Python for Data Analytics & Applied Machine Learning



Duration 10 days
Course Time 9.00am - 5.00pm
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This course is delivered by Republic Polytechnic.

This is a series of 6 courses designed to equip participants with knowledge and skills in data science and AI using a hands-on approach across all courses in the set. This course will be conducted every Thursday and Friday over 5 continuous weeks.

Key Takeaways

Key Takeaways

Fundamentals of Data Visualisation (Using Tableau) (2 days)
This course provides participants with a good foundation to create effective visualisation charts, and build interactive dashboards using Tableau for supporting data-driven business decisions. 

At the end of this course, the participants will be able to:

  • Recognise the history of visualisation
  • Describe data, information and visualisation
  • Identify the human perception to visualisation
  • Interpret the levels of visualisation
  • Critique samples of visualisation
  • Examine factors of graphical excellence
  • Create interactive charts and dashboards

Fundamentals of Data Analytics (2 days)

This course provides participants with a good foundation in data analytics with a focus on data mining and predictive analytics.

At the end of this course, the participants will be able to:

  • Summarise the role and tasks of an analytics professional
  • Describe the application of data analytics in the real-world
  • Distinguish the use of analytics models to solve various problems
  • Create some learning models for appreciation
  • Evaluate the benefits and challenges of employing analytics at work

Artificial Intelligence for Techies - A Hands-On Approach (1 day)

This is an introductory course with hands-on experience for participants to have an overview of Artificial Intelligence.

At the end of this course, the participants will be able to:

  • Outline the fundamentals of Artificial Intelligence
  • Examine applications of AI
  • Differentiate Machine learning, Deep learning & Reinforcement Learning
  • Explore AI technologies through demonstration and hands-on
  • Select AI frameworks, software and hardware
  • Apply practical text analytics using AI services
  • Apply practical computer vision using a deep learning framework

 An Introduction to Code-Free Machine Learning (1 day)

This workshop will take participants through various code-free tools and platforms to experience machine learning without programming.

At the end of this course, the participants will be able to:

  • Provide an overview of Machine Learning
  • Present Machine Learning workflow
  • Solve a regression problem
  • Train an Object Detector
  • Perform topic modelling
  • Transcribe an audio file to text
  • Identify and apply AI at work.

Introductory Programming using Python (2 days)

This is a foundational course in computer programming using the Python programming language.

At the end of this course, the participants will learn the essentials of Python and key programming concepts, including:

  • Data types and variables
  • Expressions and statements
  • Operators
  • Input / output
  • Decision making and repetition
  • Strings, lists, tuples, dictionaries
  • Handling of files (text files, spreadsheets)
  • Functions, modules and packages

Data Science with Python (2 days)

Participants will learn and apply data extraction and analysis techniques to draw useful insights for decision-making, as well as a range of statistical techniques that enable machines to improve tasks with experience and perform predictive analytics.

At the end of this course, the participants will learn:

  • Python virtual environments
  • Well known libraries used for Data Science
  • Data visualisation
  • Machine learning
  • Supervised and unsupervised learning
  • Regression techniques
  • Classification techniques
  • Clustering techniques

Who Should Attend

Who Should Attend

This course is designed for anyone with a keen interest to learn and apply the broad concepts of data visualisation, data science and analytics, as well as machine learning.

Participants should have basic IT literacy.

ICT and SS Competency Framework

ICT and SS Competency Framework

As part of the ICTCF, this course falls under the Data Science & AI functional clusters and tagged to the following competencies:
  • Data Collection
  • Data Quality
  • Exploration Analysis
  • Statistical Techniques
  • Machine Learning
  • Visual Analytics Principles
  • Charts & Dashboards
  • Data Storytelling
  • Scripting

The course is mapped to the following job roles:
  • General Public Officers

Course Structure

Course Structure

This instructor-led course is delivered physically, and will cover the following topics:

Fundamentals of Data Visualisation (Using Tableau) (2 days)
Day 1

  • Introduction to Data Visualisation
  • Tableau 101
Day 2
  • Tableau Interactive Charts
  • Dashboards

