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Data Science Introduction (Python): SSNC Edition

This special edition of the course will introduce students to data science and deepen their understanding of Python through real world Singapore datasets and problems.


Created By : ZP

1st Week of April, 2019

Part Time

6 weeks

2.5 h/session

Data Science Introduction (Python): SSNC Edition

0 1st Week of April, 2019 Part Time 6 weeks 2.5 h/session

What You'll Learn

A level up from our Python Development course, our Data Science Introduction (Python): SSNC Edition course will teach you to build simple yet functional web applications using real world data from open data resources from the Singapore government.

You will learn how to perform the various parts of the data science process:
- Preparing data: Cleaning, transforming, etc.
- Selecting a machine learning model (algorithm & settings)
- Training a model
- Evaluating a model
- Making predictions

You’ll emerge from the course having built apps that can:
- Recreate Gmail's spam identifier
- Predict the availability of taxis near you at specific timings
- Predict the PSI in Singapore at a specific time of the day

As part of the course, you will be taught key data libraries - Pandas, Numpy and Matplotlib. These libraries are responsible for data loading, mathematical computations and data visualisation respectively.

Basic statistic knowledge and web service development will also be taught to help you understand the advanced concepts in Data Science I (Python) and start their career in data science.

What is SSNC?

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Course Plan

Lesson 1: Reading Reports in Python

Students will be taught how to use Python to do file processing, including reading in tabular (CSV, Excel) data.

Lesson 2: Basic Statistics Explained

We will be covering basic statistics knowledge, such as mean medium, mode, and looking at doing regression models.

Lesson 3: HTTP and Web Services

Digressing a little from statistics, we will look at how to build web servers using Python. This lesson will teach us simple HTML structures, that we can use for crawling and mining data on web pages.

Lesson 4: Collaborative Filtering

For this lesson, we will be looking at how websites build recommendation services. e.g. Amazon's "You might also be interested in purchasing ...".

We will be working on a movie database and learning how to use collaborative filtering to make recommendations. At the end of the class, we will be able to correctly recommend movies based on their ratings.

For example, when a user rates "Star Wars: A New Hope" 10/10, our code will be able to recommend "Star Wars: The Empire Strikes Back".

Lesson 5: Web Scraping

Building off last week's lesson, you will learn how to write scripts which will allow you to mine web data by crawling HTML pages and using it to build a database of information.

Lesson 6: Working with a Classifier Web Service

In the last lesson, we will combine all that we learnt so far to build a classifier web server. We will be using the web service to process data and make predictions.

Lesson 7: Combined Workshop

Students will find out how they can use what they have learnt in the past six sessions to further their goals.

1. Meet industry experts
2. Learn what's next after Data Science Introduction
3. Find like-minded individuals who want to work on guided data science projects (with UpCode instructors)



April 1st, 2019
(6 weeks)
Location The Cathay
Lesson 1 01/04/19
07:30pm - 10:00pm
Lesson 2 08/04/19
07:30pm - 10:00pm
Lesson 3 15/04/19
07:30pm - 10:00pm
Lesson 4 22/04/19
07:30pm - 10:00pm
Lesson 5 29/04/19
07:30pm - 10:00pm
Lesson 6 06/05/19
07:30pm - 10:00pm
Lesson 7 07/05/19
07:30pm - 09:30pm


This course requires a basic understanding of Python. You should have completed our Python Development course or already have an intermediate-level understanding of Python.

If you’ve not attended our Python Development course, our friendly Student Affairs Officer will get in touch upon your registration to confirm your mastery of the Python language.

About this Course:

This course serves as a starter for students looking to move on to Data Science I (Python), and pays special attention to specialised Python libraries for data retrieval and visualisation techniques.

By the end of this special edition of the course, participants of the Singapore Smart Nation Challenge (SSNC) will be ready to build a functional prototype required for the challenge. Participants of the SSNC will also receive personal mentorship with an instructor in the product ideation and building stages.

Everyone is welcome to join this course, whether or not you've registered for the Singapore Smart Nation Challenge!

Find out more about the Singapore Smart Nation Challenge (SSNC) here.

What You Get

Students can also look forward to receiving course materials that will serve as useful reference for post-course revision and practice.

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