COMP42315: Programming for Data Science

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• To provide knowledge of, and the ability to apply, popular Python software packages currently used in industry settings.
• To give students an understanding of how to programmatically gather, manipulate and process real-world data.
• To introduce students to the key concepts of data analysis and data visualisation.

• Programming in Python using popular packages such as Pandas, NumPy, SciPy and Matplotlib.
• Reading, writing and parsing files in different formats.
• Obtaining a data set through the use of web scraping.
• Data munging, cleaning and preparing a dataset for analysis and visualisation.

Subject-specific Knowledge:

• By the end of this module, students should:
• Understand advanced concepts of programming in Python.
• Have a critical appreciation of the main strengths and weaknesses of a range of Python packages and understand how to use them.
• Have a critical appreciation of how to acquire and clean datasets for analysis.
• Understand how to manipulate potentially large datasets in an efficient manner.

Subject-specific Skills:

• By the end of this module, students should:
• Be able to write computer programs in python using industry standard packages.
• Be able to select appropriate data structures for modelling various data science scenarios.
• Be able to select the appropriate algorithm and programming package for a given problem.
• Be able to write a computer program in python to collect or read data from available sources, and clean these datasets using the appropriate packages.

Key Skills:

• Effective written communication
• Planning, organising and time-management
• Problem solving and analysis

• This module will be delivered by the Department of Computer Science.
• Learning outputs are met through practical workshops, supported by online resources. The workshops consist of a combination of taught input, case studies, discussion and computing labs. Online resources will typically consist of directed reading and a programming environment with example code.
• The summative assessment requires the development of a Jupyter notebook that will demonstrate the design, implementation, analysis and testing of Python code to analyse and visualise real-world data science problems. This might consist of programming source code files (Jupyter notebook or Python script) and/or a report of 1500 words max.
• Teaching on this module will be delivered in a blended mode with specific elements delivered online where student numbers determine online teaching as the most effective method.

The formative assessment consists of classroom-based exercises on specific computer science topics, relevant to the learning outcomes of the modules. Oral feedback will be given on a group and/or individual basis as appropriate.

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