Postgraduate Course: Practical Introduction to Data Science (INFD11010)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This online course will provide a practical introduction to data science. It will have two broad themes, namely Data Management and Data Analytics.
Data Science is an emerging field, which is becoming very important both in research, business and industry. The amount of data that is being generated and stored is greater than it has ever been, and this brings both challenges in terms of how you work with the data and - importantly - rewards in terms of new insight gained from analysing the data.
The course is practical in the sense that you will have the chance to use R and Python to explore the techniques and ideas described in the course videos.
The course will be delivered entirely online, and videos, notes and exercises will be released as the course progresses.
This course may not be compatible with some on-campus programmes of study. Students interested in the area may also wish to consider: INFR11176 - Fundamentals of Data Management, INFR11171 - Data Analytics with High Performance Computing |
| Course description |
Data Science means different things to different people. In this course, we interpret the term fairly broadly, and look at the various aspects of the process of extracting knowledge from data. This course is intended to give a broad introduction to the topic but it will get into sufficient detail to provide practical, hands-on experience of some of the tools and techniques used widely in academic research and in commercial environments.
Data Science is a very interdisciplinary field and so the course will expose students to aspects of computer science, software engineering, maths and statistics. It is designed to be accessible whether you come from one of these backgrounds, or whether you come from an applications area (be that in business, science, or elsewhere).
The course will have two main (intertwined) strands that will cover "looking after data" so that it can be used for analysis and the actual processing of this data to provide insight and answers to specific questions.
The course will cover:
- Why managing data better matters, and why it's hard
- Data formats: structuring data and keeping them useful Metadata: describing data and keeping them useful Research data management planning
- Publication and citation of research data
- Persistence, preservation and provenance of research data
- Licensing, copyright and access rights: some things researchers need to know
- Key data analytical techniques such as, classification, optimisation, and unsupervised learning
- Key parallel patterns, such as Map Reduce, for implementing analytical techniques
- Practical introductions to key Data Science tools and their application to data science problems, e.g., R, Python
- Case studies from academia and business
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2019/20, Available to all students (SV1)
|
Quota: None |
| Course Start |
Full Year |
| Course Start Date |
16/09/2019 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Online Activities 45,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
151 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework 100 %
** Formative
1.Peer assessment of answers to set exercises;
2.Self-assessment via online multiple-choice questions to reinforce key topics.
** Summative
1. Written assessment on the importance of data management and associated concepts (25%)
2. Written assessment addressing appropriate data analytic techniques for given problems (25%)
3.Write-Up of an Exercise involving the analysis of an online data set (or sets) (50%)«br /»
The first assessment will be due half-way through the course. This will ensure that all students engage in the course from an early stage
The assessments will be based on core course material, but for an excellent mark, the student will be expected to illustrate and expand on their answer using examples from existing literature or online materials produced by others.«br /»
The write-up will show the results of an exercise designed to test the practical skills of the students. This will involve some programming and/or use of analysis tools. Some degree of flexibility will be incorporated to allow the student to demonstrate that they can extract interesting information from datasets. |
| Feedback |
Feedback will involve peer and self-assessment of the formative assignments and feedback will be provided on summative assessment both individually for each assignment and more generally for the cohort via scheduled sessions. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Have knowledge of the common, popular, important data analytics techniques and the types of compute and data infrastructures used for data analytics
- Understand what data analytics, data science and big data are and the importance of data management
- Understand the importance of structuring research data, the importance of good metadata and describe best practice for publishing, citing and preserving data
- Be able to write programs in R and Python to undertake basic data processing and analysis
- Be able to identify and apply appropriate data analytic techniques to a problem and critically evaluate the analytical performance of a data analytic technique
|
Reading List
| There is no compulsory course text. The book "Doing Data Science" (O'Neil, Schutt; O'Reilly, 2013 ISBN:978-1-4493-5865-5) is a useful complement to parts of the course for those who prefer learning from textbooks. |
Additional Information
| Graduate Attributes and Skills |
- Critical thinking
- Communication of complex ideas in accessible language
- Working in an interdisciplinary field
- Programming and Scripting
-Effective written and diagrammatic communication.
-Data analysis.
-Reflection on learning and practice.
-Adaptation to circumstances.
-Solution Exploration, Evaluation and Prioritisation. |
| Special Arrangements |
Priority given to students on the Data Science, technology and Innovation Online Learning Programme. |
| Additional Class Delivery Information |
Delivered fully online |
| Keywords | DSTI EPCC,Data Science,PItDS,Practical,Data Management,Data Analytics,Online,Online Learning |
Contacts
| Course organiser | Dr Adam Carter
Tel: (0131 6)50 6009
Email: |
Course secretary | Mr Ben Morse
Tel: (0131 6)51 3398
Email: |
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