ICT110 Introduction To Data Science Assignment

Assignment Task

You are a member of the team and need to perform data analysis on selected attributes.

Although the team has not set any specific goal for the analysis, but they are interested in exploring the following research objectives:

1. What are the interesting and/or significant facts/patterns/trends about emissions in Australia over the years?
2. What are the interesting and/or significant facts/patterns/trends about energy production and use in Australia over the years?
3. Is there any correlation between energy production/use and emissions?

You have been requested to prepare a data analysis report about your work and explain your findings. The potential audiences include other researchers, business representatives, and government agencies. They may have limited ICT or mathematical knowledge. Therefore, the report should be technical but have clear explanations describing the findings.

Note:

not all columns are related to this purpose. Some attributes have missing data for some years.

For detailed explanations to the data set attributes, please refer to the metadata file. To prepare the report, please include the following sections:

1. Introduction

Provide an introduction to the problem. Include background material as appropriate: who cares about this problem, what impact it has, where does the data come from, what are the dimensions and structure of the data.

2. Data Setup

Describe how to load the data, and how the pre-processing is performed. The original dataset is not ready for analysis and it is different from the data forms that we are familiar with in previous practices. This means we need to do some pre-processing, either for the whole dataset, or for a subset of the dataset required for each sub task described later.

Once you have some ideas of exploratory or advanced analysis, you need to adjust the form of dataset. This can be achieved either by manipulating records in R by transposition or subsetting, or with other tools (e.g. notepad or excel) before reading them into R. For simplicity, you can also rename the attribute names. Please clearly explain the way you have cleaned the data in this section. If you use Excel please still explain the steps that you used for cleaning.

3. Exploratory Data Analysis

3.1.

Two, one-variable analyses with graphs One-variable analysis studies one variable (one column/attribute) each time. It is up to you to decide which attribute/variable you use for this analysis but the attribute you select need to be related to the research objectives.

• Perform 2 one-variable analyses and graph them. One analysis is about emissions and one analysis is about energy production/use.
• Explain the findings for each graph.
• Provide the code for each graph.

3.2.

Two, two-variable analyses with graphs A two-variable analysis studies the relation between two variables. It is up to you to decide which attributes/variables you use for this analysis but the attributes you select need to be related to the research objectives.

• Perform 2 two-variable analyses and graph them. If you include Year in the analysis, please remember the time is also a variable.
• Explain the findings for each graph.
• Provide the code for each graph.

4. Advanced Analysis

4.1.

Two, Linear regression analyses with graphs Briefly explain the concept of linear regression (with references). It is up to you to decide which attributes/variables you use for this analysis but the attributes you select need to be related to the research objectives.

• Perform 2 linear regression analyses and graph them.
• Explain the findings for each graph.
• Provide the code for each graph.

4.2.

Clustering

Briefly explain the concept of clustering and k-means (with references). Perform 1 clustering analysis. It is up to you to decide which attribute(s) you use for this analysis but the attribute(s) you select need to be related to the research objectives.

• Explain the clustering result and findings. No need to graph it.
• Provide the code for the clustering.

5. Conclusion

Sum up your findings and provide some insight into the findings.

6. Reflections

In this part, discuss any difficulties you had performing the analysis and how you solved those difficulties. Reflect on how the analysis process went for you, what you learnt, and what you might do differently next time. Aim to write 2-4 paragraphs.

For the data analysis (Section 3 & 4), you need to provide both R code, the explanation to the code, and the result. Please represent each R code snippet in your report using a box with some comments. For example:

# Draw a boxplot on the attribute “Income”
boxplot(MyData$income)

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