SCIENCE 1500 - Introductory Data Science - Becoming Smart About Data
North Terrace Campus - Semester 2 - 2018
General Course Information
Course Code SCIENCE 1500 Course Introductory Data Science - Becoming Smart About Data Coordinating Unit School of Physical Sciences 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 Course Description 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. Becoming Smart About Data
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.
In this course you?ll learn how Google knows your current location and why Facebook keeps trying to sell you active wear. Critically, students in this course will learn via an innovative and multi-disciplinary approach to problem solving, drawing on your own knowledge to solve problems.
You will uncover trends, algorithms and solutions to problems that cross borders, from biology to business. You will also be encouraged to view problems through the lens of a different discipline; this will give you a distinct advantage over someone who is merely trained to apply algorithms without creativity.
Through this topic, you will develop transferrable skills that will allow you to connect science to everyday issues.
Course StaffDr Martin White
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesThe anticipated knowledge, skills and attitudes to be developed by the student in this course are:
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) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1,2,3,4,5,6 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
3,5,6,7 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
3,4,7 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
2,3,4,5,6 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
Online LearningMyUni: Teaching materials and course documentation will be posted on the MyUni website (http://myuni.adelaide.edu.au).
Learning & Teaching Activities
Learning & Teaching ModesThis course will be delivered by the following means:
- 1 x 2 hr weekly lecture
- 1 x 2 hr weekly computer laboratory sessions
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.A student enrolled in a 3 unit course, such as this, should expect to spend, on average 12 hours per week on the studies required. This includes both the formal contact time required to the course (e.g., lectures and practicals), as well as non-contact time (e.g., reading and revision).
Learning Activities SummaryThe first class each week will be devoted to group and open discussion of what data science is and how it works. This will include short lecture segments that introduce and develop an understanding of different data science methods (e.g. k-means clustering, neural networks), then group discussion of problems that use that particular technique. Sample output of data science algorithms will be provided for a range of problems from business, health science, geology, chemistry and physics to aid discussion. Students will spend some time discussing problems with students in the same discipline, and some time discussing problems with students from other disciplines. This will facilitate an understanding of the similarities and differences between naively disparate problems.
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 RequirementsBoth the computer laboratory sessions and the workshops are compulsory.
Small Group Discovery ExperienceThis course offers small group discovery experiences within the literature comprehension exercise, computer lab sessions, group discussion activity and final project.
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 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 RequirementsComputer laboratory sessions are compulsory. This includes attendance and conduct of the required computer work, which will be assessed during the session.
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 DetailIn-semester tests (total of 10%)
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.
SubmissionIf an extension is not applied for, or not granted then a penalty for late submission will apply. A penalty of 10% of the value of the assignment for each calendar day that the assignment is late (i.e. weekends count as 2 days), up to a maximum of 50% of the available marks will be applied. This means that an assignment that is 5 days late or more without an approved extension can only receive a maximum of 50% of the marks available for that assignment.
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
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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