Run 1: 01 June 2020 - cancelled due to “circuit breaker”
Run 2: 20 Jul 2020 - Full
Run 3: 23 Jul 2020
Run 1: 25 Mar - 17 May 2020
Run 2: 25 Mar - 05 Jul 2020
8 hours / 1 day
8:30am to 5:30pm
Mode of Training:
This course introduces the fundamentals of data analytics and various tools such as data wrangling, data visualisation and data analytics which is one of the enablers of industry 4.0 to improve operational efficiency and business processes.
The objective of this course is to equip participants with knowledge of fundamentals of data analytics. Participants will also be able to apply these analysis tools to their data when designing and developing their future intelligent systems for the electronics & semiconductor industries. There would be hands-on session with the data analysis tools such as data wrangling, visualisations, regression models and prediction. Participants can apply the knowledge and skills to help improve their operational tasks and increase work productivity.
This course consists of 8 hours of lectures and practical.
MODULE 1: Introduction to data analytics and data wrangling
Introduction to the needs for data analytics in electronics industry and big data analytics.
Introduction to data wrangling and its application on data for the electronics industry.
Participants will be introduced to the needs of data analysis and various big data aspects and data wrangling, followed by a practical session on transforming and mapping raw data sets for further visualisation and analysis.
MODULE 2: Data Visualization and Unsupervised Data Analytic Techniques
Introduction to techniques for data visualization and unsupervised data analysis and their application on data.
Participants will be introduced to graphical visualisation methods and data analysis techniques of clustering and forecasting. They will also apply these techniques on data and will present the results in various graphical formats on an interactive dashboard to gain meaningful insights for process, equipment or quality control in manufacturing.
MODULE 3: Supervised Data Analytic Techniques
Introduction to supervised data analytic techniques and their application on data.
Participants will apply data analysis techniques of linear regression, logistic regression and correlation on data and present the results in various graphical formats on an interactive dashboard to gain meaningful insights for manufacturing.
Discussion on applications of data analytics in electronics & semicon industry.
All engineering technical or personnel.
Minimum Entry Requirements / Assumed Skills and Knowledge
Engineering or manufacturing background. Preferred to have at least an ITE level or above or at least a year of work experience in a technical role.
Award / Certification / Accreditation
• Certificate of Attendance (electronic Certificate will be issued)
A Certificate of Attendance will be awarded to participants who meet at least 75% attendance rate
• Certificate of Performance (electronic Certificate will be issued)
A Certificate of Performance will be awarded to participants who pass the assessment and meet at least 75% attendance rate
*Please note that once the maximum class size is reached, the online registration will be closed.
*Participants would need a pc or laptop with Microsoft windows 8 or 10
1. All applications must be made via Online Registration at www.pace.sp.edu.sg
Course fees can be paid by the following payment modes:
a) Credit Cards, Internet Banking, NETS (Not Applicable for company sponsored)
For e-payment using Visa/Master cards and Internet Banking, please click on the ‘Make e-Payment’ button on the acknowledgement page to proceed.
b) For NETS payment, you can pay at:
Please make cheques payable to “Singapore Polytechnic”. Do cross the cheque and write the Registration Reference ID, Applicant Name and NRIC/FIN number at the back of the cheque. When you have completed required
details, you may mail the cheque here.
Please note that an administrative charge of $15 will be imposed for any returned cheques from the bank or financial institution.
2. All successful applicants will be notified with a letter of confirmation via email.