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Postgraduate Diploma in Data Analytics

Data analytics is a hot field in today's world. The demand for analytics in healthcare, engineering, business and finance has exploded over the past 5 years. More and more tools are being developed and there is an increasing demand for experts in these fields to use data science tools to analyse data. This Postgraduate Diploma in Data Analytics is targeted towards graduates with technical expertise or business analytics background and wishes to apply data analytics to their domain areas. 

Programme Aims

The course is designed to guide participants to use data science tools to perform data analytics and data science techniques in a variety of business-related areas, including:

  • Data Analytics and Statistics
  • Machine and Deep Learning
  • Big Data Analytics
  • Business and Finance Analytics
  • Project Management
  • Cybersecurity 

The course uses Rapidminer primarily for its data flow needs and also uses GUI tools and cloud studios, which are point-and-click interfaces, making them user-friendly. 

Leaning Outcomes

Graduates of the Postgraduate Diploma in Data Analytics would be able to understand and use tools to assist them with Big Data management, within their employment context, and be confident in churning out meaningful and accurate information from data sets. 

By attending this 8 month diploma course, you will be able to: 

  • Grasps key concepts of RapidMiner software
  • Machine Learning and Deep Learning
  • Querying data from Hadoop and perform machine learning
  • Cybersecurity basics
  • Business & Finance analytics principles
  • Project Management


On successful completion of this programme, students should be able to:

  • Have a foundation for understanding contemporary business issues;
  • Describe the concepts in management;
  • Develop skills to analyse real world business issues;
  • Demonstrate enhanced capability in analytical, diagnostic, investigative and writing skills.
Mode of Delivery

Lecture, Tutorial, Case Study, Class Discussion, Group Work Project, Presentation, Computer Exercises

Course Duration

Full Time Classes: 8 Months

Part Time Classes: 12 Months

Entry Requirements

Minimum Age:
21 years or above

Minimum Academic Entry Requirement:
Local and International students MUST have

  • Bachelor’s degree 

Minimum English Language Entry Requirement:
Both local and international students MUST fulfil the minimum English language entry requirement of one of the following.

  • Achieved grade C6 or better in English language in GCE O level;
  • Completed LSBF Preparatory Course in English Advanced Level;
  • IELTS 6.0/TOEFL 600;

Students with non-standard entry requirements will be assessed on a case by case basis subject to approval of the Academic Board.

Module Synopsis

Basic Data Analysis
This module will get the student familiar with Rapidminer studio and the basic functions in Rapidminer process and modules. Students will use Rapidminer studio to import, handle and manipulate data and perform visual and statistical analysis on data.

Advanced Data Analysis
This module is to prepare students for advanced data preparation and ETL data from different sources and combine them into a single dataset. Students also preform necessary aggregations on data and do engineering on the data to create new columns using existing data. Student learn the basics of macros to program their own procedures and steps. In practical situations, students must handling different data types form different sources and merge them into a meaningful dataset that can be explored and utilized with machine learning and data mining.

Machine Learning
This module will get the student familiar with concepts of machine learning and data mining for predictive analytics. Students will learn about the algorithms available, what the algorithms do and how to choose the best one and apply it to their data. Students learn to make predictions with the output of the model and compare them across different models. Students will be able to apply these concepts in business application and other fields by studying patterns in the data and using them to make predictions.

Deep Learning with RapidMiner
This module is to prepare students for advanced deep learning with neural networks in RapidMiner for processing and predicting images and text. Keras framework is introduced and student learn the Keras module to perform deep learning tasks. 

Radoop for RapidMiner
This module will get the student familiar with concepts of Hadoop and other technologies like Hive, Mahout and Spark. Students will learn the essentials of these technology platforms without the need to program. Using Radoop module extensions, students query data and perform analysis & visualizations on the data. Using machine learning modules, students create models and use to predict unlabelled data.

This module will get the student familiar with concepts of cybersecurity, various concepts and tools are introduced for students to gather network data and cyber security data. Students learn various types and entry points of cyber attack. Students collect data and perform machine learning to predict cyber attacks.

Project Management
This module will get the student familiar with concepts of project management following closely the PMBOK syllabus. Students will grasp the concepts of project management and will benefit greatly in their business & IT undertaking. Students will know the stages of each project from start to finish and all the various disciplines of project management.

Business & Finance Analytics
This module will get the student familiar with concepts of business and finance analytics and apply these techniques to their jobs and business. Students use concepts from previous courses for areas such as marketing & human resource as well as risk management and portfolio optimization. These business and financial concepts benefit any organization.

Attendance Requirements

International Students: 90%
Local, PR, non-student pass: 75%

Teacher Student Ratio

1 : 40

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