CSMAX101-20B (HAM)

The World of Data

15 Points

Edit Header Content
Division of Health Engineering Computing & Science
School of Computing and Mathematical Sciences
Department of Mathematics and Statistics

Staff

Edit Staff Content

Convenor(s)

Paul Brown

Paul Brown
5279
G.3.30
See Moodle
paul.brown@waikato.ac.nz

Chaitanya Joshi

Chaitanya Joshi
4019
G.3.22
See Moodle
chaitanya.joshi@waikato.ac.nz

Lecturer(s)

Paul Brown

Paul Brown
5279
G.3.30
See Moodle
paul.brown@waikato.ac.nz

Administrator(s)

: maria.admiraal@waikato.ac.nz
: rachael.foote@waikato.ac.nz

Placement/WIL Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)

Librarian(s)

You can contact staff by:

  • Calling +64 7 838 4466 select option 1, then enter the extension.
  • Extensions starting with 4, 5, 9 or 3 can also be direct dialled:
    • For extensions starting with 4: dial +64 7 838 extension.
    • For extensions starting with 5: dial +64 7 858 extension.
    • For extensions starting with 9: dial +64 7 837 extension.
    • For extensions starting with 3: dial +64 7 2620 + the last 3 digits of the extension e.g. 3123 = +64 7 262 0123.
Edit Staff Content

Paper Description

Edit Paper Description Content

This course will focus on some of the fundamental ideas and tools for data analysis and statistics, and will also look at some of the current issues surrounding data. The ideas and skills learnt will be useful for both practical applications and quantitative research.

This paper is designed to provide an appreciation of statistical tools. The emphasis is on interpretation and critical reasoning from
statistical analyses, not on the technical details and mathematics of the analyses themselves. There will be some basic data handling and analyses done using Microsoft Excel. No previous statistical or mathematical knowledge is assumed beyond the numeracy requirements to enter university.

Edit Paper Description Content

Paper Structure

Edit Paper Structure Content

All lectures are recorded using Panopto and are uploaded onto Moodle for students to view. Three lectures will be uploaded each week.

Supplementary videos (such as Excel instruction videos) will also be uploaded onto Moodle.

Dedicated computer lab times are available for students to use, however these will typically be unsupervised.

Edit Paper Structure Content

Learning Outcomes

Edit Learning Outcomes Content

Students who successfully complete the course should be able to:

  • Understand and interpret data summaries and graphs
    Linked to the following assessments:
  • Understand different data types, and how they may be distributed
    Linked to the following assessments:
  • How to collect data, depending on the type of study you are performing and the questions you are trying to answer
    Linked to the following assessments:
  • Understand different sampling methods, and their advantages/disadvantages over other methods
    Linked to the following assessments:
  • Describe the relationships between two variables (for both categorical and continuous data) and understand the measures that are used to describe them
    Linked to the following assessments:
  • Analyse hypothesis tests for means and proportions
    Linked to the following assessments:
  • Understand what a confidence interval is, and how to interpret it
    Linked to the following assessments:
  • Describe some of the important current issues surrounding data
    Linked to the following assessments:
  • Perform some simple data analyses using Microsoft Excel
    Linked to the following assessments:
Edit Learning Outcomes Content
Edit Learning Outcomes Content

Assessment

Edit Assessments Content

A multiple choice quiz will be uploaded onto Moodle every week. These quizzes will start during week 2 and will continue through until week 12. You will generally have one week to complete these, and they will be due at 5pm every Friday.

There will be two tests throughout the course, and will be held in week 6 and week 12. These will be uploaded onto Moodle, and will need to be submitted online through Moodle. Instructions will be given to you before the test commences.

Also, there is one assignment where you will perform some data analyses using Microsoft Excel. This assignment can be done in groups if you choose (maximum of 3 students per group).

Edit Additional Assessment Information Content

Assessment Components

Edit Assessments Content

The internal assessment/exam ratio (as stated in the University Calendar) is 100:0. There is no final exam. The final exam makes up 0% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 100:0 or 0:0, whichever is more favourable for the student. The final exam makes up either 0% or 0% of the overall mark.

Component DescriptionDue Date TimePercentage of overall markSubmission MethodCompulsory
1. Weekly Quiz
20
  • Online: Submit through Moodle
2. Test 1
21 Aug 2020
No set time
25
  • Online: Submit through Moodle
3. Test 2
16 Oct 2020
No set time
25
  • Online: Submit through Moodle
4. Excel Assignment
30
  • Online: Submit through Moodle
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
Edit Assessments Content

Required and Recommended Readings

Edit Required Readings Content

Required Readings

Edit Required Readings Content
There is no required textbook for this course. All course material will be provided.
Edit Required Readings Content

Recommended Readings

Edit Recommended Readings Content
Seeing Through Statistics 4th edition by Jessica Utts (Cengage)
Edit Recommended Readings Content

Online Support

Edit Online Support Content

All lectures and lecture materials will be provided on Moodle.

Microsoft Excel is needed to perform data analysis tasks, and Microsoft Word will be needed to write the assignment report. Microsoft Office 365 is available and free to all enrolled University of Waikato students. Instructions to download can be found through the following link:

https://www.waikato.ac.nz/ict-self-help/guides/free-microsoft-office-suite-download

Edit Online Support Content

Workload

Edit Workload Content
Students are expected to spend around 10 hours a week on this course. This includes lectures, time spent on weekly quizzes, studying for assignments and tests, and your own self-directed study.
Edit Workload Content

Linkages to Other Papers

Edit Linkages Content

For further study in data analysis and statistics (such as STATS221), students should aim for a mark of a B- or better in this course.

Edit Linkages Content