Reference no: EM133738744 , Length: word count:2000

Fundamentals of Data Analytics

Learning Outcome 1: Exhibit comprehension of the fundamental principles of data analysis, including theoretical frameworks and methodologies, applicable to business and social contexts.

Learning Outcome 2: Evaluate high level of expertise in assessing data analytics methods critically to solve real-world problems.

CLO1: Demonstrated capacity to understand, apply, and evaluate the fundamental principles and methods of information technology.
CLO2: Demonstrated theoretical and advanced knowledge and expertise and their application in the evolving field of information technology.
CLO3: Demonstrated ability to evaluate, research, and synthesise multimodal information in order to apply critical thinking and problem-solving skills to generate innovative information technology,
system, and business solutions.
CLO4: Demonstrated advanced level of communication skills that are effective and appropriate to the context and profession.
CLO5: Demonstrated ability to apply leadership theories and strategies across a range of sociotechnical settings in order to lead information technology, client focused, and business-related
projects to successful completion.
CLO6: Demonstrated ability to critically reflect and apply ethical reasoning to a range of ethical, social, and cultural issues within the information technology profession and across the broader
community.

Exercise 1: To evaluate students based on the topics covered each week, requiring them to submit their completed work on a weekly basis.

Assessment Details - . This means that students will be expected to submit their work every week for ten weeks. The assessment will evaluate the quality and completeness of the work submitted by the students, as well as their progress throughout the lab activities. This assessment is designed to ensure that students are actively engaged in the lab activities and are making steady progress towards achieving the learning objectives of the lab.

Aim - To ensure that students study regularly and are familiar with the material discussed and presented each week in lectures and workshop activities.

Aim - Do research on machine learning methods for predicting customer churn, demonstrating a thorough understanding of data analytics techniques and evaluating their effectiveness through a comprehensive report and presentation.

Assessment Details - Students are required to write a report on the use of machine learning methods for predicting customer churn

Assessment Details - In this assignment, students will conduct comprehensive research on the application of data analytics techniques to a chosen dataset. Their task is to explore various data analysis methods, their application in different fields, and evaluate their effectiveness using academic literature. The report should include a literature review, dataset description, methodology, technical findings, and performance evaluation.