sectors in the past 10 years.

Titania has over 25 years of experience in Big Data & Analytics. She was a Director of Consumer Analytics in an international information services provider, dedicating to providing consumer analytics/consulting services and championing segmentation /data product for a wide range of clients in Asia Pacific.

MS TITANIA KWAN (UNIT 9-11)

Sharon has been working in the analytical area for years with special focus on big data application and business intelligence. She has collaborated with various industries including finance, luxury retail and real estate and public sector for their HR, marketing, finance analytics. Sharon owns a master degree in marketing from the Chinese University of Hong Kong.

MS SHARON LIANG (UNIT 6&7)

Ivan is one of the earliest certified Big Data experts in Hong Kong and has delivered over 50+ Big Data related training sessions for over 1,000 participants since 2012. Participants include OGCIO, Hong Kong Monetary Authority, Education Bureau and AXA Insurance. He is also a Founder of Lively Impact and the Chief Technology O�cer of Radica Systems Limited.

MR IVAN NG (UNIT 4,5&8)

DR EFSTRATIOS TSOUGENIS (UNIT 3)

Efstratios is an experienced A.I. engineer and entrepreneur, co-founder of Toloscope that provides IoT and Video Analytics solutions for the retail industry. He was involved in a number of medical projects including HEp2 image analysis for autoimmune diseases by using A.I., skin collagen quantification and stem cell video analysis.

Francis is a Founder and Chairman of Radica Systems Limited and the CEO of PopSquare.io. He is also the Chief Designer of Data Studio at Hong Kong Science Park. Being a veteran in the industry, Francis is a regular author and speaker on Big Data topics, including commercial, governmental and academic sectors.

MR FRANCIS KWOK (UNIT 1-2)

MR BRIAN TSANG (UNIT 12-16)

Brian is a Data Analytics Consultant at Radica Systems Limited and an active data scientist on Kaggle, a global crowdsourcing platform for predictive modeling and Big Data analytics competition since 2010. He has over 5 years of professional training experience on course content design and development for subjects in information, communications and technology.