Reference no: EM132478039 , Length: 6 pages

COSC 2670 - Practical Data Science with Python - RMIT University

Introduction

In this assignment, you will examine a data file and carry out the first steps of the data science process, including the cleaning and exploring of data.

You will need to develop and implement appropriate steps, in IPython, to load a data file into memory, clean, process, and analyse it.
This assignment is intended to give you practical experience with the typical first steps of the data science process.

The "Practical Data Science" Canvas contains further announcements and a discus- sion board for this assignment. Please be sure to check these on a regular basis - it is your responsibility to stay informed with regards to any announcements or changes.

Part 1: Data Preparation
Have a look at the file StarWars.csv, which is available in Canvas under the Assignments
-> Assignment 1 section of the course Canvas.
This file contains data behind the story America's Favorite ‘Star Wars' Movies (And Least Favorite Characters)1. The author collected the data by running a poll through SurveyMonkey Audience, surveying 1,186 respondents. The description of the questions asked in the survey is given below.

• Have you seen any of the 6 films in the Star Wars franchise?
• Do you consider yourself to be a fan of the Star Wars film franchise?
Which of the following Star Wars films have you seen? Please select all that apply. (Star Wars: Episode I The Phantom Menace; Star Wars: Episode II Attack of the Clones; Star Wars: Episode III Revenge of the Sith; Star Wars: Episode IV A New Hope; Star Wars: Episode V The Empire Strikes Back; Star Wars: Episode VI Return of the Jedi)

Please rank the Star Wars films in order of preference with 1 being your favorite film in the franchise and 6 being your least favorite film. (Star Wars: Episode I The Phantom Menace; Star Wars: Episode II Attack of the Clones; Star Wars: Episode III Revenge of the Sith; Star Wars: Episode IV A New Hope; Star Wars: Episode V The Empire Strikes Back; Star Wars: Episode VI Return of the Jedi)

Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her. (Han Solo, Luke Skywalker, Princess Leia Organa, Anakin Skywalker, Obi Wan Kenobi, Emperor Palpatine, Darth Vader, Lando Calrissian, Boba Fett, C-3P0, R2-D2, Jar Jar Binks, Padme Amidala, Yoda)
• Which character shot first?
• Are you familiar with the Expanded Universe?
• Do you consider yourself to be a fan of the Expanded Universe?
• Do you consider yourself to be a fan of the Star Trek franchise?
• Gender
• Age

• Household Income
• Education
• Location (Census Region)
Being a careful data scientist, you know that it is vital to carefully check any available data before starting to analyse it. Your Part is to prepare the provided data for analysis. You will start by loading the CSV data from the file (using appropriate pandas functions) and checking whether the loaded data is equivalent to the data in the source CSV file. Then, you need to clean the data by using the knowledge we taught in the lectures. You need to deal with all the potential issues/errors in the data appropriately.

Part 2: Data Exploration

Explore the provided data based on the following steps:

1. Explore the survey question: Please rank the Star Wars films in order of preference with 1 being your favorite film in the franchise and 6 being your least favorite film. (Star Wars: Episode I The Phantom Menace; Star Wars: Episode II Attack of the Clones; Star Wars: Episode III Revenge of the Sith; Star Wars: Episode IV A New Hope; Star Wars: Episode V The Empire Strikes Back; Star Wars: Episode VI Return of the Jedi), then analysis how people rate Star Wars Movies.
2. Explore the relationships between columns. You need to choose 3 pairs of columns to focus on, and you need to generate 1 visualisation for each pair. Each pair of columns that you choose should address a plausible hypothesis for the data concerned.
3. Explore whether there are relationship between people's demographics (Gender, Age, Household Income, Education, Location) and their attitude to Start War characters.

Note, each visualization (graph) shoul be complete and informative in itself, and should be clear for readers to read and obtain information.

Part 3: Report
Write your report and save it in a file called report.pdf, and it must be in PDF format, and must be at most 6 (in single column format) pages (including figures and references) with a font size between 10 and 12 points. Penalties will apply if the report does not satisfy the requirement. Moreover, the quality of the report will be considered, e.g. clarity, grammar mistakes, the flow of the presentation.
Remember to clearly cite any sources (including books, research papers, course notes, etc.) that you referred to while designing aspects of your programs.
• Create a heading called "Data Preparation" in your report.
- Provide a brief explanation of how you addressed the Part. For the steps of dealing with the potential issues/errors, please create a sub-section for each type of errors you dealt with (e.g. typos, extra whitespaces, sanity checks for impossible values, and missing values etc), and also explain and justify how you dealt with each kind of errors.

• Create a heading called "Data Exploration" in your report.
- For each numbered step in Part 2 above, create a sub-section with correspond- ing numbering.

Attachment:- Practical Data Science with Python.rar