Academic Handbook Course Descriptors and Programme Specifications
LDSCI62110A Data Analyst End-Point Assessment Course Descriptor
| Course Code | LDSCI62110A | Discipline | Computing and Information Systems |
| UK Credit | 30 | US Credit | N/A |
| FHEQ Level | 6 | Date Approved | October 2023 |
| Core Attributes | |||
| Pre-Requisites | |||
| Co-Requisites | |||
Course Overview
Digital and Technology Solutions Professionals are found in organisations where digital technologies can be used to solve problems that exist across a range of functions. Whether looking for ways to reduce waste, increase productivity, ensure resilient and responsive customer service, or create a secure transactional environment, organisations turn to digital and technological solutions to achieve these aims. Wherever these activities take place Digital and Technology Solutions Professionals are influencing outcomes and making things happen.
The primary role of a data analyst is to collect, organise and study data to provide new business insight to a range of stakeholders. They are responsible for leading the provision of up-to-date, accurate and relevant data analysis for the organisation. They are typically involved with managing, cleansing, abstracting and aggregating data across the network infrastructure. They look for opportunities to build data driven insights into decision making. They have a current understanding of data structures, software development procedures and the range of analytical tools used to undertake a wide range of standard and custom analytical studies, providing data solutions for a range of business issues. They are comfortable supporting teams and colleagues with analytics and report the results of data analysis activities making recommendations to improve business performance.
This course enables learners to demonstrate the occupational competencies of the role via a work-based project and a portfolio. The implementation of the course assessments will be in line with the Digital and Technology Solutions Professional End-Point Assessment Plan.
Learning Outcomes
On successful completion of the course, students will be able to:
Knowledge and Understanding
| K1c | Plan and deliver a substantial individual project by applying a digital technology solutions and demonstrate a competitive advantage by adapting and exploiting it |
| K1c | Assess sustainable development approaches within digital technologies as they relate to their role including diversity and inclusion. |
| K1c | Demonstrate how data analytics can be applied to improve an organisation’s processes, operations and outputs |
| K1c | Demonstrate how data and analysis may exhibit biases and prejudice and how ethics and compliance affect data analytics work, and the impact of international regulations. |
| K2c | Evaluate the principles of strategic decision making concerning the acquisition or development of digital and technology solutions |
| K2c | Produce a project plan which estimates risks and opportunities and determines mitigation strategies. |
| K2c | Critically evaluate appropriate techniques and approaches that are used in creating a business case |
| K2c | Apply techniques to estimate cost and time resource constraints |
| K2c | Critically analyse the business problem behind the project proposal to identify the role of digital and technology solutions |
| K2c | Carry out the identified solution proposal utilising a range of digital tools and standard approaches |
| K3c | Critically analyse data sets, using a range of industry standard tools and data analysis methods. |
| K3c | Apply approaches to data processing and storage, database systems, data warehousing and online analytical processing, data-driven decision making and the good use of evidence and analytics in making choices and decisions |
| K3c | Critically evaluate the nature and scope of common vulnerabilities in digital and technology solutions |
| K4c | Critically evaluate how data analytics operates within the context of data governance, data security, and communications in respect of the project |
| K4c | Assess the barriers that exist to effective data analysis between analysts and their stakeholders and how to avoid or resolve these |
| K4c | Critically analyse data formats, structures, architectures and data delivery methods including unstructured data |
| K4c | Critically reflect on core technical concepts for digital and technology solutions and their applicability to organisation’s standards; data gathering, data management, data analysis and computer networking concepts. |
| K4c | Demonstrate how teams work effectively to produce a digital and technology solution applying relevant organisational theories using up to date awareness of trends and innovations. |
| K4c | Critically evaluate the concepts and principles of leadership and management as they relate to their role and how they apply them |
| K4c | Critically analyse relevant evidence to produce a proposal for a digital and technology based project in line with legal, ethical and regulatory requirements whilst ensuring the protection of personal data, safety and security |
Subject Specific Skills
| S1c | Apply relevant legal, ethical, social and professional standards to digital and technology solutions considering both technical and non-technical audiences and in line with organisational guidelines. |
| S1c | Visualise data to tell compelling and actionable narratives by using the best medium for each audience. |
| S1c | Demonstrate how they have applied a range of techniques for analysing quantitative data such as data mining, time series forecasting, algorithms, statistics and modelling techniques to identify and predict trends and patterns in data. |
| S1c | Apply exploratory or confirmatory approaches to analysing data. and how they have validated and tested stability of the results. |
| S1c | Analyse, in detail, large data sets, using appropriate industry standard tools and data analysis methods. |
| S1c | Encounter barriers to effective analysis both by analysts and their stakeholders within data analysis projects |
| S2c | Extract data from a range of sources. |
| S2c | Define data requirements and perform data collection, data processing and data cleansing. |
| S2c | Apply different types of data analysis, as appropriate, to drive improvements for specific business problems. |
| S3c | Demonstrate the use of core technical concepts for digital and technology solutions, including: Initiate, design, code, test and debug a software component for a digital and technology solution; security and resilience techniques and apply the principles of data analysis |
Transferable and Employability Skills
| T1c | Present an overview of the project to appropriate stakeholders using appropriate language and style |
| T1c | Display an advanced level of technical proficiency in written English and competence in applying scholarly terminology, so as to be able to apply skills in critical evaluation, analysis and judgement effectively in a diverse range of contexts. |
| T3c | Make meaningful conclusions on the basis of a long period of independent study. |
| T4c | Manage the project delivery to achieve digital and technology solutions |
| T4c | Justify the methods of research and evaluation which determined the selection of digital and technology solutions identified for the project |
Teaching and Learning
The contact hours on this course are formed predominantly of supervisory meetings, typically 4 x 1 hour.
Students 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
| AE: | Assessment Activity | Weighting (%) | Duration | Length |
| 1 | Project report with presentation, questions and answers | 50% | 6000 word (report) & 60 minutes (presentation and Q&A) | |
| 2 | Professional discussion underpinned by a portfolio | 50% | 60 minutes |
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.
- Walliman, N., (2013), Your Undergraduate Dissertation: The Essential Guide for Success, London: Sage.
- Rugg, G., & Petre, M. (2006). A gentle guide to research methods. McGraw-Hill Education (UK).
- Berndtsson, M., Hansson, J., Olsson, B., & Lundell, B. (2007). Thesis projects: a guide for students in computer science and information systems. Springer Science & Business Media.
- Stephan Felix, M., & Smith, I. (2019). A practical guide to dissertation and thesis writing. Cambridge Scholars Publishing.
- Maheshwari, S., Gautam, P. and Jaggi, C.K., 2021. Role of Big Data Analytics in supply chain management: current trends and future perspectives. International Journal of Production Research, 59(6), pp.1875-1900.
- Mikalef, P., Pappas, I.O., Krogstie, J. and Giannakos, M., 2018. Big data analytics capabilities: a systematic literature review and research agenda. Information systems and e-business management, 16, pp.547-578.
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 is used as a general guide and part of the approval/modification process only.
- How to solve a technological problem based on an organisation’s problem
- Managing technology projects to a successful outcome
- Using real-world data and scenarios
Version History
| Title: LDSCI62110A Data Analyst End-Point Assessment Course Descriptor
Approved by: Academic Board Location: academic-handbook/digital-and-technology-solutions |
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| Version number | Date approved | Date published | Owner | Proposed next review date | Modification (As per AQF4) & category number |
| 1.0 | October 2023 | October 2023 | Dr Alexandros Koliousis | October 2028 | |