TECH 1002 - Data Analytics for Technology

North Terrace Campus - Semester 2 - 2020

This course provides a practical introduction to probability, statistics and data analysis required to extract information and build understanding from data in technology applications . It will provide the basic tools required to extract data relevant to a given problem, prepare it for analysis, and then analyse it in order to explain and/or predict systems behaviours. There is a strong practical focus using Excel and the R programming language. Topics to be covered will include: an introduction to probability (counting, probability axioms and Bayes' Theorem); organisation, description and visualisation of data; random variables and probability distributuons ; standard statistical inference analyses ; and, basic regression methods.

  • General Course Information
    Course Details
    Course Code TECH 1002
    Course Data Analytics for Technology
    Coordinating Unit Centre for STEM Education and Innovation
    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 SAGE Stage 2 Mathematical Methods or 4 in International Baccalaureate Mathematics SL
    Incompatible ECON 1008, MATHS 1005, SCIENCE 1500, STATS 1000 and STATS 1004
    Restrictions Only Available to students in the Bachelor of Technology
    Assessment Ongoing assessment, exam
    Course Staff

    Course Coordinator: Professor Dmitri Kavetski

    Lectures and practicals will be delivered by Course Staff below:

    Prof Dmitri Kavetski -
    Assoc Prof Andrew Metcalfe -
    Dr Exequiel Sepulveda -
    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. Use established statistical tools and methods to validate, process and analyse data

    2. Use Python code to manipulate and visualise data to report measurable outcomes

    3. Analyse data to explain and/or predict system behaviour

    4. Apply logical thinking to problem solving in technical, social and teamwork contexts

    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)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    1, 2, 3, 4
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
    1, 3, 4
    Teamwork and communication skills
    • developed from, with, and via the SGDE
    • honed through assessment and practice throughout the program of studies
    • encouraged and valued in all aspects of learning
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    1, 2, 3, 4
    Intercultural and ethical competency
    • adept at operating in other cultures
    • comfortable with different nationalities and social contexts
    • able to determine and contribute to desirable social outcomes
    • demonstrated by study abroad or with an understanding of indigenous knowledges
    Self-awareness and emotional intelligence
    • a capacity for self-reflection and a willingness to engage in self-appraisal
    • open to objective and constructive feedback from supervisors and peers
    • able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
  • Learning Resources
    Required Resources
    Lecture material (slides and audiovisual recordings) and all other course material will be available on MyUni. If a lecture is
    missed, it is essential to view the recording prior to the next scheduled contact time

    Online Learning
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The course is delivered in 5 modes

    1. Lectures - 3 hrs/week : presentation of new material, emphasis on motivation, theiry and worked examples

    2. Tutorials/practicals - 2 hrs/week : opportunity for students to work through practical examples (including programming) and get feedback

    3. Assignments - ~ 9 hrs x 4 : assessable items to be undertaken individually, except Assignment 4 has teamwork elements (but report individual)

    4. Final Exam - 3hrs : assessable item based on entire course content, emphasis on general understanding

    5. Independent study - ~ 4 hrs/week : in addition to the formal activities above, students are expected to invest substantial effort into learning and practicing course material in their own time


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

    The estimated workload for the course components is listed below:

    1. Lectures - 3 hrs/week

    2. Tutorials/practicals - 2 hrs/week

    3. Assignments - ~ 9 hrs x 4

    4. Final Exam

    5. Independent work - ~ 4 hrs/week

    This workload is based on University of Adelaide guidelines

    On average, a student in a 3-unit course will need to invest approx 156 hours over 13 weeks (12 hours/week) of total work (contact + non-contact time, including assessment tasks) to achieve a mark of "Credit".

    Higher marks, such as "Distinction" and "High Distinction", will require substantially more quality time and effort (Section 6.1).

    Learning Activities Summary
    The course consists of 4 modules:

    Module 1 - Weeks 1-3 : Intro to data analysis and statistics

    Module 2 - Weeks 4-7 : Python programming and basic computing literacy

    Module 3 - Weeks 8-10 : Probababiity theory and modelling

    Module 4 - Weeks 11-12 : Mini-project applying knowledge from Modules 1-3 to a realistic data analysis problem
    Small Group Discovery Experience
    Assignment 4 represents a "Small Group Discovery Experience" where students can explore realistic practical problems and hone their abilities in applying the technical and programming knowledge taught earlier in the course
  • 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 item Description Weight Work mode Due date Hurdle Learning Outcomes
    Assignment 1 Exploratory data analysis 10% Individual W3 N 1*, 2, 4
    Assignment 2 Python / Visualisation 10% Individual W7 N 2*, 3
    Assignment 3 Probability and prediction 15% Individual W10 N 1*, 2, 3*, 4
    Assignment 4 Mini project on data analytics using Python 25% Mixed: some groupwork but submission individual W12 N 1, 2*, 3, 4*
    Final exam 40% Individual (3hrs+10min) Exam period Y (40%)

    Late submission penalty: 20% per day. Submissions will not be accepted more than 2 days late.
    Assessment Detail

    No information currently available.


    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

    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 ( 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

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