DAT107 Data Analytics and Visualization with SQL & Python Overview In this final project, you will submit a slide deck and an accompanying video presentation that showcases your capacity to analyse and communicate insights from a dataset using Python relating to a specific business question

DAT107 Data Analytics and Visualization with SQL & Python

Overview

In this final project, you will submit a slide deck and an accompanying video presentation that showcases your capacity to analyse and communicate insights from a dataset using Python relating to a specific business question.

This project will test your capacity to develop data insights demonstrated by your understanding of the data project lifecycle.

In your video submission, you will present your data story, as if to an Executive Team, accompanied by the slide deck and other supporting documentation you develop.

The project will share your data story clearly, with a focus on providing insight into the business question that has been posed.

Assessment criteria

This assessment will measure your ability to:

• define the right type of questions to support business decision-making
• acquire and evaluate relevant data
• clean and format data in preparation for analysis
• apply analytics on data using Python and selected libraries
• visualize business data using Pandas
• Communicate visual data interpretation to business decision-makers.

Course learning outcomes

This assessment is relevant to the following course learning outcomes:

• CLO1 Utilise SQL and Python to drive powerful analysis and predictions for business.
• CLO2 Assess and implement data modelling, forecasting and classification using introductory level SQL and Python functions.
• CLO3 Build and apply a simple data analytics model to solve a defined business problem.

This project requires you to analyse a set of data to address a specific business question.

Option One

You will be supplied with a dataset and a specific business question. The dataset is dataset20172020.csv

Business question for Option One

SuperFoodsMax decision-makers ask the data team to provide insights that can support a strategy to lift sales revenue by 5% over the next two years.

Key personnel you consult suggest focussing on loyal (existing) customers and the conversion of new customers into loyal customers.

They reveal that:

• a failure to lift the average spend of loyal customers is stagnating revenue growth
• first-time customers are not converting into regular (loyal) customers.

Option Two

You supply your own dataset and specific business question. You will need to check your dataset and proposed business question with your mentor before attempting the assessment task.

Overview

This project is in three parts.

1. Slide deck: Your slides should address the following:

• Your understanding of the business question.
• Justification for how your understanding of the business question guided your identification and selection of relevant internal and external data.
• A strategy for addressing any ethical, privacy and legal issues with data you acquired, used, or handled during the data project.
• The process used to clean and format data, ready for analysis and any issues encountered.

• Provide a range of data visualisation in Python and associated libraries that clearly demonstrate how you shaped and transformed data for easier interpretation by your audience.

• Your data project findings from your data visualisation returned from Python and associated libraries that provide clear insights for decision-makers to address the business question confidently. (The PowerPoint presentation should contain ten slides or less.)

Supporting documentation Your supporting documentation should include the following:

Legal and ethical considerations

Provide a 150-word description of how your treatment of data is in line with legal and ethical considerations.

Cleaning and Exporting data

Provide a 150-word report that describes types of ‘dirty data’ and steps you could use to clean it.

Visual Data:

Export the code run for all transformations and visualisations returned by Python that are included in your slides.

2. Video

Record a 3–5 minute video of the presentation using your slide deck contents. Your video should include your presentation of your data project analysis, including visualisation. You may draw on the work you’ve done for each milestone and the artefacts and insights you’ve produced during each stage of the design process.

Submission format

1. Submitting the slides

The PowerPoint presentation that accompanies your video file should be uploaded to Canvas Assignment The name of the PowerPoint file should be: Report<Student Name>.ppt

2. Submission of supporting documentation

The supporting documentation should be uploaded to Canvas Assignment 1 in one Word document. The name of the Word file should be: Documentation<Student Name>.doc

3. Submission of Python code

Your Python code should be exported from Jupyter Notebook as a PDF or HTML file and named: Code<Student Name>.pdf/html

4. Submission instructions for the video Your video should include your presentation of your data project analysis, including visualisation.

Use your laptop/desktop to record your screen and your voice as you click through the slides in your presentation and talk to the key points on each slide. You can include footage of yourself talking directly to your computer camera as you progress through the slides.

1. To record your response, please use the Studio record tool (on the left-hand side navigation panel in Canvas). Refer to these instructions on how to record your video directly in Studio or how to upload an existing video in Studio (if, for example, you use another tool such as screencast-o-matic.)
2. When your video is uploaded, click on Share and Create Public Link.
3. Ensure you paste this link in your supporting documentation Word document and submit with your slides.

Referencing guidelines

If you source any material from a primary or secondary source, please include the information in your submission. You must acknowledge all the sources of information you have used in your assessments and use RMIT Harvard referencing style for referencing.

Refer to the RMIT Easy Cite referencing tool to see examples and tips on how to reference in the appropriated style. You can also refer to the library referencing page for more tools such as EndNote, referencing tutorials and referencing guides for printing.

Academic integrity and plagiarism

Academic integrity is about honest presentation of your academic work. It means acknowledging the work of others while developing your own insights, knowledge and ideas.

You should take extreme care that you have:

• Acknowledged words, data, diagrams, models, frameworks and/or ideas of others you have quoted (i.e. directly copied), summarised, paraphrased, discussed or mentioned in your assessment through the appropriate referencing methods
• Provided a reference list of the publication details so your reader can locate the source if necessary. This includes material taken from Internet sites

If you do not acknowledge the sources of your material, you may be accused of plagiarism because you have passed off the work and ideas of another person without appropriate referencing, as if they were your

own.                       

RMIT University treats plagiarism as a very serious offence constituting misconduct. Plagiarism covers a variety of inappropriate behaviours, including:

• Failure to properly document a source
• Copyright material from the internet or databases
• Collusion between students

For further information on our policies and procedures, please refer to the University website. Assessment declaration

When you submit work electronically, you agree to the assessment declaration.

Define questions to support the business question

Evaluate business request for data analysis to support informed decision making.

Measurable, clear, and concise questions are established to answer a clearly defined business problem.

Measurable, clear, and concise questions are not established to answer a clearly defined business problem.

1 pts                                                                                                                                             0pts

Clean and format data

Convert, clean and format data to produce analysis.

Anticipate and address legal and ethical considerations when using and handling data at all stages of the data project.

Clear criteria for cleaning data established and applied to rectify ‘dirty data’ in the dataset.

Use of private data is clearly demonstrated throughout the data project to be in line with industry, organisational, ethical, and legal guidelines.

Clear criteria for cleaning data are not established and/or not applied to rectify ‘dirty data’ in the dataset

Use of private data is not clearly demonstrated throughout the data project to be in line with industry, organisational, ethical, and legal guidelines.

1 pts                                                                                                                                             0 pts

1 pts                                                                                                                                             0 pts

All models, charts and graphics created are           Models, charts and graphics created are not

appropriately selected, statistically             appropriately selected, statistically

sound and correctly labelled                                sound and or correctly labelled

and visually pleasing.                                                    and not visually pleasing.

1 pts                                                                                                                                             0 pts

Communicate and present data project analysis

Presentation includes visualisation exported from Python, Pandas and or Numpy that enable data analysis insights against the business question.

Presentation includes visualisation exported from Python, Pandas and or Numpy are not provided or do not enable data analysis insights against the business question.

Communicate visual data interpretation to business decision-makers.

Structure and organisation present the main points in a logical and clear and persuasive way that is appropriate to the audience.

Report is presented confidently and enthusiastically to business audience.

The presenter has clearly practised the material and delivers their results confidently.

Structure and organisation do not present the main points in a logical and clear and persuasive way and or are not appropriate to the audience.

Report is not presented confidently and or enthusiastically to business audience.

The presenter has not practised the material and did not deliver their results confidently