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 course, 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, Tableau and Python
- A Data Journey with Excel
Data Visualisation Workshop (Part 2)
- A Data Visualization Journey with Tableau
A Data Science Journey with Python
Case Study
Tools
Orange, Excel, Tableau 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
Dr Guo Lei
Dr Guo Lei is an active educator and researcher in data science, behavioural study and design thinking, with extensive experience in delivering practice-based learning programmes and applied research projects with successful results.
Starting her career in Singapore as a marketing practitioner, Dr Guo worked across manufacturing, entertainment and education industries. She was the Chief Representative in China for a Singapore listed company. She was also responsible for setting up Shanghai Office and promoting executive education programmes for NUS Business School in Greater China market.
With the aspiration of bridging the gap between research and practice, Dr Guo pursued her PhD in the UK, where she worked on large-scale research projects with Cambridge University Service Alliance, BAE Systems and China Mobile.
Dr Guo returned to Singapore and joined NUS as a faculty member in 2011. She has particular experience in tackling complex challenges through applied research and education. She was the Principal Investigator for a series of research projects to inform better public transport policy decisions. She consults local and overseas corporate clients on data analytics, user experience design and service innovation. Dr Guo teaches executive programmes of Data Analytics and Design Thinking at NUS. She has a passion for engaging and inspiring working professionals at all levels by applying the theory to real world business applications.
Dr Guo holds a PhD in Marketing from University of Exeter, an MBA from University of Adelaide and a BA in Literature from Beijing Normal University.
Mr Koo Ping Shung
Mr Koo Ping Shung is an experienced Data Scientist with more than 13 years of relevant experience. He is also currently a Adjunct Senior Faculty with the Singapore University of Social Sciences (SUSS) and a SAS Trainer as well. To date, he has conducted over 1,600 man-hours of data science training. He is also the mentor to the trainees accepted into 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.
Prior to this, he was the Analytics Practicum Manager of the Master of IT in Business (Analytics) at the Singapore Management University School of Information Systems. He managed the industrial relationships through projects and attachments. He often advised companies on the type of data analytics projects they can work on with their data and is a co-supervisor for many Masters students on their data analytics capstone projects. He was an instructor for the DBS Graduate Associate Programme for 3 years, teaching over 200 Graduate Associates on data analytics and received positive ratings. He has also trained professionals from various companies on data analytics and the use of SAS software.
Ping Shung was a facilitator for the IDA Data Science MOOC programme for 2 cohorts (over 300 professionals) and participated in Singapore's first Data Literacy Bootcamp which was co-organised by the IDA Singapore and The World Bank.
Ping Shung through his career has gathered much experience on statistical modeling, from working in the banks, supervising Masters students and doing education research. His data analytics experience range from a wide variety of business functions and industries, gathered from talking to companies or working on data analytics projects.
His strong passion in data analytics and data science can be seen through his involvement in data analytics interest groups, being a Co-founder of DataScience.Sg and former Working Committee Chairman of SAS User Group Singapore and Data Ambassador for one of the DataKind SG project. He also read widely on the different topics related to data science and artificial intelligence, keeping himself up-to-date with their development.
His research interest lies in how data science can help organisations and businesses to be more efficient and effective.
Ping Shung holds an MBA from University of Adelaide. He obtained his bachelor degree in Economics from National University of Singapore, with a minor in Computational Finance.