MIS304 Assignment Help
| ASSESSMENT 1 BRIEF | |
|---|---|
| Subject Code and Title | MIS304 Information Systems for Business (Advanced |
| Assessment | Data Exploration Task |
| Individual/Group | Individual |
| Length | 1000 words (+/- 10%), supporting visuals |
| Learning Outcomes | The Subject Learning Outcomes demonstrated by successful completion of the task below include:
|
| Submission | Due by 11:55pm AEST/AEDT Sunday end of Module 2.2 (Week 4) |
| Weighting | 20% |
| Total Marks | 100 marks |
Assessment Task
You are required to search and choose a suitable public dataset. You will clean and format the data, create visualisation charts, and report on key information contained in the data. You will also complete and submit a report describing the process that you followed, the analysis you completed, and your findings.
Please refer to the instructions section below for details on how to complete this task.
Context
Data exploration, also known as “exploratory data analysis”, is the first step in data analysis where a set of simple tools are used to achieve a basic understanding of data, data types, formats, and structure of a dataset. The results of data exploration can be extremely useful in grasping the structure of the data, the distribution of the values, the presence of outliers, and the
interrelationships within the dataset. Simple descriptive statistics are useful in exploring numerical data, especially to know their averages, frequencies, and variabilities.
In Modules 1 and 2, you were introduced to data, types of data and data formats. You learnt about the need for exploring datasets before starting to use them for any analysis purposes and about methods and approaches to explore and handle data. Assessment task 1 will allow you to demonstrate your understanding of data analysis and data exploration on a public data set. You will also be given the opportunity to apply these skills to a real data set.
Instructions
To successfully complete assessment task 1, you will need to review resources and content from Module 1.1 (Week 1) to 2.1 (Week 3) to recall the aspects and skills discussed under the topics of data exploration, data charts and data handling.
To complete this assessment task, you must follow the steps below:
Step 1: Find and select a public dataset
You will use a public dataset for this assessment task. Each team will choose a different dataset. The learning facilitator will assist you in finding suitable databases and websites. • Download your chosen dataset in an excel format.
- Save the dataset as an Excel file onto your computer for further analysis.
- Make sure to save the link for the dataset for your reference. You will need to provide this link in section 1 of your report (see step 4).
Step 2: Explore and review the dataset
Open your chosen dataset with Excel and manually investigate the dataset as follows: • Describe the data and its context.
- List available data types and key attributes.
- Compute descriptive statistics on numerical data columns.
- Check for consistency, errors, and missing values, and confirm the validity of the data.
If you identify any issues, you will need to document them in section 1 of your report (see step 4). You are also required to state why you think they are issues and how they can be fixed.
Step 3: Create and interpret visualisation charts
From your review in Step 2, formulate 4-5 investigative questions that you can answer using your chosen dataset. For example, if you are exploring a sales dataset, your questions may be:
– Which product made the most profit in a particular month? or
– What is the best distribution fit for profit variable?
Using Excel, you will create visualisation charts that will help you answer each of the questions you prepared. Include titles, axis, and legends with detail on each visualisation chart. Interpret the charts or graphs to answer each of your questions and document all your findings and interpretations in section 2 of your report (see step 4).
Step 4: Document your findings and write a report (1000 words)
You must include the following sections with the correct headings and relevant content in your report:
Section 1: Selected Dataset
- Provide a link to data.
- Explain why you selected this data.
- Explain what the issues are in the selected data and how they can be fixed.
- Document all findings from Step 2.
Section 2: Analysis Plan
- Document your investigative questions from Step 3 and the reasons you think these questions are important.
- Copy your relevant Excel visualisation charts for each of your questions into this section. Beneath each chart, ensure that you:
– include objectives for each graph and visualisation charts, and
– briefly explain what information and/or knowledge you obtained
from the charts.
- Explain your interpretations using appropriate terminology and clear language.
- Ensure that you document all work done in Steps 2 and 3.
- Section 3: Findings and Limitations
- Summarise your findings and list their limitations.
- Provide recommendations for further analysis if required.
Section 4: Reference List
- Ensure secondary research is referenced using APA referencing guidelines.
Use Microsoft Word for the layout of your report. Ensure your report is 1000 words (+/-10%). The dataset and created charts will be submitted as a separate Excel document.
You will need to structure the content and visualisation charts so that the information is clear and logical.
