Data Science Immersive

Programme Code D33
General Digital Literary
Data Science & AI
Learning Partner(s)
General Assembly
51 Days
Format Hybrid
Scripting Data Storytelling Charts & Dashboards Visual Analytics Principles Machine Learning Statistical Techniques Exploration Analysis Data Quality Data Collection Data Visualisation & Communication Data Exploration & Analysis
Job Roles
ICT&SS Professional Data Engineer Quantitative Analyst Public Service Officer (non-ICT&SS) Digital Business Analyst Data Analyst Chief Data Officer


Data science has topped LinkedIn’s Emerging Jobs Report for three years running. Capitalise on demand with this programme that's primed for industry relevance.

Get hands-on with the skills you need to derive value from complex data. Dive into Python, data analysis, and statistical modelling. Then, branch into machine learning with algorithms of increasing complexity, from decision trees and random forests to natural language processing and neural networks.

Key Takeaways

At the end of this programme, you will be able to:
  • collect, extract, query, clean, and aggregate data for analysis
  • perform visual and statistical analysis on data using Python, and its associated libraries and tools
  • build, implement, and evaluate data science problems using appropriate machine learning models and algorithms
  • use appropriate data visualisation tools to communicate findings
  • present clear and reproducible reports to stakeholders - speaking confidently about the technical choice made as well as communicating insights clearly to on-technical audiences
  • apply question, modeling, and validation problem-solving processes to data sets from various industries to provide insight into real-world problems and solutions
  • prepare for the world of work, compiling a professional-grade portfolio of solo, group, and client projects

Who Should Attend

  • Please refer to the job roles section.
  • Most participants have some technical background, such as a degree in mathematics or computer science, or work experience in research or analysis. The recommended background is a strong mathematical foundation and familiarity with Python and programming fundamentals.
  • Common incoming participants' profiles include:
    • Mid-career analysts such as marketing analysts, financial analysts, business analysts, etc.
    • Academic researchers from quantitative fields, as well as teachers of maths and science
    • Programmers, engineers, and recent STEM graduates
    • Business backgrounds including strategy, audit and accounting
    • Some unrelated backgrounds including sales, legal and trades
Book an 1-on-1 session with General Assembly personnel to find out more about the programme. 


Due to the technical nature of the programme, you are required to have a base level of competency across two main areas in order to be best prepared for the programme.

1. Be comfortable with numbers
You should have strong familiarity with math and descriptive statistics, particularly with knowing what the following terms are and how to apply them practically: Mean, Min, Max, Mode, P-Values, and Histograms (the On-boarding Task will help prepare students for this, but additional preparation is HIGHLY recommended).

2. Have some programming experience
We recommend that you arrive with basic familiarity with Python and programming
fundamentals. Prior programming experience in any language would be advantageous.

Admissions Process
You will be required to undergo an admissions screening process with General Assembly before enrolment. The process includes completion of pre-admissions assignment (~5hr to complete) as well as an interview with an admissions representative.

What To Bring

Please bring your own laptop for this class. Refer to the DSI Tech Guide for minimum system requirements.

Programme Structure

This programme is delivered in hybrid mode over 24 weeks part-time. The schedule is as follow:

Part-time (24 weeks)
Tuesdays and Thurdays: 6.30pm to 10.00pm
Optional office hours 7.00 pm - 9.00 pm (Wed)
Saturdays: 9.30am to 6.30pm

The programme will cover the following topics:
  • Pre-Work: 12 hours of Online Tutorials
  • Unit 1: Data Science Fundamentals
  • Unit 2: Exploratory Data Analysis
  • Unit 3: Classical Statistical Modeling
  • Unit 4: Machine Learning Models
  • Unit 5: Advanced Topics and Trends


Full Fee

Full programme fee


8% GST on nett programme fee


Total nett programme fee payable, including GST S$15,822

With effect from 1 Jan 2023 till 31 Dec 2023

How To Register


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

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 GovTech Digital Academy will inform you whether you have been successful in enrolment.