Data Science Immersive

Overview

Overview

Duration 48 days
Course Time 6.30 pm - 10.00 pm (Tue & Thu), 9.00am - 5.00pm (Sat)
Enquiry Click here to contact us
This course is delivered by General Assembly.

Data science has topped LinkedIn’s Emerging Jobs Report for three years running. Capitalise on demand with a 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

Key Takeaways

At the end of this course, the participiants 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

Who Should Attend

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

Pre-requisites
Due to the technical nature of the programme, students 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

Students 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 students arrive with basic familiarity with Python and programming
fundamentals. Prior programming experience in any language would be advantageous.

Admissions Process
All students looking to enrol in this programme will be required to undergo an admissions screening process with General Assembly. The process includes completion of pre-admissions assignment (~5hr to complete) as well as an interview with an admissions representative.

Laptop requirements
Students are required to supply their own laptop for this class. Refer to the DSI Tech Guide for minimum system requirements.

Connect with General Assembly directly to explore the course in more detail.


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:
  • Digital Business Analyst
  • General Public Officers
  • Quantitative Analyst


Course Structure

Course Structure

This course is delivered in hybrid mode over 24 weeks part-time. The course schedule are 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

Start Date: 26 Nov 2022
End date: 17 Jun 2023
NOTE: Classes will break over the holiday period from 27 Dec 2022 to 7 Jan 2023 and on public holidays.



The course 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

Instructors

Instructors


Fees

Fees


Full Fee

Full course fee

S$14,650

7% GST on nett course fee

S$1025.50

Total nett course fee payable, including GST S$15,675.50



Upcoming Classes

Upcoming Classes

Class 1

Duration: 48 days

26 Nov 2022 to 17 Jun 2023 (Part Time)

When :
Nov:
26(Sat), 29
Dec:
01, 03(Sat), 06, 08, 10(Sat), 13, 15, 17(Sat), 20, 22, 24(Sat)
Jan:
10, 12, 14(Sat), 17, 19, 26, 28(Sat), 31
Feb:
02, 04(Sat), 07, 09, 11(Sat), 14, 16, 18(Sat), 21, 23, 25(Sat), 28
Mar:
02, 04(Sat), 07, 09, 11(Sat), 14, 16, 18(Sat), 21, 23, 25(Sat), 28, 30
Apr:
01(Sat), 04, 06, 08(Sat), 11, 13, 15(Sat), 18, 20, 25, 27, 29(Sat)
May:
02, 04, 06(Sat), 09, 11, 13(Sat), 16, 18, 20(Sat), 23, 25, 27(Sat), 30
Jun:
01, 06, 08, 13, 15

Time : 6:30 PM to 10:00 PM (Tue & Thu), 9:30 AM to 6:30 PM (Sat)
Registration:

How To Register

How To Register


Agency-sponsored

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.