UNIVERSITY CREDIT RATED QUALIFICATION

Postgraduate Diploma in Data Science

The Postgraduate Diploma Data Science is a fully accredited programme covering the key skills and knowledge needed by data scientists to develop insights and solutions across a wide range of industries and domains.
COURSE MODE AND DURATION
 Mode: Online
Duration: 12 months
COURSE FEES
Course Fees before funding
S$ 18,000
SkillsFuture / PSEA funding to be announced

DUAL CERTIFICATION
1.Postgraduate Diploma in Data Science (TVI)
2. Postgraduate Diploma in Data Science
(Woolf University)
SOME OF THE ORGANISATIONS OUR STUDENTS AND GRADUATES WORK AT

What skills and knowledge will I gain?

The Postgraduate Diploma in Data Science has been developed by practising data scientists who have experience working with major international firms across a wide range of industries. They have identified the key skills required for data scientists and have also ensured that the course content conforms to the Edison European Data Science Framework’s Body of Knowledge (BoK-DS).
This course is credit-rated at the postgraduate level on the European Qualifications Framework and carries 60 ECTS credits. These credits can be transferred to the Institute’s MSc in Data Science and other master's degree programmes.

Key Skills and Knowledge

  • R and Python programming skills
  • Data management
  • Descriptive and inferential statistics
  • Data visualisation
  • Hypothesis testing
  • Analysis of variance
  • Predictive analytics
  • R and Python programming skills
  • Advanced predictive modelling
  • Time Series Analysis
  • Unsupervised methods 
  • Machine learning
  • Text mining and natural language processing

Tools and Languages

What you will learn on the course

You are provided with highly structured and detailed course content, broken down into ten distinct modules  covering core skills and knowledge through our learning management system. While the course is self paced, tutors are on hand to provide support and feedback for your exam prep and project work.
 
* Holders of the Postgraduate Diploma in Data Science can transfer 60 ECTS credits to the MSc in Data Science.
Postgraduate Diploma  Modules
MODULE 1

Exploratory Data Analysis

Most industry analysis starts with exploratory data analysis and a thorough study of this will help you to perform data health checks and provide initial business insights. You will gain a sound understanding of Python and R programming, descriptive statistics, data management and data visualisation. You will also learn SQL for big data pre- processing and prepare data for big data analytics. The module serves as an essential foundation for advanced analytics taught later in the course.
  • Programming basics in Python and R
  • Data management
  • Measures of central tendency and variation
  • Bivariate relationships
  • Data visuallsation
MODULE 2

Statistical Inference

Statistical inference is the process of drawing inferences or conclusions from data using statistical techniques. This is at the core of data science, and a strong understanding of statistics from the beginning is the prime ingredient for a competent data scientist. In this module, you will cover sampling, statistical distribution, hypothesis testing, and variance analysis and use R code to carry out various statistical tests and draw inferences from their output.
  • Principles of statistical inference
  • Parametric tests
  • Non-parametric tests
  • Analysis of variance (ANOVA)
MODULE 3

Fundamentals of Predictive Modelling

Solutions to many business problems are related to successfully predicting future outcomes. This module introduces predictive modelling and provides a foundation for more advanced methods and machine learning. You’ll gain an understanding of the general approach to predictive modelling and then build simple and multiple linear regression models in Python and R and apply these in a range of contexts.
  • Predictive modelling principles
  • LInear regression models
  • Model validation
  • Python and R packages for predictive modelling
MODULE 4

Advanced Predictive Modelling

In this module, you are introduced to model development for categorical dependent variables. Binary dependent variables are encountered in many domains such as risk management, marketing and clinical research and this module covers detailed model building processes. Multinomial and ordinal logisitic regression are also covered.
  • Logistic regression models
  • Survival analysis
  • Cox regression
  • Poisson regression
MODULE 5

Time Series Analysis

In this module, time series forecasting methods are introduced and explored. You will analyse and forecast macroeconomic variables such as GDP and inflation, as well as look at complex financial models using ARCH and GARCH, ARIMA, time series regression, exponential smoothing and other models.
  • Time series concepts
  • Assessing stationarity
  • ARIMA, ARCH, GARCH modelling
  • Panel Data Regression
MODULE 6

