B8IT154 Platforms for Data Analytics Continuous Assessment One | Dublin Business School

B8IT154 CA1

Module Code: B8IT154
Module Title: Platforms for Data Analytics
Assessment Title & Description: Practical assignments on BI tools, programming languages, and data management solutions
Percentage (%) Weighting: 50%
Lecturer Nitya Govindaraju
Individual/Group:   Individual  
Task: Design and interpretation of dashboards on Big Data using tools demonstrated in the course such as Tableau or Power BI
MIMLOs being assessed: 1,2,3
Issue Date 23 October 2025
Submission Date   5 December 2025  

Assignment Goal:

The goal of this assignment is to demonstrate your application of course concepts and achieve the following course learning outcomes:

  • Identify common data issues and rectify simple problems, such as data type errors and missing data.
  • Apply basic statistical concepts to derive insights from everyday data tasks.
  • Use Tableau or Power BI to perform data clean-up, visualizations, and dashboard creation.
  • Effectively communicate results using storytelling or other techniques.
  • Demonstrate a clear understanding of creating dashboards and reports using Business Intelligence tools.
  • Demonstrate a clear understanding of data modelling and data importing.

Assignment Overview:

For CA1, you are required to create TWO dashboards based on real-world cases using either Tableau OR Power BI. The assignment should encompass the processes of Data Understanding, Data Importing, Data Cleaning, Data Processing, Statistical Analysis, Visualization, and Storytelling.

Assignment Specifics:

  1. Tools: Use either Tableau or Power BI for this assignment.
  2. Data Selection: S elect a dataset from a real-world context, which could pertain to various domains such as Entertainment, Sales, Business, Marketing, Finance, or others.
  3. Dashboard Design: Create TWO dashboards that effectively communicate insights derived from the selected dataset. Consider layout, design, visualizations, and interactivity to enhance user experience.
  • Each dashboard should consist of 4-5 worksheets/graphs
  1. Techniques: Apply techniques such as filtering, DAX operations, creating new fields, and measures to your data visualizations.
  2. Report: Prepare a report with a word count between 600-800 words (excluding references) that explains your approach, the dataset used, the insights derived, and how your dashboards address the learning outcomes.

Report Document Template:

  1. Cover Page: Assignment title, course details, student name, and student ID.
  2. Table of Contents: Clearly list sections with page numbers.
  3. Introduction: Briefly introduce the assignment and your approach.
  4. Dashboard Descriptions: Explain the design and insights of both dashboards.
  5. Report: Present insights derived, relevance to learning outcomes, and how the tools were effectively used.
  6. Conclusion: Summarize key points.
  7. References: Cite data sources, tools, and references.

Documents For Submission – 1 PDF and 1 ZIP File(Dashboard + Dataset)

  1. Dataset
  2. Dashboard file : Tableau OR Power BI files. Please compress the dataset and dashboard files and Zip it.
    a. Follow the naming convention:
    b. Tableau Workbook file should be named as – twb.
    c. Power BI workbook file – pbix
    d. Zipped Folder should be named as – zip.
  1. Report: Create a pdf document : CA1_Report_FirstName_Surname.pdf

Grading Rubrics:

Grade Criteria Fail Pass Good Very Good Excellent
Criteria 1: Data Understanding and Preparation (15%) The student does not demonstrate a  basic understanding of data issues and lacks any attempt to rectify them

 

The student identifies some data issues but struggles to address them effectively. The student identifies

common

data issues and adequately rectifies simple problems in the dataset.

The student thoroughly identifies and rectifies common data issues with precision, showing a sound understand ing of data types and missing data. The student expertly identifies and rectifies data issues with a high degree of accuracy, displaying an advanced comprehension of data quality.
Criteria 2: Data Importing,  Cleaning, and  Processing (15%)

                 

                 

The student fails to demonstrate basic data

importing and cleaning skills, resulting in a poorly prepared dataset.

The student imports and cleans data but does so inadequately

, leaving some issues unresolved.

The student imports data effectively and cleans it sufficiently, resulting in an acceptable dataset. The student imports and cleans data adeptly, addressing most data issues and preparing the dataset effectively. The student imports and cleans data expertly, displaying a high level of skill in data processing and ensuring the dataset is wellprepared.
Criteria 3: Dashboard 1 Design and Insights (20%) The student fails to create an informative dashboard with poor design, lacking The student creates a dashboard, but it lacks visual appeal and contains The student designs a good dashboard, which is visually The student designs a very good dashboard with a The student

excels in dashboard design, creating an

                 

                 

meaningful insights. limited insights. acceptable

and provides

some

valuable insights.

pleasing visual layout,

offering

numerous meaningful insights.

excellent, visually stunning

dashboard with a rich depth of insights.

Criteria 4:  Dashboard 2 Design and Insights (20%)

                 

               

The student fails to create an informative dashboard with poor design, lacking meaningful insights. The student creates a dashboard, but it lacks visual appeal and contains limited insights. The student designs a good dashboard, which is visually acceptable

and provides

some

valuable insights.

The student designs a very good dashboard with a pleasing visual layout,

offering

numerous meaningful insights.

The student excels in dashboard design, creating an excellent, visually stunning

dashboard with a rich depth of insights.

