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.

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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 - /directory/dmitri.kavetski
Assoc Prof Andrew Metcalfe - /directory/andrew.metcalfe
Dr Exequiel Sepulveda - /directory/exequiel.sepulvedaescobedo

Course Timetable

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

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	4
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	4
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	4

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

eBook from library

Python 3 for Absolute Beginners

Python Online Tutorials

Automate the Boring Stuff with Python:

YouTube videos

Other resources
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

Workload

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 成人大片 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

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

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

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 .

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
This section contains links to relevant assessment-related policies and guidelines - all university policies.
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鈥檚 disciplinary procedures.

The 成人大片 is committed to regular reviews of the courses and programs it offers to students. The 成人大片 therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.

Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

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