ITEC610 Introduction to Data Science with Python
150 hours over a twelve-week semester or equivalent study period
Unit rationale, description and aim
Machine learning is the process of teaching a machine to learn from datasets for a variety of tasks. Machine learning models and algorithms are widely used in human’s digital life such as email client, search engine, social media, virtual personal assistant, healthcare and recommendation system. This unit provides a practical and technical introduction to machine learning models and algorithms. Topics to be covered include problems of discovering patterns in the data, classification, regression, feature extraction and data visualisation. Also covered are hands-on implementation and usage of various types of machine learning techniques via Python Scikit-Learn to solve real-world problems such as in digital health. This unit will also consider the issue of machine bias and how it may have an adverse impact on the common good.
On successful completion of this unit, students should be able to:
LO1 - Demonstrate comprehensive knowledge of using data science libraries and tools for data processing and analysis (GA5, GA10)
LO2 - Appraise the use of fundamental data science and machine learning theories, key techniques and relevant tools for machine learning preparation (GA5, GA8)
LO3 - Develop an end-to-end data science and machine learning solution to real-world problems e.g. in digital health with appropriate choices of data science and machine learning techniques (GA4, GA5, GA7)
LO4 - Examine the issue of machine bias and how it may affect the common good (GA2, GA5)
GA2 - Recognise their responsibility to the common good, the environment and society
GA4 - Rethink critically and reflectively
GA5 - Demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession
GA7 - Work both autonomously and collaboratively
GA8 - Locate, organise, analyse, synthesise and evaluate information
GA10 - Utilise information and communication and other relevant technologies effectively.
Topics will include:
- Overview of data science and its implementation life cycle and tools
- Recap of data processing concepts including data quality and data operations such as cleaning, integration, reduction and transformation.
- Theory and practice of essential statistics 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 will be delivered in a multimode over a twelve-week semester or equivalent study period. Students will have access to all primary learning materials online through LEO, along with formative and summative assessments, all of which will be available online, to provide a learning experience beyond the classroom. While there are no formal classroom lectures for this unit, students will be required to attend weekly two-hour workshop and fortnightly one-hour lab for the achievement of the unit learning outcomes. Workshops facilitate learning by theory comprehension and problem solving while lab sessions focus on hands on practices, which in combination is particularly effective for learning information technology skills.
Students should anticipate undertaking 150 hours of study for this unit, including class attendance, readings, online forum participation and assessment preparation.
This unit uses an active learning approach to support students in the exploration of knowledge essential to the discipline. Students are provided with choice and variety in how they learn. Students are encouraged to contribute to asynchronous weekly discussions. Active learning opportunities provide students with opportunities to practice and apply their learning in situations similar to their future professions. Activities encourage students to bring their own examples to demonstrate understanding, application and engage constructively with their peers. Students receive regular and timely feedback on their learning, which includes information on their progress.
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 simple programming tasks to practice data processing, analysis practical and machine learning skills. The purpose is to assess students’ practical skills of using Python data science and machine learning libraries and tools for data processing and analysis. The second assessment is a more specific image data exploration and machine learning preparation task that covers fundamental knowledge of data science and machine learning. The purpose is to assess students’ understanding and skills in data preparation for machine learning preparation. The final assessment is a group project to do experiments with machine learning models and algorithms. The purpose is to assess students’ knowledge and skills of applying key machine learning algorithms to solve real-world problems e.g. in digital health with consideration of machine bias, continuing from the machine learning preparation task. 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 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 item consists of practicing simple Python data science and machine learning libraries and tools. The assessment requires students to demonstrate their understanding and use of Python data science and machine learning libraries and tools for small sized tasks.
Submission Type: Individual
Assessment Method: Content knowledge coding task
Assessment Task 2: Data exploration tasks preparing for Machine learning project
The second assessment is to prepare specific image data for machine learning models and algorithms exploration. The purpose is to assess students’ understanding and skills in using Python data science and machine learning packages in data exploration.
Submission Type: Individual
Assessment Method: Conceptual knowledge coding tasks
Assessment Task 3: Machine learning project
The final assessment is a group-based machine learning assignment focusing on machine learning models and algorithms to solve real-world problems such as in digital health. The assessment requires students to develop an end-to-end machine learning project with key machine learning algorithms and consideration of machine bias.
Submission Type: Group
Assessment Method: Applying knowledge project task
Artefact: Code and Report
GA2, GA4, GA5, GA7
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
Vikas Kumar, 2018. Healthcare Analytics Made Simple: Techniques in healthcare computing using machine learning and Python, Packt Pulishing limited.
M. Ghassemi, T. Naumann, P. Schulam, A. L. Beam, I. Y. Chen, and R. Ranganath, 2020. A Review of Challenges and Opportunities in Machine Learning for Health, AMIA Joint Summits on Translational Science proceedings, vol. 2020, pp. 191-200.
C. Verdonk, F. Verdonk, and G. Dreyfus, 2020, How machine learning could be used in clinical practice during an epidemic, Critical Care, vol. 24, no. 1, p. 265.
Kubben et al (Eds), 2019. Fundamentals of Clinical Data Science, Springer – open access freely available from https://www.springer.com/gp/book/9783319997124