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SCIENCE 1500 - Introductory Data Science - Becoming Smart About Data
Delve into the rapidly emerging field of data science and learn to apply it to your future career. Touted as the ?sexiest job of the 21st century? by the Harvard Business Review, and the best current job by Forbes magazine in 2016, students with data science skills are sought after across all industries. Data science techniques will enhance your employability regardless of the degree you are studying. Why? Because ?big data? and advanced problem solving skills inform decision making and innovation for all organisations. Scientists are transforming the research frontier by using machine learning techniques to find Higgs bosons, classify galaxies or unravel genetic codes. Businesses are using the same techniques to identify credit card fraud, perform social network analysis and to develop automatic approaches to targeted marketing. In this course, you will become familiar with all major modern approaches to data science, including machine learning techniques and big data analysis strategies. Critically, students in this course will learn via an innovative and multi-disciplinary approach to problem solving. After a basic introduction to the different types of data analysis problem, students will be introduced to a variety of algorithms from the research frontier. To keep the course accessible to a broad audience, no mathematical knowledge will be assumed, and students will instead gain a hands-on, intuitive knowledge of how the algorithms work by using simple spreadsheet examples. A wide variety of problems from physics, chemistry, biology, health sciences and business will be used to encourage students to view problems through the lens of a different discipline; this will enhance your ability to spot innovative solutions to research problems in your own field. For business students, it will give you an ability to determine what your company or employer needs to remain competitive. Through this topic, you will develop transferable skills that will allow you to connect science to everyday issues, and you will also learn how to use real-world problems to solve new problems in science.
General Course Information
Course Details
| Course Code | SCIENCE 1500 |
|---|---|
| Course | Introductory Data Science - Becoming Smart About Data |
| Coordinating Unit | Physics |
| Term | Semester 2 |
| Level | Undergraduate |
| Location/s | North Terrace Campus |
| Units | 3 |
| Contact | Up to 4 hours per week |
| Available for Study Abroad and Exchange | Y |
| Assessment | Assignments, project report and tests |
Course Staff
Course Coordinator: Professor Martin White
Course Timetable
The full timetable of all activities for this course can be accessed from Course Planner.
Learning Outcomes
Course Learning Outcomes
| 1 |
an understanding of what data science is and how it is both practised and applied |
| 2 | a knowledge of the different classes of data science algorithm (including k-means clustering, principal component analysis, regression analysis, association rules, k-nearest neighbours, neural networks, social network analysis, self-organising maps, decision trees and random forests) |
| 3 | an ability to suggest which type of algorithm would suit a particular problem from business, science or health science |
| 4 | an ability to confidently discuss data science problems, both orally and in writing |
| 5 | an ability to interpret the output of data science algorithms |
| 6 | an understanding of data science problems in the abstract, in addition to their discipline-specific content |
| 7 | critical and logical thinking |
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
| University Graduate Attribute | Course Learning Outcome(s) |
|---|---|
|
Attribute 1: Deep discipline knowledge and intellectual breadth Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts. |
1,2,3,4,5,6 |
|
Attribute 2: Creative and critical thinking, and problem solving Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges. |
3,5,6,7 |
|
Attribute 3: Teamwork and communication skills Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals. |
3,4,7 |
|
Attribute 4: Professionalism and leadership readiness Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities. |
2,3,4,5,6 |
|
Attribute 8: Self-awareness and emotional intelligence Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions. |
Learning Resources
Online Learning
Learning & Teaching Activities
Learning & Teaching Modes
- 1 x 2 hr weekly lecture
- 1 x 2 hr weekly computer laboratory sessions
Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Learning Activities Summary
The second session each week will involve hands-on computer work, in which an intuitive knowledge of data science algorithms will be developed by using spreadsheet examples. Here, students will be able to actually apply the techniques that they learnt in the workshop sessions. These sessions will involve assessment via question sheets. In addition to these activities, two literature comprehension exercises will be run to encourage students to engage with the research literature (this will be assessed by written report). Finally, a project will be developed by students in the last two weeks of the computer lab sessions, with assessment via written report.
Specific Course Requirements
Assessment
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
| Assessment Task | Task Type | Weighting | Hurdle | Learning Outcome | Due |
| PC lab in-class exercises | Formative & Summative | 30% | No | 1,2,3 | Weeks 1-10 |
| Literature exercise 1 | Formative & Summative | 15% | No | 1-7 | Week 4 |
| Literature exercise 2 | Formative & Summative | 15% | No | 1-7 | Week 9 |
| In-semester tests | Formative & Summative | 10% | No | 1,2,3 | Week 6 & 10 |
| Final project | Summative | 30% | No | 1-7 | Week 12 |
Assessment Related Requirements
The workshops are compulsory as they involve group discussion that cannot be conducted online.
The learning outcomes for this course are substantially dependent on computer laboratory experience and practice, and on the outcomes of the workshop discussions.
Assessment Detail
Students will complete a total of 2 in-semester tests during semester (worth 5% each). These are designed to refresh knowledge of a topic and indicate the major points students are required to learn in preparation for the final project. Tests will consist of short answer, discursive questions. They are held at the start of tutorial sessions. Students receive feedback one week later.
Final project report (30%)
Students will prepare a 2000 word report on the results of the project undertaken during the final two weeks of computer lab sessions. Students will work individually, and will pick problems from a pre-prepared set. Students will be assessed on their problem-solving ability, understanding of data science techniques and communication skills.
Literature review exercises (30%)
Students will complete two literature comprehension exercises during the course. Examples of data science applications will be taken from the research literature. Students will have to read the paper, then attend a 2 hour tutorial at which they get to ask questions. They will then prepare a written report of 1000 words detailing their understanding. 2 of these will be completed in total (worth 15% each).
PC lab in-class tests (30%)
During the first ten weeks of PC lab sessions, students will complete a short question sheet testing their knowledge of the algorithm that is introduced that week. This will include questions relating to changing the spreadsheet example and documenting the changes (via either multiple choice questions, or short written answers). Students will receive feedback one week later.
Submission
Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme)| Grade | Mark | Description |
|---|---|---|
| FNS | Fail No Submission | |
| F | 1-49 | Fail |
| P | 50-64 | Pass |
| C | 65-74 | Credit |
| D | 75-84 | Distinction |
| HD | 85-100 | High Distinction |
| CN | Continuing | |
| NFE | No Formal Examination | |
| RP | Result Pending |