Unit Outline - CITS2402 Introduction to Data Science - Face to face Blended learning PDF

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27/07/2020

Unit Outline - CITS2402 Introduction to Data Science - Face to face / Blended learning

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CITS2402 Introduction to Data Science - Face to face / Blended learning Unit Information Unit Code

CITS2402

Title

Introduction to Data Science - Face to face / Blended learning

Level

2

Faculty

Engineering and Mathematical Sciences

School

Physics, Mathematics and Computing

Unit Coordinator

Cara MacNish

Credit Points

6 points

Academic Information Content

Data science is a booming field that builds on the recent advancement of computational treatment of data. Unprecedented amount of data are ubiquitously and continuously generated from almost all facets of our modern society, which calls for professionally trained data scientists to turn raw data of various formats into valuable corporate intelligence. This unit will focus on the overall lifecycle of a data science project, introducing appropriate techniques and tools for key data science stages. Starting from data extraction, integration, cleansing, transformation and representation for tabular data and time series data, the unit will introduce practical tools and packages that step through the data science lifecycle of exploratory data visualisation, statistical modelling as well as basic machine learning techniques such as clustering and classification. Critical assessment of the data analytics outcome will be communicated back to the stake holder through an integrated reporting environment.

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Unit Outline - CITS2402 Introduction to Data Science - Face to face / Blended learning

Unit

How outcome will

Learning Outcomes

# Outcome

be assessed

1 explain the work flow and key components of a data science project

midterm test; exam

2 use appropriate tools for data processing, data modelling and evaluations

midterm test; exam

3 identify business questions that can be answered for a given dataset

project; exam

4 apply appropriate visualisation methods for different types of data, including

midterm test;

categorical, numerical and time series data

project; exam

5 critically assess the application and outcome of Data Science workflow to a business project; exam problem Indicative

# Assessment

Assessment

Indicative weighting Failed component

1 midterm test 20%

0

2 project

30%

0

3 exam

50%

0

The indicative assessment structure provides an overview of how this unit is typically assessed. In order to fit teaching requirements, this may change slightly from year to year. Restrictions to face-to-face learning associated with COVID-19 may result in variation to the Indicative Assessment structure. The Required Assessments table below shows how the unit is being assessed in this specific teaching period. Required

Unit grades are determined from final unit marks in accordance with the UWA Policy

Assessments (http://www.governance.uwa.edu.au/procedures/policies/policies-and-procedures? method=document&id=up15/5) on Assessment

Contact Details Unit Coordinator Contact Information Name

Cara MacNish

Email

[email protected]

Phone number Room number or location Consultation

https://secure.csse.uwa.edu.au/run/help2402

information

Unit Details CITS2402 - Introduction to Data Science - SEM-2-2020 - 2020 - Face to face / Blended learning

Unit Information

https://lms.uwa.edu.au/bbcswebdav/institution/Unit_Outlines_2020/CITS2402_SEM-2_2020/CITS2402_SEM-2_2020_UnitOutline.html

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Unit Outline - CITS2402 Introduction to Data Science - Face to face / Blended learning

This is a "hands-on" unit in which we will

data science. The unit will be based on a series of case studies using real-world data,

in which skills are developed to collect, inspect, clean, analyse, interpret and present data - the skills that you will need to work as a Data Scientist. It is vital that you understand the lectures and keep up to date with the lab work, and seek help as needed.

Lecture Capture System In accordance with UWA’s policy on lecture capture all lectures* in enabled venues will be recorded and made available to stream and download by enrolled students via the Lecture Capture System (LCS) for the duration of the unit (including Supplementary and Deferred Examinations period). * Please note that in some limited circumstances lectures may not be recorded due to: ethical and/or security related issues; the use of commercially sensitive material; the use of third-party copyright teaching material; or culturally sensitive content. Students will be advised, in advance, when this applies.

Learning And Teaching Strategy The unit will be taught online this semester.

Unit Structure Workshop-style lectures. 2 per week. May be broken into sections for file size considerations, ease of use, and to allow students to work examples in the case studies. Labs 2 hrs supervised per week. You may attend labs other than the one you are enrolled in if numbers permit. Labs will require additional time to complete outside of lab hours. Consultation 1 per week. Additional sessions may be scheduled as needed. Students are anticipated to spend 6 hours per week on study of lecture/workshop material and completion of lab and assignment work outside of scheduled hours.

Required Assessments Item # 1

2 3

Assessment Item

Weighting

Deadline

Submission Procedure

Unit Learning Outcome

Laboratory work/assignment

35

0

Collected via CoCalc

1,2,3,4,5

Mid-semester test

15

0

TBC

1,2,3,4,5

Exam

50

0

Examplify (TBC)

1,2,3,4,5

Unit Schedule #

Date Starting

Topic

Preparation

Assessment

https://lms.uwa.edu.au/bbcswebdav/institution/Unit_Outlines_2020/CITS2402_SEM-2_2020/CITS2402_SEM-2_2020_UnitOutline.html

Notes

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Unit Outline - CITS2402 Introduction to Data Science - Face to face / Blended learning

Participation Research shows that the more actively people engage in learning, the more they learn!

Referencing Requirements Students are encouraged to discuss concepts with teaching staff and other students at the conceptual level as part of the learning process. However, any work submitted for grading MUST be the student's (or students' in the case of a joint project) own work (that is, written only by the student).

