Module Specifications.
Current Academic Year 2024 - 2025
All Module information is indicative, and this portal is an interim interface pending the full upgrade of Coursebuilder and subsequent integration to the new DCU Student Information System (DCU Key).
As such, this is a point in time view of data which will be refreshed periodically. Some fields/data may not yet be available pending the completion of the full Coursebuilder upgrade and integration project. We will post status updates as they become available. Thank you for your patience and understanding.
Date posted: September 2024
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Coursework Only This module is a pass/fail module where students will have the option to attempt a piece of assessment multiple times until they pass the assessment. The students need to pass all the assessments to pass the overall module. |
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Description This module equips students with the knowledge and skillset of Data Literacy and Analytics required in the 21st century. It enables students to gain the capability to collect, process, critique, analyse, visualise, and interpret data in an unbiased, responsible, actionable and ethical manner. It further prepares students with the ability to use tools and techniques to efficiently understand, interpret, and use data in their discipline. The delivery mode will be online and asynchronous. There will be no synchronous face-to-face or online lecture/lab taking place throughout the semester. All resources will be available to students on the module Loop page and students will complete the topics at their convenient time in a self-paced manner. For attaining the best out of this module, students are highly advised to engage with the content every week over the semester. There are seven online assessments corresponding to the seven topics. Students are encouraged to take the assessment immediately after they complete the content covered in that topic. Students are expected to pass all the seven topics to pass this module. The passing mark for each topic will be posted on the module loop page. Students are also allowed to try as many times until they pass the assessment during the term. However, failing to achieve the passing mark for three consecutive attempts may require them to revisit the content covered before trying again. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Describe the fundamental concepts of Data Literacy and Analytics, the key steps in the analytics process, and the applications and implications of data analytics in their specialism. 2. Demonstrate knowledge of big data analytics and its steps, key statistical concepts underlying data analytics techniques, including descriptive statistics. 3. Discuss the ethical and legal requirements involved when gathering, storing, analysing and reporting data and the societal impact of data analytics. 4. Be able to source and import data and apply basic operations for cleaning, and processing this data in preparation for data analysis using basic functionalities of analytics tools such as Spreadsheet, Python, or R. 5. Identify and use basic data analysis and visualisation tools to describe and interpret data. 6. Identify the relevant insights extracted from a dataset and effectively and appropriately communicate (interpreter, critique, and present) them. 7. Differentiate between the different data types in analytics and have the ability to explain basic database design concepts, including conceptual, logical, and physical data models. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml |
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Indicative Content and Learning Activities
Introduction to Data Literacy and AnalyticsData Literacy, Data, Data Types, Types of Data, Metadata, Data Sources, and Data Collection.Introduction to Big Data AnalyticsBig Data, Data Analytics Pipeline, Application Areas, Tools and Technologies, and Trends.Introduction to Data Protection and EthicsIntroduction to Data Protection, Organisational and Individual Responsibilities, Ethical Data Collection, Ethical Data Analysis and Storage, and FAIR Principles.Introduction to SpreadsheetIntroduction to Spreadsheet, Creating New Books and Sheets, Entering Data, Formatting, Sorting, Filtering, Aggregation, and Formulas.Intermediate SpreadsheetImporting/Exporting data, Data Cleaning, Advanced Formulas and References, Useful Built-in Functions, and Basic Regression and Correlation.Data Visualisation and Communication Using SpreadsheetData Visualisation Fundamentals, Selecting Appropriate Graph Types, Data Visualisation in Spreadsheets, Data Critique, and Data Misrepresentation, Data Reporting and Presentation, Arguing with Data (Support Narrative).Introduction to Database ModellingCapturing and Modelling Data, Conceptual Modelling, Logical Modelling, Physical Modelling, Data Storage Technologies, Data Retrieval Languages. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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Other Resources 64105, 0, Additonal Resources for each topics will be provided during the module delivery., | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||