Reference no: EM134002432 , Length: Word Count:2000

Assessment - Data Analytics Report and Presentation

Big Data for Software Development

Bachelor of Information Technology

Learning Outcome 1: Evaluate the role and impact of big data concepts, tools, and techniques in various aspects of software development, including but not limited to security, personalization, and predictive analytics.

Learning Outcome 2: Apply and integrate analytical and visualization methods using big data tools such as Excel, Weka, IBM Cognos Analytics, and Hadoop to solve software development challenges like feature prioritization and performance optimization.

Learning Outcome 3: Critically assess and implement advanced data pre-processing and analytics strategies in a software development context, focusing on tasks like data cleansing, transformation, and featureselection. No AI shortcuts — Get genuine assignment help from experienced, real tutors.

Assessment Objective

The objective of this assessment is to assess student's ability to apply data analytics techniques to real-world scenarios, collect and analyze business requirements, identify patterns and trends, draw meaningful insights, and make data-driven recommendations.

Assessment: Data Analytics Report and Presentation

Instructions
For this assessment, you will work in groups of 3-4. Your lecturer will assist in forming these groups during Week 3.

This assessment is comprised of two parts: a report and a presentation. You are required to submit a PDF or MS Word file for the report, and a PPT file for the presentation by Week 7. In Week 8, each group is expected to present their work in class. Additionally, all supplementary files, including datasets and models, must also be submitted.

Throughout this assessment, you will be practicing the agile data science methodology. It is important to demonstrate effective team collaboration and communication using software development tools and techniques such as Jira for project management and GitHub for software development and version control.

Problem Definition and Requirements Elicitation
To commence this assessment, you need to find a problem whose solution requires data analytics techniques and can benefit a business. You can explore applications of data analytics in security and identify vulnerabilities of a software application. Alternatively, you can consider applications of data analytics in personalisation in e-commerce - customisation strategies.

The problem must be defined in detail and motivated very well and you need to collect and analyse the requirements.

Dataset
You need to find suitable datasets for your chosen problem. Several datasets and repositories have been introduced in the first lab. The datasets are briefly described. Each group needs to choose one of these datasets for the assessment. You can also find a suitable dataset outside the list. There are many publicly available datasets which can be also used for this assessment.

Requirement Analysis
You need to analyse the business requirements and design at least two business intelligence (BI) questions whose answers can help managers to make informed decisions. Throughout this assessment, you will use suitable tools and techniques to appropriately answer these questions.

One question must be about descriptive analysis and the other must be about predictive analysis. For example, if you work on a hypothetical dataset about CIHE enrolment, a descriptive and a predictive question may look like these:

Predictive: How many students will enrol in each program in 2026? Or what would be the success rate of ICT313 in S1-2026?
Descriptive: Which programs are more favourable for students and why? Or what factors impact
the students' success in ICT313?

Import and Refine data
For this assessment, you are allowed to use any of the following tools: Weka, IBM Cognos Analytics, and Excel. Tools outside this list (e.g., Python, R, or Tableau) are not permitted.
After selecting your dataset and tools, import the dataset and prepare it for analysis. Ensure the data is ready for your analysis tasks (i.e., data preparation). Check for missing values or outliers and address them before beginning the analysis. Depending on your approach, you may need to create new columns based on existing data, transform columns, or discretize attribute values.

Predictive analysis
To answer your prediction question, you can use predictive models in Weka (e.g., classification, regression, prediction rules, etc). Alternatively, you can use decision trees, prediction rules, deriving factors, etc in IBM Cognos. If you have decided to choose a different tool, explore its prediction capabilities, and ensure that you can use it to appropriately answer your predictive question.
To answer the predictive question, you need to have a strategy to show your deep understanding of data analytics principles and techniques and the results must be satisfactory and convincing for decision-makers. All results must be backed by good analysis and appropriate models such that you can convince the decision-makers that your prediction is accurate and valid.

Descriptive analysis
To answer your descriptive question, you can use data visualization and dashboards in IBM Cognos. Alternatively, you can use Excel. They have many visualization elements and can help you to create powerful and interactive dashboards.
To appropriately answer your descriptive question, you need to have at least 6 visualization elements on a dashboard with filtering capabilities to help decision makers gain an insight into the data. For this section, you can also use data visualization in Weka (e.g., scatter plots, histograms, statistics, etc). However, please be aware that Weka's visualization capability is limited and does not support dashboards.
Visualization elements and designing the dashboard must follow a suitable strategy to appropriately answer the descriptive question. All results must be backed by good analysis and appropriate visualization elements such that you can convince the decision-makers that your analysis sounds valid.

Report

You need to submit an MS Word or a PDF file which includes the following items:
Attach the Team Contribution Declaration in the front page of your report. Use the template provided in page 5.
A brief description of the dataset and the reason for selecting this dataset. You also need to explain the applications that this dataset can be used for.

The two questions you have designed (i.e., predictive and descriptive) and justification of your decision (i.e., why is it important for decision-makers to know the answer to these questions?) - The choice of tool and justification of its suitability for your data analysis purposes.
The steps you have taken for data preparation.
The answer to each question with an explanation of the process that guided you to this answer. You need to insert screenshots as evidence of completion of each step as well as data visualization elements. Annotate all screenshots with brief descriptions.

Presentation

In Week 8, you will present your work in the class (dataset, questions, and the process of finding answers). Each group will have 10 minutes (maximum) to present their work. The time must be equally divided among team members. The contribution of each team member must be highlighted. The PPT file must be submitted with other files end of week 7.