MATHS 1004 - Mathematics for Data Science I

North Terrace Campus - Semester 2 - 2022

Data science is one of the highest-paying graduate jobs, for those with the relevant mathematical training. This course introduces fundamental mathematical concepts relevant to data and computer science and provides a basis for further study in data science, statistics and cybersecurity. Topics covered are probability: sets, counting, probability axioms, Bayes theorem; optimisation and calculus: differentiation, integration, functions of several variables, series approximations, gradient descent; linear algebra: vectors and matrices, matrix algebra, vector spaces; discrete mathematics: induction, difference equations. The course draws connections between each of these fundamental mathematical concepts and modern data science applications, and introduces Python programming for data wrangling, algorithms, and visualisation.

  • General Course Information
    Course Details
    Course Code MATHS 1004
    Course Mathematics for Data Science I
    Coordinating Unit School of Mathematical Sciences
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 5 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites At least a C- in SACE Stage 2 Mathematical Methods or IB Mathematics: at least 3 in applications and interpretations HL; or 4 in analysis and approaches SL
    Incompatible MATHS 1008, MATHS 1010, MATHS 1012
    Restrictions Not available for BMaSc or BMaSc(Adv) students
    Course Description Data science is one of the highest-paying graduate jobs, for those with the relevant mathematical training. This course introduces fundamental mathematical concepts relevant to data and computer science and provides a basis for further study in data science, statistics and cybersecurity. Topics covered are probability: sets, counting, probability axioms, Bayes theorem; optimisation and calculus: differentiation, integration, functions of several variables, series approximations, gradient descent; linear algebra: vectors and matrices, matrix algebra, vector spaces; discrete mathematics: induction, difference equations. The course draws connections between each of these fundamental mathematical concepts and modern data science applications, and introduces Python programming for data wrangling, algorithms, and visualisation.
    Course Staff

    Course Coordinator: Dr Stuart Johnson

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course students will be able to:

    1. Demonstrate understanding of basic mathematical concepts in data science, relating to linear algebra, probability, and calculus.

    2. Employ methods related to these concepts in a variety of data science applications.

    3. Apply logical thinking to problem-solving in context.

    4. Use appropriate technology to aid problem-solving and data analysis.

    5. Demonstrate skills in writing mathematics.





    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)

    Attribute 1: Deep discipline knowledge and intellectual breadth

    Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.

    all

    Attribute 2: Creative and critical thinking, and problem solving

    Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.

    3,4,5
  • Learning Resources
    Required Resources
    All required resources are provided in MyUni.
    There is no requirement to buy a textbook.
    Recommended Resources
    1. Lay: Linear Algebra and its Applications 4th ed. (Addison Wesley Longman)
    2. Stewart: Calculus 7th ed. (international ed.) (Brooks/Cole)
    3. Graham, Knuth, Patashnik: Concrete Mathematics (Addison-Wesley)
    4. Deisenroth, Faisal, Ong: Mathematics for Machine Learning (Cambridge University Press)

  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course uses a flipped learning model, the course materials are contained in modules on MyUni with a mix of text, videos and exercises to work through each week.
    In addition we have a weekly workshop including interactive exercises to support your learning of this material as well as alternating
    computer practical and tutorial classes to provide students with class/small group/individual assistance. There are computer based quizzes and written assignments to provide formative assessment opportunities for students to practise techniques and develop their understanding of the course.
    Workload

    No information currently available.

    Learning Activities Summary
    Lecture Outline

    Fundamentals (weeks 1-2)
    - Approximation
    - Sets and Functions
    - Sums and Series

    Probability (weeks 3-4) 
    - Counting 
    - Discrete random variables 
    -Conditional probability 
    - Bayes theorem

    Representing Data with Matrices (weeks 5-6)
    - Matrix operations
    - Matrix equations
    - Determinants

    Solving Linear Equations (weeks 7-8)
    - Row reduction
    - applications
    - linear independence

    Dimensional Reduction (weeks 9-10
    - Eigenvalues and eigenvectors 
    - Dimension reduction 

    Applications of Calculus (weeks 11-12)
    - Integration and continuous probability distributions 
    - Series Approximations






  • 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

    Component

    Weighting
    Assignments 20%
    Quizzes 10%
    Test 1 10%
    Test 2 10%
    Exam 50%
    Assessment Related Requirements
    An aggregate score of 50% is required to pass the course.
    Assessment Detail


    Assessment details will be provided on the MyUni site for this course.
    Submission
    1. All written assignments are to be e-submitted following the instructions on MyUni.
    2. Late assignments will not be accepted without a medical certificate.
    3. Written assignments will have a one week turn-around time for feedback to students.
    See MyUni for more comprehensive details regarding assignment submission, our late policy etc.
    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

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    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
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

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