DATASCI 100 : Data Science for Everyone

Science

2025 Semester Two (1255) (15 POINTS)

Course Prescription

Explores how to use data to make decisions through the use of visualisation, programming/coding, data manipulation, and modelling approaches. Students will develop conceptual understanding of data science through active participation in problems using modern data, hands-on activities, group work and projects. DATASCI 100 will help students to build strong foundations in the science of learning from data and to develop confidence with integrating statistical and computational thinking.

Course Overview

This course provides a practical introduction to data science, suitable for all students regardless of previous experience in statistics, computer science, or mathematics. Data can be sourced and generated from images, text, sounds, videos, social media, as well as through surveys and sensors. Data can be used to create products, such as predictions for elections, recommendations for songs, or systems that monitor the use of te reo Māori in the evening news. In this course, students will combine statistical and computational approaches to improve their understanding of how data can be used within science. Across the course, students will be introduced to a wide range of data science applications and be supported to think creatively, ethically, and responsibly about how to use data, models, and technology to produce solutions to identified problems or challenges.

Course Requirements

No pre-requisites or restrictions

Capabilities Developed in this Course

Capability 1: People and Place
Capability 2: Sustainability
Capability 3: Knowledge and Practice
Capability 4: Critical Thinking
Capability 5: Solution Seeking
Capability 6: Communication
Capability 7: Collaboration
Capability 8: Ethics and Professionalism
Graduate Profile: Bachelor of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Select and apply data science approaches to create a product-based solution to an identified problem or challenge (Capability 3 and 5)
  2. Transform data and work with a range of data sources, files and structures (Capability 3)
  3. Describe and apply responsible, and culturally-responsive data practices, acknowledging Māori Data Sovereignty (Capability 1 and 8)
  4. Select and apply appropriate technology to explore and visualise data, considering automated and reproducible approaches (Capability 4 and 6)
  5. Use statistical concepts, mathematical representations and computational techniques in the process of developing models, rules, and generalisations (Capability 3 and 4)
  6. Produce communications that integrate statistical and computational thinking (Capability 6 and 7)
  7. Consider the impact of the data science products created (Capability 2 and 5)

Assessments

Assessment Type Percentage Classification
Labs 30% Individual Coursework
Project 20% Individual Coursework
Coursework 10% Group & Individual Coursework
Quizzes 10% Individual Coursework
Final Exam 30% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7
Labs
Project
Coursework
Quizzes
Final Exam

Key Topics

  • Module 1: Creating data science products using informal generative models
  • Module 2: Producing data-based communications involving visualisations
  • Module 3: Implementing algorithms for discovering and analysing features of data
  • Module 4: Developing models from data to make predictions and classifications

Special Requirements

Lab sessions have been timetabled to provide assistance with online lab tasks, quizzes, and the projects, but attendance is not mandatory.
The test will be computer-based and may be held at a time other than the standard lecture time, including in the evening.
The final exam will be computer-based.

Tuākana

Tuākana Science is a multi-faceted programme for Māori and Pacific students providing topic specific tutorials, one-on-one sessions, test and exam preparation and more. Explore your options at
https://www.auckland.ac.nz/en/science/study-with-us/pacific-in-our-faculty.html
https://www.auckland.ac.nz/en/science/study-with-us/maori-in-our-faculty.html

Statistics has a Tuākana Programme where there is a workspace and a social space shared with Science Tuakana students. Tutorials and one-to-one assistance are available. Tuākana tutors/mentors work alongside the lecturer to support students with assignments and revision for the quizzes and exams. For more information and to find contact details for the Statistics Tuākana coordinator, please see https://www.auckland.ac.nz/en/science/study-with-us/maori-and-pacific-at-the-faculty/tuakana-programme.html
Contacts are Susan Wingfield (s.wingfield@auckland.ac.nz) and Heti Afimeimounga (h.afimeimounga@auckland.ac.nz).

Workload Expectations

This course is a standard 15 point course and students are expected to spend 12.5 hours per week involved in each 15 point course that they are enrolled in. For this course, you can expect 2 hours of lectures, a 2 hour online lab, 4 hours of reading and thinking about the content and 4.5 hours of work on quizzes, the project, other coursework, and/or final exam preparation.

Delivery Mode

Campus Experience

The lectures will be conducted on ZOOM (not in person). Lectures will also available as recordings.
The drop-in lab sessions will not be available as recordings.
Attendance on campus may be required for the test and exam.
The activities for the course are scheduled as a standard weekly timetable.

Learning 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.

There is a course book for this course, which can be accessed within Canvas.

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.

As this will be first semester the course has been taught, there is no feedback from previous students.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework, tests and examinations 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. A student's assessed work may be reviewed against electronic source material using computerised detection mechanisms. Upon reasonable request, students may be required to provide an electronic version of their work for computerised review.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

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.

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 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.

Learning Continuity

In the event of an unexpected disruption we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

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.

Published on 26/10/2024 11:07 a.m.