Complete Business Intelligence Justification - Business Assignment Help

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Assignment Task :

Coursework 1 

Lab Assessments 

Two equally weighted scaffolded lab based assignments will build up a portfolio  of evidence of skills that are business intelligence related exercises, that, for  example, involve analysis and visualization of datasets from sources such as  transactional databases, data warehouses or open datasets, and highlight the  value of the information extracted from the data.  

Each scaffolded staged submission to this portfolio will be provided formative  and qualitative feedback within the required timeframe which will act as  feedforward into the next stage to build up the students’ confidence and skills  within this area. These will occur around Week 5 for Assignment 1 and Week 7 

for Assignment 2.  

 

Assignment 1

Students will provide a complete Business Intelligence (BI) justification  document based on hypothetical scenario. Please see the documents in Lab 1  folder (Lab Assessment 1) in Blackboard Learn. This take-home assignment  will require understanding the materials in:

(i) Lecture 1 notes in Blackboard  Learn’s Lecture 1 folder,

(ii) Rick Sherman’s textbook chapters 1 to 3, and

(iii)  Lab 1 materials.  

The breakdown of marks for this assignment are indicated within the provided  BI Justification template document and in the cover sheet in Lab 1 folder. 

Students are required to attach this cover sheet at the front of this assignment,  and submit together as one Word document.  

Students will be given formative verbal feedback as they develop their skills  building up to this assignment.  

 

Assignment 2  

This assignment consists of an online lab/class test (6:15-9:05pm; duration of  2 hours and 50 minutes) in which students have to make use of the techniques  learned in previous classes (lecture and lab contents from Week 2 to Week 6) to answer a list of questions based on dataset(s) provided during the test. The  questions and dataset will be provided on the test day and students have to  download them from Blackboard Learn (Assessment). This test will involve: (i)  understanding a (new) real-life dataset, (ii) performing analyses of the dataset,  (iii) interpreting the analytical results, and (iv) projecting the data and analytical  results in the context of the business world. This is an open-book test to mimic  real-world scenario, and thus, students may refer to books and class notes, and  access the web for information during the test. The questions may require  critical or flexible thinking and not just based on memory i.e. rote learning.  

The choice of analytical software or platforms are up to the students, depending  on ease of use, familiarity and appropriateness. Answers should be provided  immediately after each question, in Word document format. Any figures or  tables based on any analysis can be copied or imported into this Word  document. Students are required to attach a given cover sheet at the front of  this assignment and submit together as one Word document by the end of the  lab session (class test). 

Since this is a formal assessment, if students are not able to be present during  the test day, please inform the module coordinator in advance. The students may need to show evidence to support the absence, and an EC1 form may be  required. 

 

1. The most straightforward ones are finding interesting and relevant open data  to analyse, and interpret your results, and relate to the business aspects (e.g. 

opportunities or impact). Students should search the literature (via published  papers or websites e.g. Google Scholar) to identify what have been done to  that selected data (or similar data) and what has yet to be done – research  opportunities (this would also help form the Introduction and Discussion  sections of the project report/paper – see below).  

2. Another option is to use data from the student’s workplace (e.g. company) to  help solve related business problems or improve business processes. This may  require non-disclosure agreement. Students should identify research  opportunities (see 1 above) and relate business aspects to their analyses. 

 

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