Criteria 5:  Report Clarity, Alignment with Learning Outcomes, Creativity, and Effective Use of Tools (30%)

 

The student’s report lacks clarity, alignment with learning outcomes, and creativity, with minimal tool usage. The report is somewhat clear, aligns partially with learning outcomes, displays moderate creativity, and some tool utilization. The report is mostly clear, aligns well with learning outcomes, demonstrate s good creativity, and effective tool usage. The report is clear, fully aligns with learning outcomes, showcases very good creativity, and highly effective tool utilization. An exceptional ly clear report with perfect alignment to learning outcomes, exceptional creativity, and innovative tool usage

Please follow this link to the Harvard Style Referencing Guide – all referencing is required in this forma:  DBS

Note: Ensure that your submission adheres to the provided format, addresses the assignment specifics, and meets the grading rubrics. Good luck with your assignment!

Generative Artificial Intelligence Assessment Scale

Can generative AI be utilised in this assignment?

No AI content is allowed in the final submission

1 2 3 4 5
NO AI AI-ASSISTED IDEA GENERATION AND STRUCTURING AI-ASSISTED EDITING AI TASK  COMPLETION, HUMAN EVALUATION FULL AI
The assessment is completed entirely without AI assistance. This level ensures that students rely solely on their knowledge,

understanding,

and skills.

AI must not be used at any point during the assessment.

AI can be used in the assessment for brainstorming, creating structures, and generating ideas for improving work.

No AI content is allowed in the final submission.

AI can be used to make improvements to the clarity or quality of student created work to improve the final output, but no new content can be created using AI.

AI can be used, but your original work

with no AI content must be provided in an appendix.

AI is used to complete certain elements of the task, with students providing discussion or commentary on the AI-generated content. This level requires critical engagement with AI generated content and evaluating its output.

You will use AI to complete specified tasks in your

assessment. Any AI

created content must be cited.

AI should be used as a ‘co-pilot’ in order to meet the requirements of the assessment, allowing for a collaborative approach with AI and enhancing creativity.

You may use AI throughout your assessment to

support your own work and do not have to specify

which content is

AI generated

         

General Requirements for Students:

  1. All assignments must be submitted no later than the stated deadline (date and time).
  2. Assignments submitted after the latest deadline specified (including any approved extension deadline) are considered late and penalised according to the Quality Assurance Handbook (QAH) Part B Section 5.2.2.6 as follows:
    a. A penalty of 2 marks will be applied per day or part thereof (including weekends and public holidays) for an ongoing failure to submit beyond the submission deadline.
    b. An examiner has the right to refuse to mark the assignment if the submission instructions have not been observed.
    c. Where a late assessment is submitted within 14 days of the deadline, and is of a passing standard, the late penalty is capped (such that the minimum grade that can be awarded is 40% for the late submission).
    d. Where a late assessment is submitted more than 14 days after the deadline, it will receive 0%. The lecturer may, at their discretion, review the submission for feedback.
    e. Where the assessment is undertaken in a group, the piece of work should be submitted in its complete entirety, and any penalty for late submission incurred applies to all group members.
  3. Extensions to assignment submission deadlines will not be granted, other than in exceptional circumstances.  To apply for an extension please go to DBS login and open a ticket.
  4. All relevant provisions of the Assessment Regulations must be complied with (see QAH B.5).
    a. Students are required to refer to the assessment regulations in their Programme Handbook, and on the Student Website.
    b. Dublin Business School penalises students who engage in academic impropriety (i.e. plagiarism, collusion and/or copying, ghost writing/ essay mills, improper use of Generative Artificial Intelligence software).
    1. Refer to the College’s Generative AI Guidelines HERE for further information.
    c. Guides on referencing are available on the Library website: 
    d. Text-matching analysis software is integrated in Moodle to generate a report regarding the degree of text-matching in a submission.
  5. Students are required to retain a copy of each assignment submitted, until the issuing of a transcript indicating the mark awarded and the closure of the Appeal period (2 weeks following the release of final results).
    a. Results can only be appealed following the release of final results, and the Appeal form must be submitted to the Exams Office within the Appeal period.
    b. An appeal must be based on valid grounds (see the Appeals Policy QAH B.3.5), dissatisfaction with a grade is not sufficient grounds for an appeal.
    c. Assignments must be appropriately packaged and presented.
    d. All assignments should be submitted to your subject/course page on Moodle by the deadline date.
    e. Where a submission involves digital media (i.e. formats other than Word, PowerPoint or PDF), it is the submitting students’ responsibility to ensure the media is appropriately labelled, fully working and they must retain a copy.
    f. Components of an assessment which are not included in the final submission cannot normally be subsequently accepted for grading. It is the student’s responsibility to ensure their file is uploaded correctly.
    g. Include an electronic cover sheet with the following details to the front of the assignment (see below)
  6. Assignments that breach the word count requirements will be penalised. There is a 10% discretion, either way, applicable in terms of word count.
  7. When you submit your assignment, you will be asked to click on a button which will declare the following:

By ticking this box, I am confirming that this assignment/exam is all my own work. Any sources used have been referenced.
I have read the College rules regarding plagiarism in the QAH Part B Section 3 and understand that penalties will be applied accordingly if work is found not to be my own. All work uploaded is submitted via Ouriginal, whereby a text-matching report will show any similarities with other texts.

The post B8IT154 Platforms for Data Analytics Continuous Assessment One | Dublin Business School appeared first on Ireland Assignment Helper.