Engineering Data Analytics & Machine Learning

This course will equip participants with the knowledge and skills to harness the intelligence that huge amounts of data from smart and connected systems provide.  Participants will learn the various stages of analytics, right from gathering and cleaning data to visualizing and analyzing it to provide actionable recommendations towards system improvement. Participants will also get exposed to the emerging trends of machine learning that is made possible by the availability of big data and increasing processing speeds.  They will get to appreciate the impact that machine learning can have on industrial process / product /system improvements through case studies. 

Who Should Attend

Process Engineers, Product Test Engineers, Integration Engineers, Equipment Engineers and Individuals who  require  IIOT Core Skills to manage and analyse data (Data Analytics, System Integration).

For course details, click here.

Course Outline

  • The essential of Data Analytics
  • The Data Analytics Framework
  • Statistical theory and concepts
  • Visualization of the data
  • Introduction to machine learning

 

Certification

Participants will be issued with a Certificate of Accomplishment upon meeting 75% of the required course attendance.

Trainers' Profile

The course will be conducted by Lecturers of Temasek Polytechnic School of Engineering who are specialists in the topics covered and are equipped with an expanse of experience in training practitioners in the industry.

Enquiries & Application

Submit on-line application via:
https://bluesky.tp.edu.sg/PTOAS.aspx
(Please select Course Code:  ZB6)

Temasek SkillsFuture Academy
Temasek Polytechnic
East Wing Block 1A, Level 3, Unit 81
21 Tampines Avenue 1
Singapore 529757

Opening Hours
8.30 am to 7.00 pm (Mon to Fri)
Closed on Sat, Sun & Public Holidays

Contact Details
6788 1212
tsa@tp.edu.sg
  http://www.tp.edu.sg/tsa

Temasek Polytechnic reserves the right to alter the course, modify the scale of fee, amend any other information or cancel course with low enrolment.