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As such, this is a point in time view of data which will be refreshed periodically.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 If no sufficient pass mark has been achieved the student can repeat the online components/quizzes and work on the digital portfolio in the months leading up to the repeat exam time. |
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Description Combined Data literacy, Databases (SQL via Excels and python), and Data Visualisation (python) module Students learn how the basic structure and concepts of a database and get an introduction on the structured query language (SQL) In a second step student learn how to integrate database queries into python programs, but also dynamic dynamic spreadsheets (Excel) The module gives an overview of different plots, their use scenarios, file formats and how to create them within python (matplotlib, seaborn) including physiological and physical limits of human perception and why this is important for information visualisation. The module concludes with a group project on interactive graphs/dashboards The module also embeds topics from the DCU developed Data Literacy content (Introduction to Data Analytics, Big Data, and Data Modelling) | |||||||||||||||||||||||||||||||||||||||||||||
Learning Outcomes 1. Understand the basic concept of structured databases 2. Is able to formulate queries using SQL statements 3. Is able to integrate database queries into spreadsheets and python programs 4. Understands various types of data presentation (scatter, line, histograms, bubble plots, error bars ...) and graphic formats (bitmap, vector) 5. Is able to create various plots in spreadsheets and python 6. is able to understand an industrial/societal scenario and able to select and implement a visualisation approach | |||||||||||||||||||||||||||||||||||||||||||||
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
Database EnvironmentOverview: Architecture, Components & Functions, Data Models.Query languageIntroduction into SQL, how to formulate queriesDatabase connectorsHow to implement connections between applications (python, Excel) and databases (Windows: odbc, python: sql conectors, mysql, sqlite)Visualisation 101Vector vs. bitmap formats, when to use which, examples for various plotting styles beyond the basic scatter/line plots. Visualisation of multidimensional datasets: colour plots, 3D plots, bubble plots, butterfly plots, error bars, confidence intervals and interactive visualisationsProject workTeam challenge in visualisation of data from a database within a societal or industrial contextIntroduction to Data AnalyticsOvervie of the use of Data analytics in society, types of data, data types, and data analysis pipelinesBig DataIntroduction of Big Data and tools for Big Data analysis, Outlook on trends in Big dataData ModellingIntroduction in Data Modelling (in the computer science sense), including casing of types, dealing with missing entries, and designing database structures | |||||||||||||||||||||||||||||||||||||||||||||
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Other Resources None | |||||||||||||||||||||||||||||||||||||||||||||