STATS 100 : Concepts in Statistics
Science
2020 Semester One (1203) (15 POINTS)
Course Prescription
Course Overview
The overall goal of STATS 100 is to increase both your confidence and your personal interest in Statistics and Data Science. So if you've done a little bit of Statistics study in the past or avoided it completely, and/or think Statistics is boring or difficult, then this course should convince you how awesome working with data really is! We will focus on how to use data to make decisions by integrating statistical and computational thinking. STATS 100 will develop your conceptual understanding of Statistics and Data Science through active participation in problems using real data, hands-on activities, group work and projects. The course makes full use of appropriate technology and prepares students for further study in Statistics, in particular STATS 101/STATS 108. STATS 100 covers similar material to NCEA Statistics but with a greater focus on visualisation, computation (including coding), data manipulation, and modelling approaches. The lectures, tutorials and labs are designed to be interactive and to build on each other over the course. If you are intending to study any subject that requires working with data, this course will help you build strong foundations in the science of learning from data.
Capabilities Developed in this Course
| Capability 1: | Disciplinary Knowledge and Practice |
| Capability 2: | Critical Thinking |
| Capability 3: | Solution Seeking |
| Capability 4: | Communication and Engagement |
| Capability 5: | Independence and Integrity |
| Capability 6: | Social and Environmental Responsibilities |
Learning Outcomes
- Manipulate data and work with a range of data sources, files and structures (Capability 1)
- Select appropriate technology and software to explore and visualise data (Capability 1)
- Reason critically with data, models and visualisations when forming arguments or making decisions (Capability 2)
- Use mathematical representations and computational techniques in the process of developing models, rules and generalisations (Capability 3)
- Produce written reports that integrate statistical and computational thinking (Capability 4)
- Collaborate with others to design and carry out statistical investigations (Capability 4)
- Describe and apply responsible and ethical practices when obtaining and using data from public sources (Capability 5)
- Consider practical consequences of data-based decisions and clearly communicate uncertainty (Capability 6)
Assessments
| Assessment Type | Percentage | Classification |
|---|---|---|
| Final Exam | 30% | Individual Examination |
| Tests | 30% | Individual Test |
| Tutorials | 10% | Group & Individual Coursework |
| Assignments | 12% | Individual Coursework |
| Quizzes | 18% | Individual Coursework |
| 5 types | 100% |
| Assessment Type | Learning Outcome Addressed | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |||
| Final Exam | ||||||||||
| Tests | ||||||||||
| Tutorials | ||||||||||
| Assignments | ||||||||||
| Quizzes | ||||||||||
Students must obtain at least 45% in the final exam to pass.
Key Topics
- Topic 1: Making predictions
- Topic 2: Conducting tests
- Topic 3: Building models
- Topic 4: Informing decisions
Workload Expectations
This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.
For this course, you can expect 3 hours of lectures, a one hour tutorial, two hours of reading and thinking about the content and four hours of work on assignments and/or test preparation.
Digital Resources
Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).
Please remember that the recording of any class on a personal device requires the permission of the instructor.
Copyright
The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.
You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.
Academic Integrity
The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.
Inclusive Learning
All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.
Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website at http://disability.auckland.ac.nz
Special Circumstances
If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.
If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page: https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.
This should be done as soon as possible and no later than seven days after the affected test or exam date.
Student Feedback
During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.
At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.
Your feedback helps to improve the course and its delivery for all students.
Student Charter and Responsibilities
The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter (https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html).
Disclaimer
Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.
In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.