Module: Databases and Data Visualization

Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title	Databases and Data Visualization
Module Code	PS224
School	School of Physical Sciences
Module Co-ordinator	Semester 1: Karsten Fleischer Semester 2: Karsten Fleischer Autumn: Karsten Fleischer
Module Teachers	Karsten Fleischer Stephen Power
NFQ level	8	Credit Rating	5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

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.

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

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Online activity	20	Online lecture material in form of pre-recorded lectures, loop material
Lecture	6	Accompanying "lecture type" (inverse classroom) discussions
Assignment Completion	24	Optional access to computational laboratories within DCU to carry out online assignments and consult with programming tutors when needed.
Seminars	3	Guest speakers from industry to illustrate uses of databases and data visualisation
Independent Study	46	No Description
Assignment Completion	26	Optional access to computational laboratories within DCU to carry out the project based challenge and consult with programming tutors when needed
Total Workload: 125

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

Indicative Content and Learning Activities

Database Environment
Overview: Architecture, Components & Functions, Data Models.

Query language
Introduction into SQL, how to formulate queries

Database connectors
How to implement connections between applications (python, Excel) and databases (Windows: odbc, python: sql conectors, mysql, sqlite)

Visualisation 101
Vector 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 visualisations

Project work
Team challenge in visualisation of data from a database within a societal or industrial context

Introduction to Data Analytics
Overvie of the use of Data analytics in society, types of data, data types, and data analysis pipelines

Big Data
Introduction of Big Data and tools for Big Data analysis, Outlook on trends in Big data

Data Modelling
Introduction in Data Modelling (in the computer science sense), including casing of types, dealing with missing entries, and designing database structures

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Completion of online activity	Students need to complete the DCU developed Data Literacy Topics: * Introduction to Data Analysis * Big Data * Data Modelling and participate in MCQ Loop quizzes and Selected Tasks to be submitted at the end of weekly computer based labs.	50%	Every Week
Professional Portfolio	Students built an digital portfolio over the course of the Module showcasing their skills in data visualisation. Over the course there will be several benchmarks on completion: * Demonstrate ability to create/edit the portfolio * Describe the data set to document in the portfolio * Demonstrate capability of visualising various aspects of the dataset * Final portfolio showcasing the coursework of the student	20%	As required
Group project	In Week 9-12 students will work on a challenge based project to create a interactive dashboard to visualise and explore dynamic datasets. Assessment of the group project is based on participation, quality of the code, and quality of the visualisation	30%	Sem 1 End

Reassessment Requirement Type
Resit arrangements are explained by the following categories; 1 = A resit is available for all components of the module 2 = No resit is available for 100% continuous assessment module 3 = No resit is available for the continuous assessment component
This module is category 1

Indicative Reading List

Other Resources

None

Programme or List of Programmes

PAN	BSc Physics with Data Analytics

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