| Assessment Attributes | Fail (Yet to achieve minimum standard) 0-49% | Pass(Functional)50-64% | Credit(Proficient)65-74% | Distinction(Advanced)75-84% | High Distinction (Exceptional) 85-100% |
|---|---|---|---|---|---|
| Understanding the context of data exploration and visualisation charts. Choice of suitable dataset, rationale and data description Percentage for this criterion = 20% | Demonstrates limited or no understanding of key concepts of data exploration and visualisation charts. Reasons may include:
• Key components are not addressed and/or contain many errors. • A suitable dataset is not chosen. • Source of data not provided. |
Demonstrates limited understanding of key concepts of data exploration and visualisation charts. Reasons may include:
• Some key components are only addressed. • Chosen dataset is suitable for the purpose but the source not provided. • Data context and rationale are not provided or have many errors. • Data description attempted and few data types and attributes are listed but with many errors. |
Demonstrates adequate understanding of key concepts of data exploration and visualisation charts. Reasons may include:
• Most key components are addressed but limited in detail. • Chosen dataset is suitable for the purpose and source provided. • Data context and rationale are provided; a few errors are present. • Data is described and data types and attributes are listed with some details. |
Demonstrates proficient understanding of key concepts of data exploration and visualisation charts. Reasons may include:
• Most key components are addressed in detail with only 1 or 2 errors. • Chosen dataset is suitable for the purpose and source provided. • Data context is generally explained and a strong rationale provided. • Data is described in detail and most data types and attributes are listed; 1 or 2 minor errors are present. |
Demonstrates highly developed understanding of key concepts of data exploration and visualisation charts. Reasons may include:
• All key components are addressed in detail. No errors are present. • Chosen dataset is highly suitable for the purpose. The data source is provided with details. • Data context is explained in detail and a strong rationale provided. • Data is described in detail and all data types and attributes are listed correctly |
| Data exploration and data handling Percentage for this criterion = 30% | • Limited or no attempt to explore the dataset based on simple descriptors, descriptive statistics or visual charts.
• Does not present an analysis of data issues. • No attempt to identify and fix errors, omissions, outliers or missing data. |
• Attempts to explore the dataset with few simple descriptors like distribution, range, etc. but contains errors.
• Presents few data issues but not applicable to this dataset. • Attempts to identify and fix errors, omissions, outliers, or missing data but with limited success. |
• Explores dataset using many descriptors that used more than one variable e.g., scatter plots, and a few basic descriptive statistics; three or more errors.
• Presents data issues identified and applicable to this dataset. • Identifies and fixes some errors, omissions, outliers, or missing data, but more of these can be identified and fixed. |
• Explores dataset using many descriptors that used more than one variable e.g., scatter plots, and a few basic descriptive statistics; 1 or 2 errors.
• Presents and analyses data issues applicable to this dataset. • Identifies and fixes most errors, omissions, outliers, or missing data. |
• Explores dataset using many descriptors that used more than one variable e.g., scatter plots, and a few basic descriptive statistics; no errors.
• Presents a sophisticated analysis of all data issues applicable to this dataset. • Identifies and fixes all errors, omissions, outliers, or missing data. Demonstrates thorough and critical thinking regarding the best approaches to handling these issues. |
| Creation of investigative questions, and visualisation charts to answer them with findings and interpretations. Percentage for this criterion = 30% | • Limited or no questions listed.
• Provides limited or no visualisation charts. • Limited or no findings and /or interpretations, or recommendations included. |
• Limited or few simple questions listed.
• Provides one or two simple visualisation charts using very few variables in the dataset which are not linked with objectives or questions. • Some titles, axes, and legends are added for meaning to the charts. • Some findings reported but sometimes irrelevant to the charts or lacking in meaning. Limited recommendations. • Minimal insights or interpretations of findings presented. |
• Up to 3 investigation questions listed based on a few data variables.
• Provides three or four visualisation charts using a few variables in the dataset which are linked with relevant objectives or questions. • Title, axes, and legends are partially provided. • Includes mostly relevant findings and some meaningful recommendations derived from each chart. • Some insights or interpretations of findings presented; a few errors. |
• At least four investigation questions listed based on a variety of data variables.
• Provides a variety of visualisation charts, using most variables in the dataset which are clearly and meaningfully linked to relevant objectives or questions. • Title, axes, and legends are provided with 1 or 2 errors. • Includes all relevant findings and several meaningful recommendations derived from the charts. • A few insights or interpretations of findings presented; 1 or 2 errors. |
• Five investigation questions listed based on a variety of data variables.
• Provides a variety of visualisation charts, using all possible variables in the dataset which are clearly and meaningfully linked to relevant objectives or questions. • Title, axes, and legends are provided with correct details, • Includes all relevant findings and many meaningful recommendations derived from the charts. • Insights or interpretations of findings are well presented with detailed explanations and no errors. |
| Report writing, layout and presentation. Percentage for this criterion = 20% | • Information, analysis, and findings are not presented or presented in a way that is unclear, confusing and /or limited.
• Most sections and/or subsections are missing. • Layout lacks cohesion and is difficult to follow. • Referencing is omitted or does not follow APA guidelines • Does not meet word limit requirements. |
• Information, analysis, and findings are mostly presented clearly and logically although may be confusing at times.
• May not include some required subsections of the report. • Layout is mostly structured but can be confusing, disjointed and/or cluttered at times. • APA referencing guidelines used but contains many errors.
requirements. |
• Information, analysis and findings are adequately presented.
• Includes most sections and/or subsections of the report; contains a few errors. • Layout is well structured with most content logically sequenced. • APA referencing guidelines used but contains a few errors. • Meets word limit requirements. |
• Information, analysis and findings are well presented.
• Includes sections and subsections of the report; only 1 or 2 errors. • Layout is well structured with content logically sequenced and easy to follow. • APA referencing guidelines used; contains only 1 or 2 errors.
requirements. |
• Information, analysis and findings are expertly presented.
• Includes all sections and subsections of the report; no errors. • Layout is well structured and all content is sequenced effectively and skilfully to a professional standard.
guidelines used; contains no errors.
requirements. |
