DATA101-22S2 (C) Semester Two 2022

Introduction to Data Science

15 points

Details:
Start Date: Monday, 18 July 2022
End Date: Sunday, 13 November 2022
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 31 July 2022
  • Without academic penalty (including no fee refund): Sunday, 2 October 2022

Description

Data Science is a fast growing, important, and globally in-demand discipline. This course is designed to introduce students to the fundamentals of this field. It will start by introducing key mathematical and statistical concepts and applications like exploratory data analysis, probability (with a focus on essential theories, discrete and continuous random variables), modelling, inference, and bivariate data. It will also address a range of more applied topics where data is important to making decisions, including data wrangling, data analysis, and data visualisation, supported by the statistical programming language R.

Learning Outcomes

University Graduate Attributes

This course will provide students with an opportunity to develop the Graduate Attributes specified below:

Critically competent in a core academic discipline of their award

Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.

Employable, innovative and enterprising

Students will develop key skills and attributes sought by employers that can be used in a range of applications.

Biculturally competent and confident

Students will be aware of and understand the nature of biculturalism in Aotearoa New Zealand, and its relevance to their area of study and/or their degree.

Globally aware

Students will comprehend the influence of global conditions on their discipline and will be competent in engaging with global and multi-cultural contexts.

Prerequisites

1. MATH101, or
2. NCEA 14 Credits at level 3 Mathematics, or
3. Cambridge: D at A level or an A at AS level in Mathematics, or
4. IB: 4 at HL or 5 at SL in Mathematics, or
5. Approval of the Head of School based on alternative prior learning.

Restrictions

Timetable 2022

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Tuesday 15:00 - 16:00 Jack Erskine 031 Lecture Theatre
18 Jul - 28 Aug
12 Sep - 23 Oct
Lecture B
Activity Day Time Location Weeks
01 Wednesday 12:00 - 13:00 F3 Lecture Theatre
18 Jul - 28 Aug
12 Sep - 23 Oct
Lecture C
Activity Day Time Location Weeks
01 Thursday 12:00 - 13:00 F3 Lecture Theatre
18 Jul - 28 Aug
12 Sep - 23 Oct
Lecture D
Activity Day Time Location Weeks
01 Friday 09:00 - 10:00 Jack Erskine 031 Lecture Theatre
18 Jul - 28 Aug
12 Sep - 23 Oct
Tutorial A
Activity Day Time Location Weeks
01 Thursday 11:00 - 12:00 Jack Erskine 033 Lab 1
18 Jul - 28 Aug
12 Sep - 23 Oct
02 Thursday 16:00 - 17:00 Jack Erskine 033 Lab 1
18 Jul - 28 Aug
12 Sep - 23 Oct
03 Friday 12:00 - 13:00 Jack Erskine 033 Lab 1
18 Jul - 28 Aug
12 Sep - 23 Oct

Course Coordinator / Lecturer

Marco Reale

Lecturers

Giulio Dalla Riva and Nicholas Ward

Indicative Fees

Domestic fee $926.00

International fee $4,563.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

For further information see Mathematics and Statistics .

All DATA101 Occurrences

  • DATA101-22S2 (C) Semester Two 2022
  • DATA101-22S2 (D) Semester Two 2022 (Distance)