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OverviewCourse InformationCourse StructureEntry Requirement
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Upon completion of our Specialist Diploma in Data Analytics, you would have acquired the fundamental competencies to effectively perform data analysis on business data. Further broaden your knowledge and skills in machine learning through hands-on implementation of machine learning and deep learning models.
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Take a practical approach to pick up a variety of supervised learning, unsupervised learning and deep learning models to solve business issues involving data classification, regression, computer vision and natural language processing.
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The course is suitable for:
1) IT professionals and aspiring data analysts who wish to expand their skill sets in the implementation of machine learning models.
2) Functional managers who wish to value-add to their business domains through application of machine learning and deep learning models.

All students who complete our Specialist Diploma in Data Analytics are eligible to enroll into Advanced Diploma in Machine Learning.


Course Information At A Glance
Course Date
Post Diploma Cert 1 (Data Visualisation and Programming for Analytics):
Advanced Diploma in Machine Learning is via Specialist Diploma in Data Analytics (SDDA)

Post Diploma Cert 2 (Data Wrangling and Descriptive Analytics):
Advanced Diploma in Machine Learning is via Specialist Diploma in Data Analytics (SDDA)

Post Diploma Cert 3 (Machine Learning and Deep Learning):
Advanced Diploma in Machine Learning is via Specialist Diploma in Data Analytics (SDDA)

*PDC1 and PDC2 are offered under Specialist Diploma in Data Analytics. Upon completion of PDC1 and PDC2, students enrolled in Specialist Diploma in Data Analytics have the option to enroll in PDC3. Students who complete PDC3 will also attain the Advanced Diploma in Machine Learning.

Class time6:30pm - 10:00pm, Monday and Thursday (May be subject to changes)

Application Period

Advanced Diploma in Machine Learning is via Specialist Diploma in Data Analytics (SDDA)

Course Fee ​For more info, please click here.
SkillsFuture Credit
Eligible. For more details, please click here.
Venue
Ngee Ann Polytechnic

Assessment(s)

Quizzes, Projects, Continuous Assessments


Ngee Ann Polytechnic reserves the right to reschedule / cancel any programme , modify the fees and amend information without prior notice.
What You Will Learn
You are required to complete 2 post-diploma certificates within a two-year validity period to be awarded the Specialist Diploma qualification.
Post-Diploma Certificate in Data Visualisation and Programming for Analytics

Data Visualisation and Storytelling
This module discusses the principles and techniques for creating effective visualisations based on graphic design and perceptual psychology. Using widely adopted tools, learners will apply these principles and techniques to create rich visualisations for analysis and presentation. Learners will learn visual analysis techniques to grasp pertinent information, as well as apply exploratory techniques to further derive key insights. Data storytelling and information graphics best practices will also be explored to allow learners to present data effectively and eloquently.

Programming for Analytics
The aim of this module is to equip participants with sufficient mastery of a programming language to perform operations and analysis. This module is suitable for learners with little or no programming background. The programming language taught is Python, which is fast becoming the world’s most popular coding language due to its simplicity and flexibility. Its popularity also stems largely due to its wide range of applications in areas such as machine learning, network automation, and internet of things. The module highlights the syntactical and algorithmic aspect of programming to participants. Learners will be able to code using Python, from basic to complex algorithms progressively.

Post-Diploma Certificate in Data Wrangling and Descriptive Analytics

Data Wrangling
The aim of this module is to equip participants with the tools and skills sets to handle, clean and prepare large curated data sets for data analytics purposes. Participants of this module should minimally have basic programming knowledge and be able to understand and decipher simple syntaxes. The processed data sets will allow for meaningful statistical analysis, data modelling, and machine learning to be easily performed. Emphasis will be placed on the Extraction, Transformation, and Loading (ETL) of data sets. The use of relevant programming libraries for Missing and Time Series Data will also be explored. Learners will experience the process of exploratory data analysis, normalization of data and data distribution, which will be crucial for subsequent understanding of machine learning concepts and models.

Descriptive Analytics
In this module, learners will first be exposed to Descriptive Statistics concepts, delving into topics such as central tendency, normal distribution, measures of variability, variance and standard deviation. Following which learners will be able to perform univariate, multivariate and correlation analysis in order to identify inherent patterns and derive key insights from business data. They will also create appropriate visualisation components using systems to gain insights from the data. These visualisation components will be synthesized into dashboards that can be readily adopted by users.

Post-Diploma Certificate in Machine Learning and Deep Learning

Machine Learning
This module introduces the fundamentals of Machine Learning and its applications. Learners will be provided the essential context and background knowledge of Machine Learning. Learners will gain exposure to both supervised and unsupervised learning models such as Linear & Logistic Regression, Decision Tree, K-means Clustering and more. Using leading software and associated libraries, learners will be able to implement and train Machine Learning models to address business challenges.

Deep Learning
This module introduces the fundamentals of Deep Learning (DL) and its applications. Learners will be provided the essential context and background knowledge of Deep Learning, a subset of Machine Learning. Learners will explore Deep Learning models such as Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks. There will be ample opportunities for learners to experience the practical applications of these models in areas such as computer vision and natural-language processing. Adopting an experiential approach, learners will implement and train the Deep Learning models using leading software and associated libraries.


Entry Requirements
Only applicants who have completed Specialist Diploma in Data Analytics are invited to apply for the course.


This course is brought to you by Ngee Ann Polytechnic’s School of InfoComm Technology
For enquiries, please email EnquiryPDP@np.edu.sg


Recognition of Prior Learning (RPL)

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.

For more information about Recognition of Prior Learning, please click here.