Statistical Models
for the Internet of Things

Dates: 22 Feb, 23 Feb 2024 | 9am-5.30pm | Classroom Learning 

Duration: 2 Days

Course Overview

In this course, learners will look at the various statistical methods that can be used to process Internet of Things (IoT) data in order to make predictions or to make diagnoses on the data. Topics covered include Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA) models, simple linear regression, multivariate linear regression, nonlinear regression, Bayesian models, Decision Trees and Support Vector Machines.

This course is part of Professional Certificate in Deep Learning for IoT.

Course Objectives

By the end of this course, learners will be able to apply simple statistical methods to analyse and understand IoT data to make predictions and diagnoses.

Who Should Attend

IoT, Backend or Blockchain Engineer

Prerequisites

Bachelor’s Degree in Computing or Mathematics, with programming knowledge. Other Bachelor’s Degree holders with programming knowledge may be considered. Learners should have taken the set of three courses for the Professional Certification in Cloud Technologies for the Internet of Things.

Course Convener

(Click their photos to view their short biographies)

ddddd Colin Tan Keng Yan

Dr Colin Tan Keng YanDr Colin Tan Keng Yan

Course Fees

Singapore Citizens
39 years old or younger
40 years old or older
Singapore PRs
Enhanced Training Support for SMEs
International Participants

Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes

GST shall apply at prevailing rates.

Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding. 

This course is eligible for Union Training Assistance Programme (UTAP). NTUC members can enjoy up to 50% funding (capped at $250 per year) under UTAP. NTUC members aged 40 and above can enjoy higher funding support up to $500 per individual each year, capped at 50% of unfunded course fees, for courses attended between 1 July 2020 to 31 December 2025. Please click here for more information.

To enquire, email soc-ace@nus.edu.sg

To register, click Register

Course Codes
TGS-2022011563 (Classroom Learning) 
TGS-022013108 (Synchronous e-learning)

Course Fee Breakdown

Singapore Citizens

Singapore Citizens

39 years old or younger

Singapore Citizen

40 years old or older
Singapore PRs
Enhanced Training Support for SMEs
International Participants

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