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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.

5

Created By : ZP

1st Week of November, 2018

Part Time

6 weeks

2.5 h/session

Data Science Introduction (Python)

5 1st Week of November, 2018 Part Time 6 weeks 2.5 h/session

Objectives

At the end of the course, students will build a simple functional web application project which will be used to perform a Data Science functionality, namely a "classification" or "prediction" engine. This project will be done in class, under the supervision of the instructor.

This web project is open ended, working within the parameters defined by the instructor.
Examples include:
1. A spam email classifier.
2. Taxi availability predictor.
3. Air pollution index PSI predictor.

You will be able to put it on your resume for getting into Data Science related jobs.

Skils you will learn:

- Preparing data: Cleaning, transforming, etc.
- Data retrieval: HTML, API-based
- Data extraction: XML, JSON, HTML
- Basic mathematical statistics
- Intermediate level data visualisation
- Creating simple web services

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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: 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 5: Generating and Visualising Data

We will look at how to use Python to visualise and present data in graphs and reports. We will also look at building realtime dashboards to measure data.

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.

Timetable

COHORT

November 3rd, 2018
Lesson 1 03/11/18
01:00pm - 03:30pm
Lesson 2 10/11/18
01:00pm - 03:30pm
Lesson 3 17/11/18
01:00pm - 03:30pm
Lesson 4 24/11/18
01:00pm - 03:30pm
Lesson 5 01/12/18
01:00pm - 03:30pm
Lesson 6 08/12/18
01:00pm - 03:30pm

Prerequisites

This course requireds a basic understanding of Python. A Student Affairs Officer will reach out to you upon registration to confirm your mastery of Python.

Otherwise, students will be required to graduate from our Python Development Course.

About this Course:

The course bridges the gap between Python Development and Data Science I (Python), paying special attention to the specialized Python libraries for data retrieval and visualization techniques, as well as basic statistics knowledge necessary to fully understand the advanced concepts in the latter course.

Students will be taught intermediate working knowledge of key data libraries: pandas for data loading and reporting, numpy for mathematical computations and statistics and matplotlib for data visualization. Students will also be introduced to the process of retrieving data from internet sources, both API’s and through Web Scraping techniques. Students will understand extraction of data from common data storage standards such as JSON, XML and HTML. The provided course materials also will serve as a useful reference (of practical examples) that supports post-course revision and practice. Students will be taught the basics of web service development for publishing data. Students who gain competence in the materials will have the required programming background necessary to participate in advanced pure Data Science coursework.

Assessment

A passing requirement is to solve a small problem requiring data exploration, reasoning, and articulating the thought process leading to a solution.

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