Data Visualisation in Python

Course Reference No: TGS-2020501974


Course Objectives

Knowing how best express your results from your data science and machine learning algorithms is key to convincing your team and management your point of view. This programme will teach you visualisation techniques using Python as part of your data science workflow.

In this course, you will be shown how to leverage various Python libraries such as Matplotlib, Bokeh, Seaborn and others to enable you to focus on how to communicate with visualisations for maximum impact.

At the end of the programme, participants will be able to:

  • Create and tell a story with Python visualisation with Python packages such as Matplotlib/Bokeh/Seaborn
  • Build both static and interactive visualisation
  • Understand good visual design principles


  1. Understanding Visualisation
    • Pre-attentive Attribute
    • Style
    • Colour Theory
    • Emphasis
    • Do and Don’t
  2. Data Munging – a prelude to visualisation
    • Understanding Notebook Environment
    • Data Cleansing
    • Data Filtering
    • Tidy Data
  3. Advanced data structures and visualisation
    • Slicing
    • Aggregation
    • Groupby
    • Pivot
    • Multi Index
    • And How to Apply them in Visualisation
  4. Interactive graphics & Visualisation
    • Matplotlib 
    • Seaborn
    • Grammar of Graphics (ggplot)
    • Bokeh
  5. Mini-Project: Creating various graphs


Jupyter, Python with the following packages: (Matplotlib, Seaborn, ggplot, Bokeh)


Must be familiar with the Python programming language and statistics 101 at a pre-university level.

Who Should Attend

Software Engineers, Data Scientists, Data Analysts

Mode of Training



Dr. Julian Lin


Julian Lin is a Senior Lecturer in Cybersecurity and Data Analytics with School of Continuing and Lifelong Education (SCALE) at the National University of Singapore (NUS). He is a Certified Information Systems Security Professional (CISSP) and has a dozen other IT certifications. Recently, Julian was ranked 6th in the Microsoft Data Science Capstone Competition. He has been conducting text-analytics research since 2010 and teaching visualization since 2007. During his IT consultancy career, he oversaw the application and infrastructure projects in Amoseas, Mannesmann Dematic Colby (Sydney), Alcatel (Sydney), and the University of New South Wales (UNSW) Faculty of Commerce and Economics. Julian has taught programming and data management for business at UNSW. At NUS, he had mentored student software development projects and taught visual communications, designing new media content, and research methods. He was given a teaching award for visualization class and research award for user acceptance research in NUS. Julian obtained a PhD degree in Information Systems from NUS. He was a recipient of two scholarships from the Overseas Chinese Association in Taiwan while pursuing his Bachelor degree and another scholarship while pursuing a Master degree in Australia.




1 Day
9.00am to 5.30pm


National University of Singapore
University Town

International Participants


Incl. GST

Singapore Citizens
(39 yrs old or younger) 


Singapore PRs


Incl. GST

Singapore Citizens
(40 yrs or older)


Incl. GST

Enhanced Training Support for SMEs


Incl. GST

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Fees & Fundings

Singapore Citizen1
39 years old or younger
Singapore Citizen1
40 years or older eligible for MCES2
Singapore PRs Enhanced Training
Support for SMEs3
Full Programme Fee S$750.00 S$750.00 S$750.00 S$750.00 S$750.00
SkillsFuture Funding
(Refer to Funding Page for Claim Period)
- (S$525.00) (S$525.00) (S$525.00) (S$525.00)
Nett Programme Fee S$750.00 S$225.00 S$225.00 S$225.00 S$225.00
7% GST on Nett
Programme Fee
S$52.50 S$15.75 S$15.75 S$15.75 S$15.75
Total Nett Programme
Fee Payable, Incl. GST
S$802.50 S$240.75 S$240.75 S$240.75 S$240.75
Less Additional Funding if
Eligible Under Various Scheme
- - (S$150.00) - (S$150.00)
Total Nett Programme Fee, Incl. GST,
after additional funding from the various funding schemes
S$802.50 S$240.75 S$90.75 S$240.75

  1. All self-sponsored Singaporeans aged 25 and above can use their $500 SkillsFuture Credit to pay for the programme. Visit to select the programme. 
  2. Mid-Career Enhanced Subsidy (MCES) - Singaporeans aged 40 and above may enjoy subsidies up to 90% of the programme fee. 
  3. Enhanced Training Support for SMEs (ETSS) - SME-sponsored employees (Singaporean Citizens and PRs) may enjoy subsidies up to 90% of the programme fee. For more information, visit
  4. Eligible organisations (excluding government entities) may apply for the absentee payroll funding via SkillsConnect at for Singaporean/permanent resident participants attending the programme during working hours. The absentee payroll funding is computed at 80% of hourly basic salary capped at $4.50 per hour or $7.50 per hour for SME. For more information, visit
  5. Eligible individuals may apply for training allowance capped at $6/hr under the WSS scheme, visit- for more information on WSS.
  6. NTUC Training Fund (SEPs) – All self-employed (i.e. freelancers and sole-proprietors-with-no-employee) Singaporeans and Permanent Residents are eligible to apply for the NTUC Training Fund (SEPs) from NTUC’s Employment and Employability Institute (e2i). Click here for more information.
NTUC members enjoy 50% of the unfunded course fee support for up to $250 each year for courses supported under UTAP (Union Training Assistance Programme). Terms and Conditions apply. Please visit
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08 February 2021