Texts Recommended reading: * Python Data Science Handbook, Jake VanderPlas, O'Reilly, 2016. Free on-line version: https://jakevdp.github.io/PythonDataScienceHandbook/ (https://jakevdp.github.io/PythonDataScienceHandbook/) * Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Python, 2nd Edition, Wes McKinney, O'Reilly, 2017. * An Introduction to Statistical Learning (with Applications in R), Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, Springer, 2017. https://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf (https://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf)

Other The unit will primarily be conducted online through CoCalc. The official noticeboard for the unit is help2402.

Late submission of Assignments A penalty of 5 per cent of the total mark allocated for the assessment item is deducted per day for the first 7 days (including weekends and public holidays) aer which the assignment is not accepted. Each 24-hour block is recorded from the time the assignment is due. For example, if an assignment is late by three days and was given 45 out of a possible mark of 50, you would receive a mark of 37.5 out of 50 (a mark of 2.5 is deducted per day). If there are a number of tasks within an assessment item, the late penalty may be applied to the whole assessment item aer all tasks have been completed. If an assignment is graded Pass/Fail, failure to submit the assignment in time may result in the student not being permitted to take the final exam at the end of the semester or a teaching period and being unable to progress to the next level of the course or to graduate in the case where it is the final unit of the course;

Penalty for exceeding word limit Where a submitted assignment exceeds the word limit, a penalty of 1 per cent of the total mark allocated for the assessment task applies for each 1 per cent in excess of the word limit, or the marking ceases once the word limit is reached.

Academic conduct

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Academic Integrity is defined in the University Policy on Academic Conduct (http://www.governance.uwa.edu.au/procedures/policies/policies-and-procedures?policy=UP07%2F21) as "acting with the values of honesty, trust, fairness, respect and responsibility in learning, teaching and research". UWA expects the highest degree of academic conduct from all students. Penalties for breach of academic conduct vary according to seriousness of the case, and may include the requirement to do further work or repeat work; deduction of marks; the award of zero marks for the assessment; failure of one or more units; suspension from a course of study; exclusion from the University; non-conferral of a degree, diploma or other award to which the student would otherwise have been entitled. Students should familiarise themselves with the information and resources available regarding academic conduct and ethical scholarship (http://www.student.uwa.edu.au/learning/resources/ace/conduct) at UWA.

Special Considerations If your study has been adversely affected by illness or other significant circumstances outside of your control, you can apply for special consideration. Significant circumstances may include but are not limited to: serious illness or death of a member of the student's immediate family or household or of a close friend serious injury being a victim of a crime breakdown of relationship sudden loss of income or employment serious disruption to domestic arrangements The full policy and information on applying for special consideration is available at http://www.student.uwa.edu.au/course/exams/consideration (http://www.student.uwa.edu.au/course/exams/consideration)

Review and Appeal of Academic Decisions Relating to Students The full regulations governing appeal, and the process procedures are available on the Governance website (http://www.governance.uwa.edu.au/committees/appeals-committee/review-and-appeal-of-academic-decisions-relating-tostudents)

Compulsory online modules UWA has a wealth of resources available to support your student learning. These online modules are compulsory for many students: Academic Conduct Essentials (ACE) is compulsory for all new students about ethical scholarship and the expectations of correct academic conduct that UWA has of its students. Communication and Research Skills (CARS) is compulsory for all new undergraduate students which aims to assist students at UWA to develop communication and research skills in an academic context. Indigenous Study Essentials (INDG1000) is compulsory for all students completing a first year undergraduate course and introduces you to the shared learning space that UWA embodies. This learning space includes both western and Indigenous knowledge systems. More information about these units can be found on the Academic Conduct Essentials website (http://www.student.uwa.edu.au/learning/resources/ace)

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Useful Contacts Student Guild The Guild represents all students enrolled at UWA and can help you in a number of ways. For financial, academic or welfare support please email Guild Student Assist at [email protected] (mailto:[email protected]). For all other queries email [email protected] (mailto:[email protected]) or visit the UWA Guild website (http://www.uwastudentguild.com/).

University Library Visit one of UWA's six libraries or the University Library online (http://www.library.uwa.edu.au/) to access a wide range of services, study spaces, information and help, including: Access to textbooks and readings for your units Individual and group study spaces Wireless internet and your student account Research, referencing and copyright support Printing and scanning services

Student Experience There is a wealth of material provided by UWA Student Experience that can help you settle into university life and help with many other issues you may encounter as a university student. Contact details for departments within Student Experiences can be found on their website (http://www.student.uwa.edu.au/contact).

Study Smarter STUDYSmarter provides free academic advice, support and resources for all undergraduate and postgraduate students at UWA. We can help you to develop the writing, research, English language, maths and stats skills you need to excel in your university studies. Get personalised advice at WRITESmart Drop-in (http://www.student.uwa.edu.au/learning/studysmarter/writesmart_drop-in) and (ma+hs)Smart Drop-in (http://www.student.uwa.edu.au/learning/studysmarter/mathssmart/mahssmart-drop-ins), and find out about our extensive range of on-campus workshops, and online study guides, videos and tips on the STUDYSmarter website (http://www.studysmarter.uwa.edu.au).

Student Administration Student Admin deal with enrolments, fees and other vital services for students. They can be contacted via their website (http://www.student.uwa.edu.au/course)

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