Outline

Overview

This OLE will provide the fundamentals of computer programming with a focus on developing skills for data ingesting, quality/format validation, format conversion, and summarization. It will teach these skills in Python, an easy-to-learn yet powerful, general-purpose scripting language used widely in industry and academia, especially for data science projects.

Unit details and rules

Academic unit	Computer Science
Credit points	2
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	INFO1903 OR DATA1002 OR DATA1902
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Farnaz Farid, farnaz.farid@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Programming tasks Programming tasks based on topic modules.	20%	Multiple weeks	n/a
Outcomes assessed:
Assignment	Assignment Python programing based on modules 1-9	20%	Week 12	n/a
Outcomes assessed:
Assignment	Timed Assignment Coding questions via Grok.	60%	Week 13 Due date: 04 Jun 2021 at 13:00 Closing date: 04 Jun 2021	1hr
Outcomes assessed:

Assessment summary

As part of each mandatory 10 topic modules in the GrokLearning platform, there will be challenges, where the student will be given a task for which they must produce a Python program using the knowledge that was recently explained. The program will be graded automatically, by being run against several input datasets (only one of these datasets is visible to the student before submission), and the output will be compared to what is required. Each module has its own due date.

There is one assignment that integrates all the knowledge from modules 1-9. It requires producing a Python program to meet requirements described in the assignment; the assignment will be graded automatically, by the students’ submission being run against several input datasets (only one of these datasets is visible to the student before submission), and the output will be compared to what is required.

The final assessment which is a timed assignment will be conducted via Grok, with tasks requiring students to produce Python programs. These are marked automatically (and also by hand if the automatic tests are not passed.)

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

Minimum Pass Requirements

It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

All GrokLeaning tasks (weekly modules and assignments) have due dates which cannot be extended. Late work cannot be accepted, due to immediate release of sample solutions and marking inputs. If Special consideration is appropriate, mark adjustment will be applied, replacing the affected work by the tasks from the assessment set.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Multiple weeks	The students have access to tutors who will be on duty for one or more scheduled sessions each week, either in person or through online communication.	One-to-one tuition (1 hr)
Week 01	Python as a calculator	Individual study (3 hr)
Week 02	Calculate with built-in functions	Individual study (3 hr)
Week 03	Text processing and data cleaning	Individual study (3 hr)
Week 04	Summarize a dataset	Individual study (3 hr)
Week 06	Advanced text processing 1	Individual study (3 hr)
Week 07	Advanced text processing 2	Individual study (3 hr)
Week 08	Bucketing and pivoting numeric data	Individual study (3 hr)
Week 09	Bucketing and pivoting numeric data 2	Individual study (3 hr)
Week 10	The CSV module and pandas	Individual study (3 hr)
Week 12	Write your own functions	Individual study (3 hr)
Week 13	Consolidation and looking ahead (Optional)	Individual study (3 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 2 credit point unit, this equates to roughly 40-50 hours of student effort in total.

Required readings

Material will be at accessed via the Canvas site, from which students can directly access the GrokLearning web-based platform.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Ability to automate a computational process typical of data science activities, when given a clear account of the task, or of the algorithm to be applied. This will be done by writing Python programs with core techniques of imperative programming.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Changes have been made to this unit in response to last year's student feedback.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

OLET1307: Beginner Programming for Data Analysis

Semester 1, 2021 [Online] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links