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 Outcome Number||Learning Outcome Description|
|LO1||Demonstrate comprehensive knowledge with data science libraries and tools for data processing and analysis|
|LO2||Demonstrate data science and machine learning preparation skills, via key techniques learnt and the use of relevant tools|
|LO3||Implement a data science and machine learning application with an appropriate choice of data science and machine learning techniques|
|LO4||Explain the issue of machine bias and how it may affect the common good|
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, dimensional reduction, and SVD, PCA
- Basic classification and evaluation metrics
- Regression (linear, polynomial), overfitting and regularization
- Clustering: k-means and mixture of Gaussians
- Better evaluation with k-fold cross validation and finetune model with grid search
- Neural networks and deep learning
- 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.
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.
In a blended mode, students will require face-to-face attendance in blocks of time determined by the School (weeks 1-3 and weeks 10-12) and online asynchronous sessions for the other blocks of time (weeks 4-10) during the semester. Students will have face-to-face interactions with lecturer(s) and online asynchronous engagement 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.
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.
Students should anticipate undertaking 150 hours of study for this unit, including class attendance, readings, online engagement and 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’ online engagement and 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 obtain an overall mark of at least 50%.
Overview of assessments
|Brief Description of Kind and Purpose of Assessment Tasks||Weighting||Learning Outcomes|
Assessment Task 1: Practical programming
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
Assessment Task 2: Image data exploration with online engagement tasks
The second assessment consists of tasks to do online forum participation and image data exploration which requires fundamental knowledge of data science and machine learning. The purpose is to assess students’ online engagement and understanding and practical skills in the process of data preparation for machine learning models. There are 6 weeks sub tasks related to AT2 and students are required to participate weekly sub-tasks forum discussions. Students need to incorporate weekly forum participation summary and prepare AT2 submission
Submission Type: Individual
Assessment Method: Conceptual knowledge coding tasks
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
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