SDSA is a brand under Technology Academy Group Pte. Ltd.

Why Data Science?

Have you tried searching for a flight on Skyscanner and realise that many flight advertisement starts showing on your social media feeds such as Facebook and Instagram? Or how is it possible that your best friend appears first on your Instagram feed instead of some random dude you just followed?

In the real world, business, finance, economics, and everything else, Data Science is creating a great amount of value to aid humans in their decisions through machine learning, deep learning, analytics. Data Science is an emerging & booming industry.

why-data-science

REGISTRATION CLOSED

Overview

  • Course will be running on 6-days intensive bootcamp
  • Each lesson will be 3 hours with a 20 mins break. Meals are provided.

Course Fees

Course fees will be S$2500.

Course Schedule

Week 2 of December:

  • 10th – 15th December 2018
    9AM – 12PM / 12PM – 3PM / 3PM – 6PM / 7PM – 10PM

Week 3 of December:

  • 17th – 22th December 2018
    9AM – 12PM / 12PM – 3PM / 3PM – 6PM / 7PM – 10PM

Location:

  • Singapore Management University

Course Outline

Learning Objective 1.0:

Have a better understanding on how Data Science plays a part in today’s society.

Breakdown:

  • Self Introduction
  • Introduction to Data Science
  • Why Python?

Learning Objective 1.1:

Basics of computational thinking and python.

Breakdown:

  • Primitives, variables and types
  • String slicing
  • Operators, combination, abstraction
  • Booleans, Truth Tables
  • Week 1 Bring-home lab

Learning Objective 2.0:

How to solve errors with available resources

Basic sequences and loops

Breakdown:

  • Bring-home lab discussion
  • Sequence
    • List and set
  • Iterations
    • For, while
    • Continue, break

Learning Objective 2.1:

Hashing values

Breakdown:

  • Dictionary
  • Nested Dictionary of lists
  • Week 2 Bring-home lab

Learning Objective 3.0:

Conditions

Breakdown:

  • Bring-home lab discussion
  • Functions

Learning Objective 3.1:

Higher Order Functions

Breakdown:

  • Abstraction
  • Brief on first project (LTA cars dataset)
  • Week 3 Bring-home lab

Learning Objective 4.0:

Manipulation of real datasets using simple operations

Making use of mathematical libraries for analysis

Breakdown:

  • Bring-home lab discussion
  • How all of these are integral parts of data science
  • Introduction of libraries
    • Math
    • Statistics
    • Numpy
    • Pandas
  • Reading Data; Selecting and Filtering Data; Data manipulation, sorting, grouping and rearranging
  • Descriptive Statistics
  • Visualisations
  • Week 4 bring-home lab

Learning Objective 5.0:

Machine Learning

Breakdown:

  • Lab Discussion
  • What is Machine Learning?
  • Machine Learning Process
  • Types of Machine Learning

Learning Objective 5.1:

Linear Regression

Breakdown:

  • Linear Regression
  • Capstone Project

Learning Objective 6.0:

  • Discussions on Capstone Project
  • Bonus topics on advanced libraries
    • Sentiment Analysis

Breakdown:

  • Common mistakes
  • Using Twitter feeds to predict sentiments of Apple’s products
  • Example of different prediction use cases
  • Ending note: Takeaways and bridge to DSC1020 Machine Learning

Course Instructors

Dexter Tan
Senior Technology Consultant

Shumin Lai
Data Analyst @ Accenture

Patrick Ching
Data Analyst Executive
@ Banyan Tree Hotels & Resorts

Learn more about our instructors

Find out more

Kick-start Your Career in Data Science

Emerging Industry
Demand for Data Scientists is booming right now.
High Salary
Salary can go up to S$120k for a mid-level Data Scientist.
Future Prospects
Data Science will be at the forefront of tech revolution 4.0

According to IMDA Chief Executive, over 42,000 additional information and communications technology professionals will be needed over the next three years, including in industries like finance, healthcare and logistics.

apply now
homepage-intake-image