SGUS Specialist Diploma in Data Science & Analytics

Introduction

Data is ubiquitous in government and in industry sectors including banking, insurance, healthcare, telecommunications, design and manufacturing, and retail. The need to handle, prepare, analyse and model data of varied structures is prevalent in the modern day industrial setting. This course provides graduates with fundamental skills in statistics, data mining and predictive analytics that are required by jobs in these industries. Such jobs involve extracting, cleaning, managing, analysing and modelling data that is useful to the business.

The specific objectives of the SGUS Specialist Diploma in Data Science and Analytics are to provide training in the fundamentals of statistics and programming for data science, as well as in specialised skills in the areas of data mining, applied statistical methods, statistical modelling and prediction. Graduates of the course will be trained in preparing data, summarising and presenting data, performing statistical analysis of univariate and multivariate data, using descriptive models to uncover patterns in data, developing, applying and deploying predictive models and quantifying risks associated with prediction.

Trainees are required to equip with a computer Notebook (installed with minimally windows10 OS) when attending lessons.


Course Objectives

The objective of the programme is to provide foundational training in the fundamental concepts and methods in statistics and programming as well as in specialized skills in specific domains in data science such as Data Analytics, and Statistical Data Modelling


Suitable for

People who want a career in Data Science, Business analysts, Business executives, people who want to learn the fundamentals of statistical analysis, data wrangling, visualization, model building, prediction and error-quantification in model predictions.


Industry and Career Options

With the skills and knowledge gained from this programme, participants will be able to take on new challenges in data analytics and statistics-related work which is critical to the fast growing data-driven economy of Singapore and to the fast changing industry-landscape here.

Career options include:
• Data Analysts
• Business Analyst
• Manufacturing and Design Engineers
• Researchers working with data


Minimum Entry Requirements

Applicant must be a Singapore Citizen or Singapore Permanent Resident aged 21 and above, and is able to commit to the full-time training schedule.

In addition, the target trainees for SGUS programmes will exclude the following:

a) Graduated or graduating in Calendar Year (CY) 2019 / 2020 from ITE, Polytechnics, Universities, and other educational institutions. This includes graduates from private universities as well as overseas institutions and part-time / post-graduate programmes; and

b) Graduated from the educational institutions mentioned in (a) and completed or completing their National Service (NS) in CY 2019 / 2020 and are entering the labour force.

Applicants applying to be enrolled in this programme must satisfy at least one of the following entry requirements:

1) An engineering diploma from a polytechnic in Singapore,

2) Any diploma from a polytechnic in Singapore and

  • a C6 or better in Additional Mathematics in the O-level examinations; or 
  • a C grade or better in a one-semester polytechnic mathematics module that substantially covers the Additional Mathematics syllabus or 
  • relevant work experience (considered on a case-by-case basis).

3) Any degree from one of the following Singapore universities: NUS, NTU, SMU, SUTD, SIT, or SUSS. Candidates with degrees from other universities will be considered on a case-by-case basis.

Recognition of Prior Learning

Applicants who do not meet the entry requirements may be considered for admission to the course based on evidence of at least 5 years of relevant working experience or supporting evidence of competency readiness. Suitable applicants who are shortlisted may have to go through an interview and/or entrance test. The Polytechnic reserves the right to shortlist and admit applicants.


Course Outline

The Programme consists of seven (7) certificates to be conducted within 12 months. Classes will be conducted in full-time mode. 

1. Certificate in Communication and Interpersonal Skills (1 month)
This certificate covers the key communication areas which include mindset transformation, emotional intelligence (EQ), communications between generations and building digital confidence in the workplace.

2. Certificate in Introduction to Analytics, Cyber Security, AI and Blockchain (1 month)
This certificate introduces key concepts in the areas of data and visual analytics using Power BI, statistics, cyber security, blockchain, AI and machine learning.

3. Certificate in Essential and Emerging Skills for Employability (1 month)
This certificate covers fundamentals in various essential areas within an organisation, which includes human resource, finance, design thinking, robotic process automation and digital marketing.

4. Certificate in Fundamentals of Data Science (2 months)
To provide training in the fundamentals of statistics and programming for data science.

5. Certificate in Data Analytics (2 months)
To provide training on specialised skills in the area of data mining and applied statistical methods. 

6. Certificate in Statistical Data Modelling (2 months)
To provide training on specialised skills in the area of modelling of data of varied statistical structures for the purpose of performing predictive analytics.

7. Practicum (3 months)
This project module provides opportunities for learners embark on an industry project either in-house or in an attachment to a company or at the Digital Building Innovation Centre (DBIC).

Please click here for topic synopsis.

*Certificates may not be conducted according to the sequence listed.


Award / Certification / Accreditation

• Upon completion of the modules in each certificate (certificate 1-3, 6-7), trainees will be awarded a Certificate of Completion
• Upon completion of all modules in certificate 4 and 5, trainees will be awarded with the Post-Diploma Certificates (PDC) respectively. Upon successfully acquiring these 2 PDCs, trainees will be conferred with a Specialist Diploma in Data Science (Data Analytics) from SP
• Upon completion of all the certificates, trainees will be awarded with a Certificate in SGUS Specialist Diploma in Data Science & Analytics from SP.


Please refer here for the FAQ on SGUS programme.

Course Fees

Full Fees (inclusive of GST): $25,735.64

Subsidised Fees for Singaporeans & Singapore PR (inclusive of GST): $1,000.00

Trainees must fulfil minimum attendance requirements and pass assessments to qualify for course fee subsidies. Trainees who are unable to meet these requirements may be asked to return the course fee subsidies that they have received.

Refund Policy:

• The programmes allow trainees to exit without penalty if they are successfully placed into a job or have secured a job on their own accord while undergoing training. However, trainees who exit the programme without a valid reason may be asked to return the course fee subsidy that they have received.
• Refund of paid course fees will be pro-rated based on certificates that have not commenced
• Trainees who wish to exit from the programme must write to ptenquiry@sp.edu.sg

Register Here

Please refer to the application procedure

Eligible for SkillsFuture Credit
Terms and Conditions

Date of Course Commencement:

To be advised

Registration Period:

To be advised

*Registration may be closed earlier once seats are filled up. 

Class Schedule:
Mon-Fri, 9 am to 6 pm (at least 15 hours or more per week)

The following modules will be conducted in the evening (6 - 9 pm):
- Introduction to Statistics for Data Science
- Data Mining Techniques
- Applied Statistical Methods
- Building Linear Statistical Models
- Building Time Series and Non-Linear Statistical Models  

Lessons will be conducted in online synchronous/asynchronous or face-to-face mode

Duration:
765 hours (1 year)

Enquiries
Email: ptenquiry@sp.edu.sg

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