Key Takeaways
At the end of the course, the participants will be able to:
- Structure Big Data Projects & Data Readiness Evaluation
- Understand the pre and post-data collection perspective of a Big Data project for analytics.
- Design a suitable strategy for harnessing Big Data for analytics to create business value.
- Classify and contextualise different tools and solutions to deal with the technical landscape.
- Understand the importance of data quality and the importance of pilot programs to check data and business users' readiness.
- Data Pipeline Designing & Feature Engineering
- Understand the importance and need of data pipelines, their components, and how to organise data pipeline components to automate end-to-end data flow.
- Understand the domain-specific variation needed for feature engineering and model development.
- Develop Models & Plan Model Aggregation, and Monitoring
- Some best practices for model development, and monitoring
- Model aggregation for efficient deployment in different scenarios
Who Should Attend?
This course has been designed for analytics professionals and managers seeking to learn about structuring a big data project (in any domain), getting an overview of the end to the end development cycle, and analytics model development, aggregation & monitoring.
It will be useful for the following roles who are specializing in Big Data Space:
- Data Analysts
- Data Scientist
- Data Engineers
- Data Product Managers
Prerequisites
Participants who have successfully completed Big Data Engineering for Analytics or have equivalent knowledge.
Python knowledge is strongly desirable, participants with no prior Python knowledge is encouraged to attend Python for Data, Ops and Things course.
What to Bring
No printed copies of course materials are issued.
Participants must bring their internet-enabled computing device (laptops, tablet etc) with power charger to access and download course materials.
If you are bringing a laptop, please see below for the tech specs:
|
|
Minimum
|
Recommended
|
|
Operating Systems
|
• Windows 7, 8, 10 or
• Mac OS
|
Laptop running the latest
version of either Windows or
Mac OS
|
|
System Type
|
32-bit
|
64-bit
|
|
Memory
|
8 GB RAM
|
16+ GB RAM
|
|
Hard Drive
|
256 GB disk size
|
|
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Others
|
• An internet connection – broadband wired or wireless
• Installation permissions (non-company laptops)
• Keyboard
• Mouse/Trackpad
• Display
• Power adapter (laptop battery might run out) |
DirectX 10 graphics card for graphics hardware acceleration
|
What Will Be Covered
This course will cover :
- Structuring Big Data Project for Analytics
- Design a strategy to harness big data
- Data Readiness Evaluation
- Data pipelines and their importance
- Feature Engineering
- Model Development & Monitoring
- Model Aggregation
Fees & Subsidies
Fees for 2024
| |
Full Fee |
Singaporeans & PRs
(self-sponsored) |
| Full course fee |
S$2700 |
S$2700 |
| ISS Subsidy |
- |
(S$270) |
| Nett course fee |
S$2700 |
S$2430 |
| 9% GST on nett course fee |
S$243 |
S$218.70 |
| Total nett course fee payable, including GST |
S$2943 |
S$2648.70 |
Note:
- All fees and subsidies are valid from January 2024, unless otherwise advised.
- All self-sponsored Singaporeans aged 25 and above can use their SkillsFuture Credit to pay for course fees. For more information about SkillsFuture Credit, click here.
- From 1st January 2024, the GST will be increased to 9%.
Certificate
Certificate of Completion
Participants have to meet a minimum attendance rate of 75% and are required to pass the assessment to be issued a Certificate of Completion.
Preparing for Your Course
NUS-ISS Course Registration Terms and Conditions
Find out more.
NUS-ISS and Learner’s Commitment and Responsibilities
Find out more.
WIFI Access
WIFI access will be made available to participants.
Venue
NUS-ISS
25 Heng Mui Keng Terrace
Singapore 119615
Click HERE for directions to NUS-ISS
In the event of a change of venue, participants are advised to refer to the acceptance email sent one week prior to the commencement date.
Course Confirmation
All classes are subject to confirmation and NUS-ISS will send an acceptance email to participants one week prior to the commencement date. Confirmed registrants are to attend and complete all lectures, class exercises, workshops and assessments (where applicable). Additionally, all responses to feedbacks and surveys conducted by NUS-ISS and its partners must be submitted. All training and assessments will be delivered as described in the course webpage.
General Enquiry
Please feel free to write to ask-iss@nus.edu.sg if you have any enquiry or feedback.