Dr Christine Yuen

This course is compulsory on the BSc in Data Science. This course is available on the BSc in Actuarial Science. This course is available with permission as an outside option to students on other programmes where regulations permit and to General Course students.

This course has a limited number of places (it is capped). Students who have this course as a compulsory course are guaranteed a place. Places for all other students are allocated on a first come first served basis.

The course focuses on the fundamental principles of effective manipulation and visualisation of data. This will cover the key steps of a data analytics pipeline, starting with formulation of a data science problem, going through manipulation and visualisation of data, and, finally, creating actionable insights. The topics covered include methods for data cleaning and transformation, manipulation of data using tabular data structures, relational database models, structured query languages (e.g. SQL), processing of various human-readable data formats (e.g. JSON and XML), data visualisation methods for explanatory data analysis, using various statistical plots such as histograms and boxplots, data visualisation plots for time series data, multivariate data, dimensionality reduction methods for visualisation of high-dimensional data, graph data visualisation methods.

The course will cover basic concepts and principles and will enable students to gain hands-on experience in using Python programming for manipulation and visualisation of data. This will include use of standard modules and libraries such as NumPy, Pandas, matplotlib and Seaborn, and programming environments such as Jupyter notebook.

The course will use examples drawn from a wide range of applications such as those that arise in online services, social media, social networks, finance, and machine learning. The principles and methods learned will enable students to effectively derive insights from data and communicate results to end users.

This course will be delivered through a combination of classes, lectures and Q&A sessions totalling a minimum of 35 hours in Lent Term. This year, some of this teaching may be delivered through a combination of classes and flipped-lectures delivered as short online videos. This course includes a reading week in Week 6 of Lent Term.

Students are required to install Python on their own laptops and use their own laptops in the seminar sessions.

Students not having a laptop of their own, which can be used for the purpose of the course, will be offered to use personal computers available in seminar rooms.

Students will be expected to produce 7 exercises in the LT.

Weekly exercises will be given, using Python and various libraries to apply various data manipulation and visualisation methods to data.  

1. W. Mckinney, Python for Data Analysis, 2nd Edition, O’Reilly 2017
2. A. C. Muller and S. Guido, Introduction to Machine Learning with Python, O’Reilly, 2016
3. A. Geron, Hands-on Machine Learning with Scikit-Learn & TensorFlow, O’Reilly, 2017
4. R. Ramakrishnan and J. Gehrke, Database Management Systems, 3rd Edition, McGraw Hill, 2002

Additional Reading: 

1. NumPy, https://numpy.org/
2. Python Data Analysis Library, https://pandas.pydata.org/
3. Matplotlib, https://matplotlib.org
4. Seaborn: statistical data visualization https://seaborn.pydata.org
5. Sci-kit learn, Machine learning in Python, http://scikit-learn.org
6. NetworkX: Software for complex networks, https://networkx.github.io

Coursework (30%) and project (70%) in the LT.

Students are required to hand in solutions to 3 sets of exercises using Python each accounting for 10% of the final assessment, and hand in a report for an individual project (accounting for 70% of the final assessment). The project consists of applying data manipulation and visualisation methods to a particular dataset.

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.