Unsupervised Multivariate Methods

Data reduction is a key process in data science and you will learn to apply data reduction methods such as principal component analysis, factor analysis and multidimensional scaling. You will also learn to segment and analyse large data sets using clustering methods, another key analytical technique that brings out rich business insight if carried out skillfully.
  • Principal Component Analysis
  • Factor Analysis
  • Multidimensional Scaling
  • Cluster Analysis
MODULE 7

Machine Learning 1

Machine learning algorithms are new generation algorithms used in conjunction with classical predictive modelling methods. In this Machine Learning 1 module, you will understand applications of the support vector machine, K nearest neighbours and naive bayes algorithms for classification and regression problems using case studies from a range of industries and sectors.
  • Naive Bayes Method 
  • Support Vector Machine Algorithm
  • K nearest neighbours
MODULE 8

Machine Learning 2

The Machine Learning 2 module continues developing your machine learning knowledge and you will cover decision tree, random forest and neural network algorithms for regression and classification, again drawing on case studies from real world data. You will have the opportunity to compare the performance of machine learning algorithms against classical statistical models and learn to assess which are most appropriate for specific scenarios.
  • Decision Tree
  • Random Forest
  • Association Rules
  • Neural Networks
MODULE 9

Text Mining and Natural Language processing

This module looks at analysing unstructured data such as that found social media, newspaper articles, videos and more. In particular you will look at methods for text mining and natural language processing using R and Python code to produce graphical representations of unstructured data and carry out sentiment analysis.
  • Structured vs unstructured data
  • Text mining in R and Python
  • Text mining using ggplot2
  • Sentiment analysis using R and Python
MODULE 10

Data Science in Practice

The Data Science in Practice module provides you with an opportunity to yor apply knowledge through project work. You will select a project from a specific domain and appropriately apply exploratory data analysis, statistical methods and select appropriate advanced modelling techniques. This module also develops your scientific communication skills through the preparation of project reports and presentations.
  • Presentation and communication skills
  • Synthesis of data science knowledge
  • Application to real world data and scenarios

Who is the course for?

The Postgraduate Diploma in Data Science is for professionals and aspiring data experts who want to develop advanced skills in data analysis and machine learning, making it ideal for career advancement or transitioning into data-driven roles.  This includes experienced professionals, managers and leaders, regardless of whether they have an undergraduate degree or not.

Entry Requirements:

Diploma OR 
Advanced Diploma OR
Higher Nitec OR
Undergraduate Degree

In addition to meeting formal entry requirements, students should possess:
- Strong numerical skills. -
- An inquisitive mindset and commitment to learning.
- A keen interest in applying data science and analytics to solve real-world business problems.

Holders of the Professional Certificate in Data Science
Individuals who have completed the Professional Certificate in Data Science or a similar foundational programme in Data Science and Analytics
Career Changers
Professionals transitioning from other fields who want to develop essential skills to succeed in data science and analytics
Practising Data Professionals
Those with analytics experience looking to deepen their skills to advance their careers
Recent Graduates
Graduates of universities, polytechnics and Institutes of Technical Education
Managers and Leaders
Individuals who are serious about leveraging data science and analytics to enhance business performance and make informed strategic decisions.
Upskillers
Those aiming to add data science skills to their existing job roles

Unique Pathway to Internationally recognised Masters in Data Science

The Data Science Institute’s Professional Certificate in Data Science offers a unique pathway to internationally recognized postgraduate qualifications in Data Science—including a Master’s degree—accredited under the European Qualifications Framework. Successful students can join our postgraduate program through Performance-Based Admissions (PBA) or Recognised Prior Learning (RPL) even without an undergraduate degree.

