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Data Analytics and Visualisation for IT Managers

Course Reference No: CRS-N-0047106

Course Objectives
IT managers Project Managers and Engineers who are managing information technology and computer systems in their organisations have to play the lead role in shaping the direction and planning the strategies on data infrastructure, platform, process and operations. 

At the end of the programme, you will be able to:
• make better decisions
• understand data infrastructure and platform, data science techniques and tools, and data analytics    project management,
• optimise resources to achieve the best results.

Outline
Day 1
Overview of Big Data, Data Science, Machine Learning and Artificial Intelligence    
    - Introduction to Artificial Intelligence and Deep Learning; 
    - Unsupervised Learning;
    - Supervised Learning.

The Data Analytics Process    
    - Discussion of Analytics Use Cases.

Hands-on: The Data Analytics Process (Part 1)    
    - Introduction to the Orange data Analytics Toolkit; 
    - Data Preparation;
    - Data Exploration and Understanding;
    - Model Building.

Hands-on: The Data Analytics Process (Part 2)    
    - Test, Score and Model Selection;
    - Validation;
    - Sharing and deployment of your models.

Day 2
Data Analytics Infrastructure and Tools (Part 1)    
    - The Data Analytics Stack; 
    - Open Source and Analytics;
    - Discussion on R and Python – the standard tools for open source analytics today;
    - Visualization and Storytelling;
    - Data Analytics Infrastructure and Tools;    
    - Data Model Deployment Best Practices.

Data Analytics Infrastructure and Tools (Part 2)        
    - The Case for Analytics in the Cloud (Public or On Premise) 
    - Reproducible Data Science
    - The Data Science Team
    - Data Analytics Project Management and Setup
    - Discussion on Setting up a Centre of Excellence for Analytics
    - Privacy, Ethics and Biases in Data Science

Data Analytics Project Best Practices

Project Scoping Workshop

Day 3
Data Visualisation Workshop (Part 1)        
    - Recap and Comparison of Analytics Tools: 
    - Excel, Microsoft Power BI* and Python;
    - A Data Journey with Excel.

Data Visualisation Workshop (Part 2)    
    - A Data Visualization Journey with Microsoft Power BI*

A Data Science Journey with Python

Case Study

Tools
Orange, Excel, Microsoft Power BI* and Python

Pre-requisites
Participants should have enterprise IT knowledge, some programming and basic statistics experience. 

Who Should Attend
IT Managers, Project Managers and Engineers

Mode of Training
Classroom



Data Analytics Begins With Me
Mobirise

Guo Lei

Dr Guo Lei is an active researcher and educator in behavioural research, data analytics, and service innovation, with international experience in delivering applied research and practice-based learning programmes with successful results. She has particular experience in tackling complex challenges through design thinking, business analytics and strategic communications. Dr Guo is also an accomplished author, having publications in both academic and professional domains. She has worked with clients in sectors such as transport, public service, banking, retail, gaming, support services and infocomm. Previous clients include Land Transport Authority of Singapore, Singapore Public Transport Council, Singapore National Library Board, Info-communications Development Authority of Singapore, United Overseas Bank, Bank of China, China Agricultural Bank, Club 21, CapitaLand, Singapore Pools, UK BAE Systems, China Mobile, and Wuxi Municipal Government China. 

Mobirise

Koo Ping Shung

Mr Koo Ping Shung is an experienced Data Scientist with 13+ years of relevant experience. He is also an Adjunct Senior Faculty with Singapore University of Social Sciences (SUSS). He is a seasoned trainer, having conducted over 1,600 man-hours of data science training and is the mentor to trainees in the IMDA-SAS BIA Programme for 5 intakes. Ping Shung was a guest lecturer at NUS Institute of Systems Science and previously held an adjunct position in School of Information Systems, Singapore Management University. Previously, he was the Analytics Practicum Manager at SMU, SIS. He often advised companies on the data analytics projects they can work on and is a co-supervisor for many Masters students on their projects. He was an instructor in DBS Graduate Associate Programme, training 200+ Graduate Associates on analytics. Ping Shung was a facilitator for the IDA Data Science MOOC programme and participated in Data Literacy Bootcamp, co-organised by the IDA Singapore and The World Bank. Ping Shung has gathered much experience on analytics, from data collection, machine learning to presentation of insights. His analytics experience range from a variety of business functions and industries.
His strong passion in data science can be seen, being a Co-founder of DataScience.Sg and former Working Committee Chairman of SAS User Group Singapore. His research interest lies in how data science can help organizations be more efficient and effective. Ping Shung holds an MBA from University of Adelaide and obtained his bachelor degree in Economics from NUS, minor in Computational Finance. 