Fundamentals of Data Analytics (2 days)
Day 1

  • Intro to Data Visualisation with demo
  • Data-Information-Knowledge-Wisdom pyramid
  • The Evolution and four levels of data analytics
  • CRISP-DM data mining methodology
  • Data preparation and cleansing
  • Supervised learning: predictive analytics
  • Decision tree modelling, its pros and cons
  • Hands on practice in Knime

Day 2

  • Improving a decision tree model
  • Model complexity
  • Model assessment via confusion matrix
  • Application of model effectiveness (case study)
  • Unsupervised learning: cluster analysis
  • K-means clustering algorithm, its pros and cons
  • Hands on practice in Knime
  • Relationship between AI, ML, DL and DS
  • Data Analytics landscape, architecture and challenges
  • Real word data analytics in action

Artificial Intelligence for Techies - A Hands-On Approach (1 day)

  • Fundamental of Artificial Intelligence
  • Machine Learning and Deep Learning
  • Google Teachable machine
  • CNN and RNN
  • AI Frameworks, software and hardware
  • AI Services
  • Practical Text Analytics using AI services
  • Practical Computer Vision using a programming framework
  • Application of AI

An Introduction to Code-Free Machine Learning (1 day)
Part 1: Machine Learning

  • Articulate the fundamentals of Machine Learning
  • Describe the Machine Learning workflow
  • Hands-on activity to solve a regression problem on a cloud platform

  Part 2: Computer Vision

  • Articulate the fundamentals of Computer Vision
  • Describe the purpose and process of Transfer Learning
  • Hands-on activity to perform object detection on GovTech’s Video Analytics System

Part 3: Natural Language Processing

  • Articulate the fundamentals of Natural Language Processing
  • Hands-on activity to perform topic modelling on GovTech’s GovText platform
  • Hands-on activity to perform speech-to-text on GovTech’s Transcribe platform
Part 4: Code-free AI development
  • Case study AI applications from use cases in Government
  • Propose use cases from work where AI is applicable

Introductory Programming using Python (2 days)
Day 1

  • Install Python and using Wing IDE
  • Variables, Values
  • Basic Data Types
  • Data Types Conversion
  • Display/Outputs
  • Writing Comments
  • User Inputs
  • Decision-Making: if/elif/else
  • Lists
  • Tuples
  • Repetitions: while vs for
  • Functions
  • External Library

Day 2

  • String functions
  • String formatting
  • Dictionary
  • File reading/writing
  • Copying, moving and deleting files and folders
  • Image Processing
  • Working with Excel
  • Connecting to the Web
  • Sending Emails

Data Science with Python (2 days)
Data science and analytics are rapidly gaining prominence as some of the most sought-after disciplines in every industry. Participants of this 2-day workshop will acquire data science knowledge and data science skills in Python to extract insights from data to make meaningful decisions and predictions.

Upon completion of this course, participants will be able to:

  • Perform data wrangling and aggregations in NumPy and Pandas
  • Apply visualisation techniques to understand, explore and explain the data
  • Understand and apply supervised and unsupervised learning models
  • Assess and evaluate the performance of various machine learning methods



Fees and Funding

Fees and Funding

Full Fee

Full course fee


7% GST on nett course fee


Total nett course fee payable, including GST S$3022.75

NOTE: Funding is available for this course. Please visit the training provider’s website to find out about the updated course fee funding breakdown, eligibility, terms and conditions.

Upcoming Classes

Upcoming Classes

Class 1

Duration: 10 days

10 Nov 2022 to 09 Dec 2022 (Full Time)

When :
Time : 9.00am - 5.00pm

How To Register

How To Register


Step 1 Apply through your organisation's training request system.

Step 2 Your organisation's training request system (or relevant HR staff) confirms your organisation's approval for you to take the course.

Your organisation will send registration information to the academy.

Organisation HR L&D or equivalent staff can click here for details of the registration submission process.

Step 3 The Digital Academy will inform you whether you have been successful in enrolment.