Year
2021Credit points
10Campus offering
No unit offerings are currently available for this unitPrerequisites
ITEC102 Python Fundamentals For Data Science
Unit rationale, description and aim
Data science is an inter-disciplinary area that employs scientific methods, algorithms, tools and systems for extracting insights, knowledge and value from data. Machine learning, as a core part of data science and data analytics, and a subfield of artificial intelligence, is the scientific study of algorithms and mathematical models that computer systems use to make decisions or predictions. Machine learning algorithms and models are widely used in human’s digital life such as email client, search engine, social media, virtual personal assistant and recommendation system, although machine bias is an important ethical concern of which many people are unaware. Python is one of the most popular programming languages with comprehensive libraries and tools for putting data science and machine learning into practice in an efficient manner.
This unit will cover fundamental concepts and theories of data science and machine learning with focus on their practical use and implementations. The issue of machine bias in machine learning and how it may have an adverse impact on the common good will be examined. The aim of the unit is to learn both theoretical and practical data science and machine learning techniques to build real-world data science and machine learning 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 comprehensive knowledge with data science libraries and tools for data processing and analysis (GA5, GA10)
LO2 - Demonstrate data science and machine learning preparation skills, via key techniques learnt and the use of relevant tools (GA5, GA8)
LO3 - Implement a data science and machine learning application with an appropriate choice of data science and machine learning techniques (GA4, GA5)
LO4 - Explain the issue of machine bias and how it may affect the common good (GA2, GA5)
Graduate attributes
GA2 - recognise their responsibility to the common good, the environment and society
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:
- Overview of data science and its implementation life cycle and tools
- Recap of data processing concepts and techniques
- Exploratory data analysis in data science
- Machine learning (ML) introduction
- ML projects and basic linear algebra
- Basic matrix analysis and SVD, PCA
- Basic classification and evaluation with ROC curves
- Probability Theory and Naïve Bayesian Classifier
- Regression (linear, polynomial), overfitting and regularization, Bayesian regression
- Clustering: k-means and mixture of Gaussians
- Better evaluation with k-fold cross validation and finetune model with grid search
- Machine bias in the real world and its impact on the common good
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 item consists of simple data and machine learning practical tasks. The purpose is to assess students’ practical data science and machine learning skills of Python data science and machine learning libraries and tools. The second assessment is a more specific image data exploration and machine learning preparation task that requires fundamental knowledge of data science and machine learning. The purpose is to assess students’ understanding and practical skills in data preparation for machine learning algorithms and models. The final assessment is to conduct experiments with one machine learning algorithm e.g. classification. The purpose is to assess students’ machine learning practical skills and techniques with consideration of machine bias, building on the machine learning preparation task. There are 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
| Brief Description of Kind and Purpose of Assessment Tasks | Weighting | Learning Outcomes | Graduate Attributes |
|---|---|---|---|
Assessment Task 1: Lab practical The first assessment consists of practicing simple Python data science and machine learning libraries. The assessment requires students to demonstrate their understanding and use of Python data science and machine learning libraries and tools. Submission Type: Individual Assessment Method: Content knowledge coding tasks Artefact: Code | 30% | LO1 | GA5, GA10 |
Assessment Task 2: Image data exploration tasks The second assessment consists of tasks to do image data exploration requires fundamental knowledge of data science and machine learning. The purpose is to assess students’ understanding and practical skills in the process of data preparation for machine learning models. Submission Type: Individual Assessment Method: Conceptual knowledge coding tasks Artefact: Code | 30% | LO2 | GA5, GA8 |
Assessment Task 3: Machine learning assignment The final assessment is a group-based machine learning assignment focusing on classification. The assessment builds on the data prepared by the previous assessment and conducts experiments with machine learning models with consideration of machine bias. Submission Type: Individual Assessment Method: Applying knowledge coding tasks Artefact: Code | 40% | LO3, LO4 | GA2, GA4, GA5 |
Representative texts and references
Aurélien Géron, 2019. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, O'Reilly Media, Inc.
Christopher Bishop, 2006, Pattern Recognition and Machine Learning, Springer-Verlag New York.
EMC Education Services (Editor), 2015. Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley.
Peter Bruce et al, 2020. Practical Statistics for Data Scientists, 2nd Edition O'Reilly Media, Inc.
Hadrien Jean, 2020. Essential Math for Data Science, O'Reilly Media, Inc.
Luca Massaron and John Paul Mueller, 2019. Python for Data Science, 2nd Edition, For Dummies.
Gilbert Strang, 2016. Introduction to Linear Algebra, fifth edition, http://math.mit.edu/~gs/linearalgebra/
Kavin P. Murphy, 2012, Machine Learning: A Probabilistic Perspective, MIT Press Academic