LDSCI62103A Advanced Topics in Data Analytics Course Descriptor

Course Overview

With the advances in data gathering, storage and processing techniques, new possibilities and challenges in data analytics emerge. This course will focus on advanced topics in data gathering and analysis in an organisational context: possibilities and limitations of modern computational data analytics approaches and techniques, quantitative and qualitative data gathering methods and how to select the appropriate method, how data driven insights can be facilitated and communicated, barriers that exist to effective data analysis between analysts and their stakeholders and how to avoid or resolve these, along with accessibility and inclusion issues. The learners will also be introduced to the relations between data analytics and sustainable development and green computing and will critically evaluate how data and analysis may exhibit biases and prejudice, how ethics and regulations affect data analytics work, for example, General Data Protection Regulation, Data Protection Act 2018.

This course enables the learners to gain knowledge and advance their skills in computational data analysis and communication via a work-based practical assignment and a written report.

Learning Outcomes

On successful completion of the course, students will be able to:

Knowledge and Understanding

Subject Specific Skills

Transferable and Employability Skills

Teaching and Learning

This course has a dedicated Virtual Learning Environment (VLE) page with a syllabus and range of additional resources (e.g. readings, question prompts, tasks, assignment briefs, discussion boards) to orientate and engage students  in their studies.

This is an e-learning course, taught throughout the year. This course can be offered as a standalone short course.

Teaching and learning strategies for this course will include:

• On-line learning
• On-line discussion groups
• On-line assessment

Students are required to attend and participate in all the formal and timetabled sessions for this course. Learners are also expected to manage their self-directed learning and independent study in support of the course.

The course learning and teaching hours will be structured as follows:

• Off-the-job learning and teaching (12 days x 7 hours) = 84 hours
• On-the-job learning (24 days x 7 hours) = 168 hours (e.g. 2 days per week for 12 weeks)
• Independent study (4 hours per week) = 48 hours

Workplace assignments (see below) will be completed as part of on-the-job learning.

Indicative total learning hours for this course: 300

Assessment

Both formative and summative assessment are used as part of this course, with purely formative opportunities typically embedded within interactive teaching sessions, office hours, and/or the VLE.

Summative Assessments

Feedback

Students will receive formative and summative feedback in a variety of ways, written (e.g. marked up on assignments, through email or the VLE) or oral (e.g. as part of interactive teaching sessions or in office hours).

Indicative Reading

Note: Comprehensive and current reading lists are produced annually in the Course Syllabus or other documentation provided to students; the indicative reading list provided below is for a general guide and part of the approval/modification process only.

• Mukhopadhyay, S. and Samanta, P. (2023). Advanced Data Analytics Using Python With Architectural Patterns, Text and Image Classification, and Optimization Techniques (2nd ed. 2023.). Apress.
• Farmer, D. and Horbury, J.(2022). Embedded Analytics: Integrating Analysis with the Business Workflow. O’Reilly Media.
• Dehghani, Z. Data Mesh (2022). O’Reilly Media.
• Geng, H (ed.) (2017). Internet of Things and Data Analytics Handbook, John Wiley & Sons, Incorporated, New York.
• Leading Global Diversity, Equity, and Inclusion. (2021). Berrett-Koehler Publishers.
• Quino, C. T. E. de, Robertson, R., & Robertson, R. (2018). Diversity and inclusion in the global workplace : aligning initiatives with strategic business goals (C. T. E. de Aquino & R. (Robert W. . Robertson, Eds.). Palgrave Macmillan. https://doi.org/10.1007/978-3-319-54993-4

Indicative Topics

Note: Comprehensive and current topics for courses are produced annually in the Course Syllabus or other documentation provided to students; the indicative topics provided below are used as a general guide and part of the approval/modification process only.

• Apply modern computational approaches to real-world data analysis
• Select and apply appropriate data analysis techniques and methods in contemporary business contexts
• Biases and prejudice in data analytics and how to overcome them

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