Unit Outline OLEO1602 and OLET1603 PDF

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Unit Outline OLEO1602 and OLET1603 Analysing and plotting data: Python 2019 WELCOME & DESCRIPTION Welcome to the OLE Analysing and plotting data: Python. This unit of study can be taken as a 0CP “taster” (OLEO1602) or as 2CP unit (OLET1603). For OLEO1602 you will have only access to Module 1 and 2, while for OLET1603 you will have access to all the material and the assessments. We think you made a great choice in selecting this as part of your degree. There is an increasing demand for data analysis due to the ever increasing amount of digital data that is being collected, and the rapid increase in technology to support this data. Therefore, the analysis of large amounts of data is seen as one of the most important graduate skills of today in different fields, from science to business. Scripting computer languages such as Python play a vital role in this process.

TEACHING STAFF: Unit coordinator A/Prof Willem Vervoort: [email protected] 02 8627 1054 Staff TBA

UNIT DESCRIPTION OLET1603 Analysing and plotting data: Python This unit is an introduction into coding using the popular script language Python for students who do not receive these skills in junior units of study. Through working through examples in on-line exercises and regular assessment and support hours, the students will develop hands-on skills. In particular the unit will teach analysis of text based data, numerical data and categorical data, constructing plots and developing summaries.

LEARNING OUTCOMES By the end of this unit students should develop confidence in reading and writing simple programs in Python. They should be able to perform calculations and do basic graphing in the language. More specifically they should be able to: -

Read in and write out data into Python in common formats (csv, txt) from a directory or from an internet source; 1

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Inspect the data in tabular form in Python and do element, column and row manipulations using text and numerical data; Produce statistical summaries of numerical data, text based data and subsets of data; Demonstrate a basic understanding of packages and libraries and load and use some common libraries in Python. Use basic graphics to display the data using x-y plots, bar plots and histograms, and manipulate axis, colours, lines, points and labels; and Analyse x-y data using a simple linear regression model and add the results to plots.

TIME TABLE & LOCATION This unit is officially timetabled in the Intensive August semester 2. For the students enrolled in OLET1603, there is an opportunity for one (1) face-to-face contact hour in the week for this unit. -

Monday 1 – 2 pm: Biomedical building Australian Technology Park rm 225 (1 Central Ave Eveleigh) Tuesday 1 – 2 pm: Biomedical building Australian Technology Park rm 225 (1 Central Ave Eveleigh) Wednesday 12:00 – 1 pm: Biomedical building Australian Technology Park rm 225 (1 Central Ave Eveleigh) Thursday 1 – 2 pm: Biomedical building Australian Technology Park rm 225 (1 Central Ave Eveleigh) Friday 1 – 2 pm: Biomedical building Australian Technology Park rm 225 (1 Central Ave Eveleigh)

Australian Technology Park, Biomedical building location (about 20 minute walk from City rd)

Please stick to your timetabled contact hour to avoid overcrowding of the consultation hours. 2

UNIT OUTLINE & CONTENT The unit consists of a general introduction followed by 4 modules M ODULE

CONTENT

General introduction Module 1

How to use Jupyter Notebooks and a short introduction into Python Introduction into dataframes and how to read data into Python Webscraping and analysing data from webpages Plotting the habitable range of exoplanets using Python Analysing a twitter feed using Python

Module 2 Module 3 Module 4

OLEO1602

OLET1603

yes

yes

yes

yes

no

yes

no

yes

no

yes

ASSESSMENT D ESCRIPTION

DATE

WEIGHT

On-line completion Quizzes at the end of the module

Week 1, 2, 3 12.5% (each) and 4

OLET1603

In class and in person Exam

Exam Period

OLET1603

50%

U NIT

Late penalties for assessment tasks are 10% of the assessment task mark per day. Non submission of an assessment task will result in a mark of 0. Valid applications of special consideration will be evaluated and taken into account. Please put in special consideration applications for misfortune.

WORKLOAD AND MINIMUM REQUIREMENTS Minimum student workload requirement for active learning in this unit is 6 hours per week for 4 weeks. These learning activities may include creating notes in your own words, visiting lecturers or tutors in consultation hours, discussing your work with friends to learn together, revising, researching in the library or online, reading, completing practice questions, participating in online discussion forums, preparing for tutorials and lectures by pre-reading, completing assessments.

FEEDBACK All quizzes will provide basic feedback on the answers provided by students. Further feedback can be requested during the consultation hours.

ACADEMIC HONESTY AND PLAGIARISM Commencing students should complete the academic honesty module available via Blackboard before their first assessment submission. Students should refer to the University’s policies on academic dishonesty and plagiarism (sydney.edu.au/policy), submitted for assessment. It is clearly unfair for students to submit work for assessment that dishonestly represents the work of others as their own and gain marks and degrees, which are not based on their own efforts and abilities. Deliberate breaches of academic honesty constitute academic misconduct. These breaches include: plagiarism, fabrication of data, recycling previously submitted material, engaging someone else to complete an assessment on one’s behalf and misconduct during supervised assessments.

3

The penalties for academic misconduct may include: a mark of zero on the assessment; a fail grade in the unit of study, additional assessment (including an unseen exam), and reference of the matter to the University Registrar.

STUDENT EVALUATION AND FEEDBACK We have taken into account the feedback of the first year (which was generally good) and slowed down the delivery of the material.

UNIVERSITY POLICIES AND SERVICES All students must comply with and follow all Faculty and University policies and procedures. Faculty policies are contained in the Administration Manual for Students (e.g. special consideration, appeals, late submission, feedback mechanisms, academic honesty and plagiarism).

University policies at sydney.edu.au/policy includes: academic dishonesty , plagiarism. Assistance is available from the University’s Student Centre sydney.edu.au/studentcentre. The code of conduct is an important policy which outlines the University’s expectations about treating all staff employees and students with respect, dignity, impartiality, courtesy and sensitivity and refrain from acts of discrimination, harassment or bullying.

OVERVIEW OF MODULES The below table gives a more detailed overview modules and assessment tasks. Week

Topics

Assessment (OLET1603)

OLEO1602

2

General introduction and Module 1: Dataframes

3

Module 1: Dataframes

Quiz Module 1

Quiz Module 1 (selftest)

4

Module 2: Webscraping

Quiz Module 2

5

Module 3: Plotting exoplanet data

Quiz Module 3

6

Module 4: Analysing Twitter feed data

Quiz Module 4

4...