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Course Description

TopData analysis is crucial to evaluating and designing solutions and applications, and to understanding users' information needs and uses. Often this data is distributed online among many web pages, stored in databases or available in large text files, and may be too large to obtain or process manually. Instead, we need an automated way to gather, parse and summarize the data before we can do more advanced analysis. This module explores ways to accomplish these tasks in quick and easy yet useful and repeatable ways. The ultimate goal is to glean insights from the data through analysis and basic visualizations.

Learning Outcomes

TopBy the end of the module, students should be able to:
1. Take any real-world problem and figure out the data inputs and outputs required to provide insights into a solution to the problem.
2. Understand how data is produced, filtered and recorded, and how such processes affect the richness, accuracy, interpretation and use of the data.
3. Have an appeciation for the politics of data, that data may be neutral, but its use may not be.
4. Independently find, learn and use publicly available data and data analysis tools.
5. Demonstrate the ability to solve a complex problem with the skills developed in the module.

Prerequisites

Top
Prior experience with quantitative and qualitative data analysis methods (eg. NM2103 / NM2104) will be highly advantageous!

Syllabus

Top**This is a rough outline of the course that WILL CHANGE when the semester progresses.

Week 1: Course Administration (eg. why you should NOT take this course!)
Week 2: What is "data"? Why bother anyway?
Week 3: What can become data? How does the "what" become data?
Week 4: The "politics" of data. Who said what to whom, why the who said what to whom,how the who said what to whom, when the who said what to whom, where the who said what to whom.
Week 5: 24/7/365 (or 366!) data gathering and analysis. Who needs sleep anyway?!
Week 6: Preparing to tear it all apart!
Week 7-12: Putting it all together.

Assessment

TopThis is a 100% CA module consisting of the following components:
Assignments (50%)
There will be six individual assignments which total 60% of the student grade. These assignments will provide hands-on experience with specific topics covered in the syllabus. The students will post weekly to an online forum and blog. This online environment will be a space for collaboration, reflection and the generation of new ideas.
Class Attendence (10%)
Turning up for discussions is important!
Facebook Group (10%)
Participate and engage in our class learning community online!
Group Project (30%)
The students will also work in groups towards a final project applying the concepts, skills and tools from the course. The project will have students work in groups and will constitute 30% of their grade.

Workload

Top2-0-2-3-3

Workload Components : A-B-C-D-E
A: no. of lecture hours per week
B: no. of tutorial hours per week
C: no. of lab hours per week
D: no. of hours for projects, assignments, fieldwork etc per week
E: no. of hours for preparatory work by a student per week