Performance-Based Admissions (PBA) and Recognised Prior Learning (RPL) provide non-traditional routes to postgraduate diplomas and Master’s degrees, considering alternative education backgrounds  from polytechnics, private education institutes, or Institutes of Technical Education and professional working experience
Professional Certificate in
Data Science 
30 ECTS credits
Postgraduate Diploma in
Data Science
60 ECTS credits

MSc. in Data Science
90 ECTS Credits

Employment opportunites across multiple roles and industries

Holders of the Professional Certificate in Data Science will find opportunities in data analytics and data science roles across a wide range of sectors. Data Science and analytics have become  central to organisations and are used across a wide array of industries and business functions.  Here are some examples listed  below:
  • JOB ROLES
  • INDUSTRIES 
  • Data Analyst
  • Finance
  • Business Intelligence (BI) Analyst
  • Healthcare 
  • Cybersecurity Data Analyst
  • Retail 
  • Financial Analyst
  • E-commerce 
  • Risk Analyst
  • Technology
  • Marketing Data Analyst
  • Manufacturing
  • Sales Operations Analyst
  • Energy
  • Healthcare Data Analyst
  • Telecom
  • Operations Data Analyst
  • Environment and Sustainability
  • Environment and Sustainability
  • Government and Public sector

Programme Delivery

Our unique, hands-on learning experience combined with our interactive learning platform and  industry case studies, real worlds data sets, project based assessment led by expert instructors and tutor support allows learners to build their knowledge and practice applying it before bringing it into a professional setting.
Blended Learning Approach
This course combines classroom and online lectures, workshops, and a robust learning platform. It provides face-to-face interactions and the flexibility of online learning, catering to busy professionals.
Course Duration and Commitment
Complete the Professional Certificate in Data Science in five months or less, balancing study time, group sessions, mentorship, project work, and exams, suitable for working professionals and business leaders.
Online Course Content
Course materials are organized into the four course modules on our learning management system, featuring recorded lectures, slides, case studies, quizzes, and practice assignments with real-world business cases.
Communication and Collaboration
Engage in group sessions and one-on-one interactions with mentors, while also networking and collaborating with peers to build valuable professional connections.
Assessment and Projects
Assessments include assignments, exams, and a capstone project using real-world data. This allows you to integrate course elements and apply your skills to address relevant business challenges.
Expert Tutor Support
Expert tutors are on hand to guide you through the course, prepare you for exams and support your project work

Fees and Funding

Full Course Fees before funding

  • Exploratory Data Analysis: S$1,500
  • Statistical Inference: S$1,500
  • Fundamentals of Predictive Modelling: S$1,500
  • Advanced Predictive Modelling: S$1,500
  • Time Series Analysis: S$1,500
  • Unsupervised Multivariate Methods: S$1,500
  • Machine Learning 1: S$1,500
  • Machine Learning 2: S$1,500
  • Text Mining and Natural Language Processing: S$1,500
  • Data Science in Practice: S$1,500
    Total Fee: S$15,000

How to Apply and further information

To find our more about the programme contact:

paul@trainingvision.edu.sg
Phone +65 74537 78375

SkillsFuture/PSEA funding to be announced

This course is delivered by our training partner in Singapore, Training Vision Institute


 PROFESSIONAL Certificate in DATA SCIENCE

Find out more

Please complete this form and a member of our team will get in touch with you to explain how it works.
eiuropean accredited programme (ECTS)

Accreditation

Data Science Institute is a full member college of Woolf, offering accredited degrees under the European Standards and Guidelines (Brussels 2015). Woolf is a global collegiate Higher Education Institution licensed in Europe (license 2019-015), and the operative Policy of Quality Assurance allows member colleges to share the same standards of accreditation while independently managing their own students and faculty. Courses with ECTS credits are specifically designated as such.

See what our students say about us

Mohammed Hamad

Analytics Lead

DXC Technologies

“The course is very strong technically and the support was excellent”

Jehad Mossa

Senior Manager

PWC

“The overall course programme is very complete and I developed my data science knowledge substantially”

Manuel Tanpoco

(Ed.D, MBA)

Professor of Mathematics La Salle University

“As a mathematician and lecturer, I was impressed with the course content and explanation of the statistical methods and algorithms. I also benefited greatly from learning Python and R”

Mohammed Hamad

Analytics Lead

DXC Technologies

“The course is very strong technically and the support was excellent”

Jehad Mossa

Senior Manager

PWC

“The overall course programme is very complete and I developed my data science knowledge substantially”

Manuel Tanpoco

(Ed.D, MBA)

Professor of Mathematics La Salle University

“As a mathematician and lecturer, I was impressed with the course content and explanation of the statistical methods and algorithms. I also benefited greatly from learning Python and R”

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