STATS 3022 - Data Science III

North Terrace Campus - Semester 2 - 2021

This course will introduce the fundamental concepts of modern data science. It will provide students with tools to deal with real, messy data, an understanding of the appropriate methods to use, and the ability to use these tools safely. Topics will include data structures; regression models including lasso regression, ridge regression and non-linearity with splines; classification models including logistic regression, linear discriminant analysis, support vector machines and random forests; and unsupervised learning methods such as principal component analysis, k-means and hierarchical clustering. The practical skills will be focused on data science in R.

  • General Course Information
    Course Details
    Course Code STATS 3022
    Course Data Science III
    Coordinating Unit School of Mathematical Sciences
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact up to 3 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites STATS 2107 or (MATHS 2201 and MATHS 2202) or (MATHS 2106 and 2107)
    Assumed Knowledge Experience with the statistical package R such as would be obtained from STATS 1005 or STATS 2107.
    Course Description This course will introduce the fundamental concepts of modern data science. It will provide students with tools to deal with real, messy data, an understanding of the appropriate methods to use, and the ability to use these tools safely. Topics will include data structures; regression models including lasso regression, ridge regression and non-linearity with splines; classification models including logistic regression, linear discriminant analysis, support vector machines and random forests; and unsupervised learning methods such as principal component analysis, k-means and hierarchical clustering. The practical skills will be focused on data science in R.
    Course Staff

    Course Coordinator: Dr Jono Tuke

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    Syllabus:

    The topics covered will include:

    Overview of modelling framework
    Preprocessing
    Model theory
    Resampling
    Penalised regression
    Classification modelling
    LDA / SVM
    Non-parametric
    Trees
    Random forests
    Feature selection
    Unsupervised learning
    Learning outcomes:

    On successful completion of this course, students will:

    1. Demonstrate an understanding of the foundational principles of machine learning
    2. Recognise which method to use for a given data analysis problem.
    3. Demonstrate an understanding the statistical underpinning of the chosen method.
    4. Implement safely any chosen method and interpret the results.
    5. Be confident to apply the methods to large datasets.
    6. Apply the theory in the course to solve a range of problems at an appropriate level of difficulty.
    University Graduate Attributes

    No information currently available.

  • Learning & Teaching Activities
    Learning & Teaching Modes
    The structure consists of 

    - Weekly topic videos watched in own time. 
    - One interpretation workshop a week held in the lecture time. 
    - One implementation workshop a week held in practical time. 


    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Activity Quantity Workload hours
    Topic videos 12 24
    Interpretation workshop 12 24
    Implementation workshop 12 24
    Assignments 3 33
    Online test 3 33
    Online quizzes 12 18
    Total 156
    Learning Activities Summary

    No information currently available.

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Assessment Percent of final mark
    Online quizzes 5
    Written assignments (3) 15
    Test (3) 30
    Written exam 30
    Practical exam 20
    Assessment Detail
    Assessment Distributed Due Weighting
    A1 Week 2 Friday Week 4 5%
    A2 Week 6 Friday Week 8 5%
    A3 Week 10 Friday Week 12 5%
    Test 1 Week 2 10%
    Test 2 Week 6 10%
    Test 3 Week 10 10%
    Online quizzes Weekly Weekly 5%
    Practical exam TBD (week 12 or exam period) 20%
    Final exam Examination period 30%
    Submission

    No information currently available.

    Course Grading

    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.

  • Student Feedback

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    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.

  • Student Support
  • Policies & Guidelines
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