Analytics in the Cloud - Centre for Digital Banking

Overview
Objectives & Outline
Methodology
Participant Profile
Trainer

Overview

PROGRAMME DETAILS
Duration
2 days
TIME
9:00 AM – 5:00 PM
VENUE
Asian Banking School
This programme is an advanced level course in using R for machine learning and data modelling. It has been designed for participants to grasp the concept of machine learning for supervised and un-supervised learning, as well as understand the machine learning model evaluation work flow and participate in the model fitting exercise.
LEARNING LEVEL
Foundation
PROGRAMME FEE
AICB MEMBER
MYR
2,200*
/ PAX
NON-MEMBER
MYR
2,500*
/ PAX
*Subject to 6% Service Tax
Contact us
Objectives & Outline
LEARNING OBJECTIVES
By the end of this programme, participants will be able to:
Combine data from several sources and create visualisations using PowerBI
Scrape data from online sources and create scheduled operations for future analysis using Google Cloud Functions
Scrape documents from online sources and structure tabular data using Google Vision API
Define Customer Lifetime Value algorithm, pre-process data, model selection and interpret results
Create customer service chatbot agents using Dialogflow
PROGRAMME OUTLINE
Operations reporting (data visualisation):
Decide KPIs to measure for consumer banking/commercial banking/wealth management/back office operations
Connect to external database/files
Configure relational database
Construct basic charts
Filter data
Load dashboard into PowerBI cloud

Data mining (web scraping):
Basic web scraping
Organise output into tables/JSON files
Load file and clean data
Load script into AWS Lambda/GCP Cloud Functions for periodic scraping
Data storage and extraction

Data mining (optical character recognition):
Basics of OCR technology
Install and run Tesseract
Pre-process image to improve OCR results
Store results for future extraction
Predictive analytics:
Definition of Customer Lifetime Value, decide which algorithm to use
Pre-process data
Model selection: Classification, Generalised Linear Model (GLM)
Interpret results
Load script into AWS Lambda/GCP Cloud Functions for continuous updates

Interactive agents:
Define scope: customer service/product enquiry etc.
Create conversation flow
Input list of intents and entities
Link chatbot to BigQuery
Measure response accuracy rate
Methodology

Slides presentation, interactive discussions, workshop and experience sharing.
Participant Profile
Data analysts and junior data scientists.

Requirements for the programme are:
Google Cloud Platform account (there will be $300 credits given free for new registrations)
A Google account with access to Google Colab
PowerBI (registered using a work email address or non-personal email address)
Basic knowledge of cloud computing
Trainer

KOH WYHOW
Koh Wyhow is manager of the data science team at Star Media Group Berhad. He focuses on delivering advanced analytics and business intelligence solutions for the organisation like chatbots and image recognition solutions. He has consulted for clients in the airlines, media, property and FMCG industries during his time as a senior consultant at EY’s Data and Analytics team.

His core capability is the ability to leverage data analytics to drive business opportunities in areas of customer relationship management (profitability, retention, upsell/cross sell), digital analytics (online behavioural analytics, DMP), visual analytics and enterprise dashboards.

He was one of the data scientists which implemented strategies to run a national data-driven campaign for INVOKE during the 14th General Elections. As an independent learner, he picked up basic Python programming skills after office hours during his days as a Further Mathematics lecturer at a private college. Wyhow holds a BSc in Mathematics from the National University of Singapore.

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