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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2017/2018

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DRPS : Course Catalogue : Edinburgh College of Art : Design

Postgraduate Course: Data Science for Design (DESI11100)

Course Outline
SchoolEdinburgh College of Art CollegeCollege of Humanities and Social Science
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis course provides an introduction to programming, used in support of the development of data science techniques, to give a practical facility in manipulating, analysing, visualising and contextualising data.
Course description Through a series of lectures, practical sessions and a group project, you will be introduced to the importance of data, the details of how to work with it in a robust, communicative and defensible manner, and the computational grounding that underpins this work.

The ability to program fluently brings a qualitatively different view of the world, one which designers are increasingly required to be familiar with. Similarly, the ability understand the structure and meanings of data, and communicate this while maintaining awareness of the social context from which the data came is a key part of working in this field.

Through a series of lectures and workshops, you will be supported in developing a computational mindset, learning the tools of software carpentry, and developing a competence in writing and managing software. Building on this, you will develop an understanding of data, from the microformats used in individual fields to tools for engaging with large datasets. This includes: simple descriptive statistics; exploratory visual analysis; finding, combining and relating datasets (data wrangling); and an understanding of Open Data; and how to draw inferences from data. Data visualisation will be used for both exploration and presentation, drawing on techniques from data journalism. Alongside this, you will build up an understanding of the ways in which data relates to the world: the social and political structures of its collection and use, personal data, aesthetics and communication of findings.

Bringing this together around a particular problem will help you to understand how to collaboratively create software around data, and how to work with a problem holder to collect, analyse and present data sets of social relevance.

This course will:

1. Give a solid grounding in programming with Python, version control with Git and github and other key software practices.
2. Develop a comprehensive understanding of data formats, their wrangling and management, including CSV and relational databases (SQL)
3. Develop skills in the analysis and visualisation of a range of data using descriptive statistics and exploratory data analysis.
4. Introduce you to the socio-political ramifications of data collection and use.
5. Introduce rich collaborative practices around data collection, analysis and presentation.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Additional Costs Students must have a laptop.
Information for Visiting Students
Pre-requisitesNone
Course Delivery Information
Academic year 2017/18, Available to all students (SV1) Quota:  40
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 11, Seminar/Tutorial Hours 5, Supervised Practical/Workshop/Studio Hours 16.5, Formative Assessment Hours 2, Summative Assessment Hours 1, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 160 )
Additional Information (Learning and Teaching) Tutorials and Supervised Practical hours will be taught in groups.
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) You are expected to develop programming and analytic skills around data. These will be assessed through two individual reports (1000 words each, 30% each), a group report (2000 words, 30%) and an ongoing professional collaboration component (10%) assessed through contributions to code and data repositories, and online group discussion.«br /»
«br /»
Individual Report 1 addresses LO1 and 2, and involves scripted analysis of provided datasets (submission in week 5)«br /»
Individual Report 2 addresses LO1 and 2, and involves an open ended analysis of student-selected data (submission in week 8).«br /»
The Group Report addresses LO3 and involves analysis and reporting on a particular problem holders social situation in a small team.«br /»
LO4 is assessed by the use of collaborative tools (communication, software and data repositories) over the entire course, and will be assessed by code commits and contribution to online group discussion.
Feedback Formative feedback will be provided verbally during weekly tutorials.
Written formative feedback will be provided on initial submission of 1st and 2nd outputs.
Summative feedback will be provided following the submission of all outputs (individual reports and group work) at the end of the course.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Program: Identify and deploy strategies for writing, understanding and managing computer programs using Python and version control
  2. Data: The ability to wrangle, analyse, learn from and visualise a range of data, in a way that demonstrates its relevance to particular contexts of enquiry
  3. Communicate: Communicate around socially relevant issues, supported by the use of multiple data sources and appropriate analysis
  4. Professionalism: Working in collaborative, interdisciplinary teams to a high professional standard.
Reading List
Creative Code, John Maeda
R for Data Science, Garret Grolemund and Hadley Wickham
Python for Software Design: How to Think Like a Computer Scientist, Allen B. Downey, Jeff Elkner and Chris Meyers, Green Tea Press
Doing Data Science: Straight Talk from the Frontline, Cathy O'Neil and Rachel Schutt
The Visual Display of Quantitative Information, E. Tufte
Effective Computation In Physics - Field Guide to Research with Python, Scopatz and Huff
Data Flow 2: Visualizing Information in Graphic Design, Klanten, Ehmann, Bourquin and Tissot
Additional Information
Graduate Attributes and Skills data wrangling
computer programming
expertise in tabular data and relational databases (SQL)
exploratory data analysis
data journalism
Keywordsdata science,data analysis,computer programming,visualisation,software carpentry
Contacts
Course organiserDr David Murray-Rust
Tel:
Email: D.Murray-Rust@ed.ac.uk
Course secretaryMr Mathieu Donner
Tel: (0131 6)51 5740
Email: Mathieu.Donner@ed.ac.uk
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