Postgraduate Course: Practical Introduction to Data Science (short course) (INFD11015)
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 | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This online course is designed to help you apply ideas from Data Science to you work. It is designed for technically-minded people and assumes some basic knowledge of computer programming. It introduces ideas from data science, data management and data engineering. It is broad rather than deep, but it aims to provide you with enough practical skills to tackle a real data science problem by the end of the course. |
| Course description |
The 10-week course is made up of 7 taught blocks followed by an assessed piece of coursework. The taught blocks will cover: Motivation & Groundwork ; Data Concepts & Processes ; Munging, Cleaning, Storing & Accessing ; Exploring, Summarising & Visualising ; Experimenting & Predicting ; Describing & Sharing ; Deploying & Scaling The course is based around recorded lectures, broken into short videos. These recorded lectures will be complemented with a weekly interactive online tutorial with the course organiser which will allow students to ask questions and discuss topics of interest. The concepts and ideas introduced in the lectures are explored in practical exercises to give hands-on experience of applying the techniques.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2022/23, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Flexible |
| Course Start Date |
08/08/2022 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Seminar/Tutorial Hours 5,
Feedback/Feedforward Hours 5,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
88 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
The assessment will be a single piece of assessed coursework set towards the end of the course. This will include a written component and a practical component which will involve the students doing some basic analysis with a real world dataset. The written answers will be submitted in a short report which will also describe the practical work undertaken. The assessment is designed to take no more than 20 hours, and the students will have at least four weeks to undertake the assignment from when it is posted to the deadline. |
| Feedback |
Students will receive written feedback with their mark for the assessment. Practical exercises during the course are not assessed, but form an integral part of the course. Students are invited to submit work undertaken in practical exercises for discussion at tutorials. A short example question will be set around week 4. Submission of this piece of work is optional, but individual formative feedback will be provided before the course assessment. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Explain the meaning of data analytics, data science and big data and appreciate the importance of data management
- Describe and apply important data analytics techniques including basic descriptive statistics, clustering and classification
- Identify appropriate data storage mechanisms and analytic techniques for a given problem
- Assess the value of metadata, identifiers and related data management concepts in a given scenario
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Reading List
| There is no compulsory course text for this course. For students who like learning from text books, I recommend Doing Data Science, C. O'Neil & R. Schutt (ISBN: 1449358659) which overlaps with the course material and has the same practical ethos as the course. Full text is available free online to UoE students. |
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.
-Solution Exploration, Evaluation and Prioritisation. |
| Special Arrangements |
This course is delivered stand-alone for students on a specific SFC Postgraduate Professional Development programme of study. It cannot be taken by any other students. Students interested in the material should instead look at INFD11010 ; Practical introduction to Data Science, or for on-campus students: INFR11176 ; Fundamentals of Data Management and INFR11171 ; Data Analytics with High Performance Computing |
| Keywords | Data Science,Data Engineering,Data Management,Online,Machine Learning,Data Analytics |
Contacts
| Course organiser | Dr Adam Carter
Tel: (0131 6)50 6009
Email: A.carter@epcc.ed.ac.uk |
Course secretary | Miss Jemma Auns
Tel: (0131 6)51 3545
Email: Jemma.Auns@ed.ac.uk |
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