Data Science Introduction (Python)

The course bridges the gap between programming and data science, focusing on the libraries for introducing data science, web development and basic statistics knowledge.

  • TBD
  • 6 sessions
  • 2.5 hours/session
  • Data Science

What is Virtual Classroom?

This course will be taught using a combination of video presentation, video online conferencing, screen sharing and virtual technology. We replicate the complete experience of a classroom right inside your living room!

During the virtual classroom lesson, you will be given a link to call in and speak to the instructor at the scheduled timings. The instructor will be guiding the lesson through prepared videos, and also hands on teaching to ensure that you will have a complete learning experience.

Learn from the comfort and safety of your own home during the COVID19 pandemic!

Objectives

A level up from our Python Development course, our Data Science Introduction (Python) course will teach students to build a simple yet functional web application. Working within the parameters set by the individual instructors, this fun project will see students building three web applications: a spam email classifier, taxi availability predictor and an air pollution index predictor.

Lessons

About this course

This course pays special attention to specialised Python libraries for data retrieval and visualisation techniques. Statistic knowledge will also be taught to help students understand the advanced concepts of Data Science.

As part of the course, students will also be taught key data libraries, namely Pandas, Numpy and Matplotlib; these libraries are responsible for data loading, mathematical computations and data visualisation respectively.

Students can also look forward to receiving course materials that will serve as useful reference for post-course revision and practice. At the end of the course, students will be taught the basics of web service development and will be equipped with the basic tools needed to start their career in data science.

Prerequisites

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

If you have not sat our Python Development course, a Student Affairs Officer will reach out to you upon registration to confirm your mastery of Python.
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