辅导 37989 Digital Business & Business Analytics讲解 Python程序

Assignment Remit

Assignment Remit

This assignment is based around a practical task. You are asked to source a set of data, clean, manipulate, and use it to produce insights that would be useful to a specific audience or for a defined purpose. You need to produce a report of the process undertaken and the tools you have used for collecting, processing, and analysing data. For this assignment, each student is required to:

1.   Begin by carefully selecting a dataset to employ for this assignment. Your

selection can be from any sources so long as there are no copyright restrictions that limit the use of the data. The dataset(s) you select should be those that you think are interesting to a particular group of people. You can find your own sources and take some guidance from lecture material. Other sources of useful  datasets might be datasetsearch.research.google.com, www.data.gov.uk or the  World Bank Open Data repository, Kaggle.com, etc. You can scrape data from   public websites where this is appropriate - but we are looking for large datasets  (more than 500 observations/records with more than 8-10 variables/features) as a key element of your data analytics work... not just a few numbers.

NB- Avoid redundancy by ensuring that the chosen dataset differs from the one utilized in your work group assignment. In other words, refrain from

using the same dataset for both Individual and Group Work Assignments.

2.   The dataset you find is unlikely to be in a format useful for the task. You should clean and edit the data to improve the data quality so that it is suitable for your  intended purpose. You may use any tool to clean the data including (but not necessarily limited to) Excel, Python, R or the data cleaning tools embedded in Tableau data visualization software.

3.   Use Excel or Tableau (or both) to explore the data. The exploration/visualisation

may take the form. of charts, tables, maps or other visualisations plus brief narrative as text boxes to explain key messages. To undertake this part of the task you should use Excel or Tableau software and the features in it (dashboards, stories etc) to design visual representations of your data that best convey the key messages in it. Use the interactive features of Tableau (tooltips for example) where appropriate.

4.   The Narrative in the Excel worksheets or Tableau file itself should supplement

your visualisations and help convey key messages; it should not talk about the  logistics of building your file your audience are not interested in that – save that for the data analytics report below.

5.   Select and apply appropriate data analytics techniques based on the nature of    the business problem and the goals of the analysis. This may involve tasks such as regression, classification, or clustering, depending on the type of insights you aim to derive. For regression tasks, analyse numerical outcomes based on the    identified variables. For classification tasks, categorize data into distinct groups   or classes based on relevant features. For clustering tasks, identify natural groupings or patterns within the data. For time series analysis, focus on understanding patterns and trends over time, taking into account trends, seasonality and other temporal factors.

Structure of your data analytics report (2,500 words maximum):

Chapter 1- Business Understanding:

.    Briefly introduce the industry/company/organisation.

.     Express the primary goal of the data analytics.

.     Explain business (or organizational/industrial) problem that is being addressed,

.     Detail who the target audience is and the purpose for which they might use the data analytics results.

Chapter 2- Data Understanding:

.     Explain why you chose the dataset(s) you did. You must provide a link to the dataset(s) used.

.     Describe the data: its size in terms of number of records (observations) and variables (features). List the name of variables, briefly define them, and describe their data formats.

Please note that dataset must be different from the dataset that you have used Work Group Assignment. In other words, you can’t use the same dataset for

the Individual and Group Work Assignments.

Chapter 3- Data Preparation:

.     Explain the process you used to clean, edit or constructing the data. If you discarded any data say why this was done.

.     If you merged or integrate data sets explain how and why you did this.

.     Describe what problems you encountered and how you overcame them.

Chapter 4- Exploratory Data Analysis:

.     Use the methods of descriptive analytics and visualisation (such as

crosstabulation, histogram, bar charts, line charts, scatterplot, heat maps, PivotCharts, etc) to explore the data.

.     Explain why you used the visualisation design that you selected. What

made these the best designs for the visualisation you prepared and how did they meet the needs of the audience?

Chapter 5- Modelling:

.     Depends on the business problem and dataset, apply regression, times series analysis, classification or clustering techniques to build business analytics model(s).

Submission guidance:

1.   If you use Tableau, your Tableau presentation should be submitted as a link at  the top of your report. This link should link directly to the Tableau data dashboard that you have saved in the Tableau Public cloud gallery.

2.   I also would like to see screengrabs of the Dashboard / visualisation pages in your word submission to help give the word document context.

3.  You must also provide a link to the source dataset(s) you used - otherwise I cannot audit the validity of your data and you will drop marks.

4.  Your report should be submitted in the form of a Word document, use minimum 12pt font, and at least 1.2 line spacing.

Module Learning Outcomes:

This assignment tests the following module learning outcomes:

.    Collect, analyse and interpret data analytics to make informed business decisions.

.    Appraise  how  digital  business  and  data  analytics  can  be  used  to  generate actionable insights for managers and decision-makers.

.    Communicating, presenting and disseminating analysis of the data.

Grading Criteria:

Your work will be assessed on these criteria:

1.   Have the business (or organization/industry), its problems, the primary purpose   of data analytics, and the target audience of the results described appropriately?

2.   Have relevant data sets been selected that for the goal of data analytics? Has

this selection been justified? If the data set has been created (for example through scraping) has this process been explained and any code used included?

3.   Have appropriate techniques and tools been used to edit and clean the data? Is it clear why these techniques were used?

4.   Have appropriate visualizations been produced to explore the data and

undertake descriptive analytics?

5.   Have appropriate techniques been applied for analysing and building business

analytics model(s)?

More detail on the assessment criteria is shown in the rubric in canvas.

Feedback to Students:

Both Summative and Formative feedback is given to encourage students to reflect on their learning that feed forward into following assessment tasks. The preparation for all assessment tasks will be supported by formative feedback within the tutorials/seminars.

Written feedback is provided as appropriate. Please use the browser for canvas feedback and not the Canvas App as you may not be able to view all comments.

Plagiarism:

It is your responsibility to ensure that you understand correct referencing practices. You are expected to use appropriate references and keep carefully detailed notes of all your  information sources, including any material downloaded from the Internet. It is your responsibility to ensure that you are not vulnerable to any alleged breaches of the assessment regulations. More information is available at https://intranet.birmingh am.ac.uk/as/studentservices/conduct/misconduct/plagiarism/index.aspx.