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OverviewCourse InformationCourse StructureEntry Requirement
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Begin the learning journey into data analytics by picking up critical technical skills and essential domain knowledge. Learn to derive actionable insights that help drive business value through hands-on data analysis approaches.
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Acquire fundamental coding abilities to effectively perform data visualisation, data exploration, data wrangling and statistical analysis. Build a strong technical foundation before progressing to advanced data analytics techniques.
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The course is suitable for IT professionals, business domain specialists and aspiring data analysts who wish to enhance their technical competencies in data analytics.


Course Information At A Glance
Course Date
Post Diploma Cert 1 (Data Visualisation and Programming for Analytics):
19 Oct 2020 - 28 Feb 2021​
Class time6:30pm - 10:00pm, Monday and Thursday (May be subject to changes)
Application Period
15 Jun - 31 July 2020
Application Outcome: 31 Aug 2020
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 application 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.


Entry Requirements
You will need to have a local Polytechnic Diploma or recognized Degree (with English as the medium of instruction) in business, engineering, technology, or any other disciplines with at least one (1) year of relevant work experience.

Must have working knowledge of Microsoft Office e.g. Word, Excel and Powerpoint.
Students enrolled into the course are expected to bring along their personal laptop.

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