ITEC102 - Python Fundamentals For Data Science

Year

Credit points

Campus offering

Prerequisites

Nil

Unit rationale, description and aim

Data is deemed as the world’s ‘new oil’ while data science is a new inter-disciplinary science of data that employs scientific methods, algorithms, tools and systems for uncovering insights, knowledge and value from massive data generated in different domains. Python, a general-purpose programming language, has gradually become the ‘engine’ of data and data science. In particular, many data scientists use Python because it provides a wealth of data science tools and libraries.

 This unit will cover fundamental elements of python programming language and its comprehensive use in the context of data science. This includes Python language basics, data structures, functions, files, tools and various python data science libraries for data processing, analysis and visualisation. Data ethics and elementary statistics and probability in data science will also be introduced.

The aim of the unit is for students to learn how Python can be used for building data science solutions.

Learning outcomes

To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.

Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.

Explore the graduate capabilities.

On successful completion of this unit, students should be able to:

LO1 - Demonstrate an understanding of fundamental python programming language and data science concepts and tools (GA5, GA8)

LO2 - Demonstrate the use of common python data science libraries and tools for data collection, cleaning, and wrangling (GA5, GA10)

LO3 - Experiment with data processing, analysis and visualisation techniques and tools to solve real-world data science problems (GA4, GA5)

LO4 - Evaluate data science ethical issues as they impact on human dignity and privacy (GA3, GA5)

Graduate attributes

GA3 - apply ethical perspectives in informed decision making

GA4 - think critically and reflectively 

GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession 

GA8 - locate, organise, analyse, synthesise and evaluate information 

GA10 - utilise information and communication and other relevant technologies effectively.

Content

Topics will include:

• Data science and python introduction
• Data science environment setup: IPython, Jupyter notebooks and IDEs
• Python language syntax, semantics and scalar types
• Python language control flow and basic data structures and sequences
• Python language functions and files
• Numpy and Pandas
• Data processing on data loading, storage, and file formats
• Data processing on data cleaning and preparation
• Data processing on data wrangling: join, combine, and reshape
• Data processing on data aggregation and group operations
• Data plotting, visualisation and exploratory data analysis
• Introduction to statistics and probability in data science
• Data ethics and potential adverse impacts

Learning and teaching strategy and rationale

This unit is offered in different modes. These are: “Attendance” mode, “Blended” mode and “Online” mode. This unit is offered in three modes to cater for the learning needs and preferences of a range of participants and maximise effective participation for isolated and/or marginalised groups.

Attendance Mode

In a weekly attendance mode, students will require face-to-face attendance in specific physical location/s. Students will have face-to-face interactions with lecturer(s) or lab demonstrators to further their achievement of the learning outcomes. This unit is structured with required upfront preparation before workshops, most students report that they spend an average of one hour preparing before the workshop and one or more hours after the workshop practicing and revising what was covered. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for you to prepare and revise.

Blended Mode

In a blended mode, students will require face-to-face attendance in blocks of time determined by the School. Students will have face-to-face interactions with lecturer(s) to further their achievement of the learning outcomes. This unit is structured with required upfront preparation before workshops. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for you to prepare and revise.

Online Mode

This unit uses an active learning approach to support students in the exploration of the essential knowledge associated with working with technology. Students can explore the essential knowledge underpinning technological advances and develop knowledge in a series of online interactive lessons and modules. Students are given the opportunity to attend facilitated synchronous online seminar classes with other students and participate in the construction and synthesis of knowledge, while developing their knowledge of working with technology. Students are required to participate in a series of online interactive workshops which include activities, knowledge checks, discussion and interactive sessions. This approach allows flexibility for students and facilitates learning and participation for students with a preference for virtual learning.

Students should anticipate undertaking 150 hours of study for this unit, including class attendance, readings, online forum participation and assessment preparation.

Assessment strategy and rationale

A range of assessment procedures will be used to meet the unit learning outcomes and develop graduate attributes consistent with University assessment requirements. The first assessment consists of small to medium sized python setup and programming tasks. The purpose is to assess students’ fundamental Python programming and data science skills for problem solving. The second assessment consists of data preparation tasks using key Python data science ecosystem/libraries. The purpose is to assess students’ use of Python data science libraries NumPy and Pandas and other related tools for collecting, cleaning and wrangling various types of data. The final assessment is a more comprehensive assignment involving data processing, analysis and visualisation. The purpose is to assess students’ Python programming and data science techniques from data processing to data visualisation on real-world datasets with consideration of data ethics. There are fortnightly lab sessions associated with the assessments including assessable lab participation/engagement.

The assessments for this unit are designed to demonstrate the achievement of each learning outcome. To pass this unit, students are required to:

• attempt all three assessment items
• obtain an overall mark of at least 50%

Overview of assessments

Representative texts and references

Wes McKinney, 2018. Python for Data Analysis, 2nd Edition O'Reilly Media, Inc.

Joel Grus, 2019. Data Science from Scratch, 2nd Edition, O'Reilly Media, Inc.

Eric Matthes, 2019. Python Crash Course: A Hands-On, Project-Based Introduction to Programming, 2nd Edition No Starch Press.

Luca Massaron and John Paul Mueller, 2019. Python for Data Science, 2nd Edition, For Dummies.

Peter Bruce et al, 2020. Practical Statistics for Data Scientists, 2nd Edition O'Reilly Media, Inc.

Allen B. Downey, 2015. Think Stats, 2nd Edition, O'Reilly Media, Inc.