Advanced Diploma in Data and Predictive Analytics in R Programming
- Analytics & Tech
- Innovation & Business Improvement
Please refer to respective modules for dates.
Weeknights (7pm - 10:30pm)
Saturday (9am - 6pm)
Who Should Attend
- Managers, Data Analysts, Professionals, Executives involved in the analysis, interpretation and presentation of data for decision-making across various business functions such as marketing, customer service, and corporate communications.
- Data Scientists who are familiar with basics in R programming and want to learn how to perform web scraping from multiple webpages using packages in R.
The course is intended to advance the knowledge and expertise of working professionals who already possess the fundamentals of R programming and data analytics, along with a basic understanding of statistical and causal inference modelling (modelling for explanation). - The course is intended to advance such expertise, using both base R and tidyverse ways of R programming for cutting-edge practices, to predictive models and machine learning.
- Participants who wish to advance their expertise in predictive models and machine learning, along with productivity tools for data science (e.g., creating and maintaining GitHub and interactive dashboards), beyond the cornerstone knowledge and expertise on data analytics and visualisation, will find it a great fit and will learn the most on this course.
- Targeted Job Roles: Data analyst, Statisticians, Data scientist, Data Architect, Quantitative Analysis with R, System Intelligence Manager, Trading analyst
PREREQUISITES
- Functional Laptop
- CPU must be of at least intel core I3;
- GPU must have an integrated graphics card and;
- RAM must be of at least 4GB
Overview
In this programme, participants will be equipped with practical and tangible skills in applying analytics techniques for deriving insights and predictive models from data, as well as acquiring a comprehensive knowledge of statistical thinking and data analysis using R language. As participants explore statistical inference, modelling, and reproducing analysis reports, they will also learn how to interpret and evaluate data-driven decisions for real-world applications.
Learning Objectives
- Hands-on R programming meets lessons in statistical thinking
- Analysing datasets that hold real-world implications
- Data visualisation and storytelling—beyond the “Data Speaks for Itself” approach
- Capstone project based on participants’ professional interest
- Equip with the fundamental concepts and systemic framework (workflow) of supervised machine learning
- Learn the differences between solving regression and classification problems in the workflow of predictive models
- Learn the essential concepts of dimensionality reduction using Tidymodels framework in the R data science ecosystem
- Learn the core concepts of K-means and hierarchical clustering and differentiate them from dimensionality reduction
- Learn about supervised and unsupervised learning to text data and executing predictive models when given prediction tasks dealing with text
- Learn how to store and deploy models and algorithms, using an interactive dashboard, web API, and Github
Topic/Structure
Certified Data Analytics (R) Specialist
- Introduction To Data Analytics (using R programming)
- Introduction To Data Visualisation (using R programming)
- Web Scraping and Data Insights (using R programming)
- Statistical Inference for Managerial Insights (using R programming)
- A First Look at Visual Analytics (using ggplot2 packages in R)
- Advancing Skillsets Of Visual Analytics (Using Ggplot2 Packages In R)
Predictive Analytics and Machine Learning
- Predictive Modelling for Numerical Data
- Predictive Modelling for Categorical Data
- Dimensionality Reduction
- K-Means and Hierarchical Clustering
- Text Classification and Topic Modelling
- Shiny and Machine Learning in Production
Assessment
Graduation Requirement
The Advanced Diploma qualification will be conferred to participants who successfully complete both Certification programmes within 3 years of commencing the first certificate.
Calculate Programme Fee
Fee Table
| COMPANY-SPONSORED | |||
|
PARTICIPANT PROFILE |
SELF-SPONSORED |
SME |
NON-SME |
|
Singapore Citizen < 40 years old Permanent Resident LTVP+
|
$6,278.40 (After SSG Funding 70%) |
$2,438.40 (After SSG Funding 70% |
$6,278.40 (After SSG Funding 70%) |
|
Singapore Citizen ≥ 40 years old |
$2,438.40 (After SSG Funding 70% |
$2,438.40 (After SSG Funding 70% |
$2,438.40 (After SSG Funding 70% |
|
International Participant |
$20,928 (No Funding) |
$20,928 (No Funding) |
$20,928 (No Funding) |
All prices include 9% GST
Please note that the programme fees are subject to change without prior notice.
Post Secondary Education Account (PSEA)
PSEA can be utilised for subsidised programmes eligible for SkillsFuture Credit support. Click here to find out more.
Self Sponsored
SkillsFuture Credit
Singapore Citizens aged 25 and above may use their SkillsFuture Credits to pay for the course fees. The credits may be used on top of existing course fee funding.
This is only applicable to self-sponsored participants. Application to utilise SkillsFuture Credits can be submitted when making payment for the course via the SMU Academy TMS Portal, and can only be made within 60 days of course start date.
Please click here for more information on the SkillsFuture Credit. For help in submitting an SFC claim, you may wish to refer to our step-by-step guide on claiming SkillsFuture Credits (Individual).Workfare Skills Support Scheme
From 1 July 2023, the Workfare Skills Support (WSS) scheme has been enhanced. Please click here for more details.
Company Sponsored
Enhanced Training Support for SMEs (ETSS)
- Organisation must be registered or incorporated in Singapore
- Employment size of not more than 200 or with annual sales turnover of not more than $100 million
- Trainees must be hired in accordance with the Employment Act and fully sponsored by their employers for the course
- Trainees must be Singapore Citizens or Singapore Permanent Residents
- Trainees must not be a full-time national serviceman
- Trainees will be able to enjoy ETSS funding only if the company's SME's status has been approved. To verify your SME's status, please click here.
Please click here for more information on ETSS.
Absentee Payroll
Companies who sponsor their employees for the course may apply for Absentee Payroll here. For more information, please refer to:
AP Guide (Non-SME Companies)
Declaration Guide (SME Companies)
Intake Information
Certified Data Analytics (R) Specialist
| Modules | Intake 27 | Intake 28 |
|---|---|---|
| Introduction to Data Analytics (using R Programming) | 31 Jan, 1 & 3 Feb 2024 [Registration Closed] | 26, 27 & 29 Jun 2024 [Registration Closed] |
| Introduction to Data Visualisation (using R programming) | 28, 29 Feb & 2 Mar 2024 [Registration Closed] | 24, 25 & 27 Jul 2024 [Registration Closed] |
| Web Scraping and Data Insights (using R programming) | 20, 21 & 23 Mar 2024 [Registration Closed] | 14, 15 & 17 Aug 2024 [Registration Closed] |
| Statistical Inference for Managerial Insights (using R programming) | 8, 11 & 13 Apr 2024 [Registration Closed] | 4, 5 & 7 Sep 2024 [Registration Closed] |
| A First Look at Visual Analytics (using ggplot2 packages in R) | 8, 9 & 11 May 2024 [Registration Closed] | 25, 26 & 28 Sep 2024 [Registration Closed] |
| Advancing Skillsets Of Visual Analytics (Using ggplot2 Packages In R) | 5, 6 & 8 Jun 2024 [Registration Closed] | 28, 30 Oct & 2 Nov 2024 [Registration Closed] |
Predictive Analytics and Machine Learning
| Modules | Next Intake |
|---|---|
| Module 1: Predictive Modeling for Numerical Data | To be advised |
| Module 2: Predictive Modeling for Categorical Data | To be advised |
| Module 3: Dimensionality Reduction | To be advised |
| Module 4: K-Means and Hierarchical Clustering | To be advised |
| Module 5: Text Classification and Topic Modeling | To be advised |
| Module 6: Shiny and Machine Learning in Production | To be advised |