Mobirise

Julian Lin

Dr Julian Lin is a Senior Lecturer in Cybersecurity and Data Analytics with School of Continuing and Lifelong Education (SCALE) at the National University of Singapore (NUS). He is a Certified Information Systems Security Professional (CISSP) and has a dozen other IT certifications. Recently, Julian was ranked 6th in the Microsoft Data Science Capstone Competition. He has been conducting text-analytics research since 2010 and teaching visualization since 2007. During his IT consultancy career, he oversaw the application and infrastructure projects in Amoseas, Mannesmann Dematic Colby (Sydney), Alcatel (Sydney), and the University of New South Wales (UNSW) Faculty of Commerce and Economics. Julian has taught programming and data management for business at UNSW. At NUS, he had mentored student software development projects and taught visual communications, designing new media content, and research methods. He was given a teaching award for visualization class and research award for user acceptance research in NUS. Julian obtained a PhD degree in Information Systems from NUS. He was a recipient of two scholarships from the Overseas Chinese Association in Taiwan while pursuing his Bachelor degree and another scholarship while pursuing a Master degree in Australia. 

Date

8 - 10 May 2019

Duration

3 Days
9.00am to 5.30pm
(Daily)

Venue

National University of Singapore
University Town

International Participants




S$ 2568.00

  • Incl. GST

Singapore Citizens
(39 yrs old or younger) 
or Singapore PRs


S$ 770.40

  • Incl. GST

Singapore Citizens
(40 yrs or older)




S$ 290.40

  • Incl. GST

Enchanced Training Support for SMEs




S$ 290.40

  • Incl. GST

Fees & Fundings

International 
Participant

Singapore Citizen1
39 years old or younger
Singapore Citizen1
40 years or older eligible for MCES2
Singapore
Citizen1 eligible
for WTS3
Singapore PRs 

Enhanced Training
Support for SMEs4
Full Programme FeeS$2,400.00S$2,400.00S$2,400.00S$2,400.00S$2,400.00S$2,400.00
SkillsFuture Funding
Eligible for Claim Period
(25 May 2018 - 30 Sep 2020)
-(S$1,680.00)(S$1,680.00)(S$1,680.00)(S$1,680.00)(S$1,680.00)
Nett Programme FeeS$2,400.00S$720.00S$720.00S$720.00S$720.00S$720.00
7% GST on Nett
Programme Fee
S$168.00S$50.40S$50.40S$50.40S$50.40S$50.40
Total Nett Programme
Fee Payable, Incl. GST
S$2,568.00S$770.40S$770.40S$770.40S$770.40S$770.40
Less Additional Funding if
Eligible Under Various Scheme
--(S$480.00)(S$600.00)-(S$480.00)
Total Nett Programme Fee, Incl. GST,
after additional funding from the various funding schemes 
S$2,568.00S$770.40S$290.40S$170.40S$770.40S$290.40

1 All self-sponsored Singaporeans aged 25 and above can use their $500 SkillsFuture Credit to pay for the programme. Visit http://www.skillsfuture.sg/credit to select the programme.
2 Mid-Career Enhanced Subsidy (MCES) - Singaporeans aged 40 and above may enjoy subsidies up to 90% of the programme fee.
3 Workfare Training Support (WTS) - Singaporeans aged 35 and above (13 years and above for persons With disabilities) and earn not more than S$2,000 per month, may enjoy subsidies up to 95% of the programme fee.
4 Enhanced Training Support for SMEs (ETSS) - SME-sponsored employees (Singaporean Citizens and PRs) may enjoy subsidies up to 90% of the programme fee. For more information, visit http://www.ssg.gov.sg/programmes-and-initiatives/training/enhanced-training-support-for-smes.html?_ga=2.154478072.1748789781.1519700056-512306731.1519700056
5 Eligible organisations (excluding government entities) may apply for the absentee payroll funding via SkillsConnect at www.skillsconnect.gov.sg for Singaporean/permanent resident participants attending the programme during working hours. The absentee payroll funding is computed at 80% of hourly basic salary capped at $4.50 per hour or $7.50 per hour for SME or 95% of hourly basic salary for WTS. For more information, visit https://www.skillsconnect.gov.sg/sop/portal/e-Services/For%20Employers/AbsenteePayroll.jsp

Ver: 88160119

Registration will close 5 working days prior to programme commencement date