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.

Students who have no previous experience in Python are required to take an online pre-sessional Python course from the Digital Skills Lab.

Students should have taken, or be taking concurrently, a first course in statistics such as Elementary Statistical Theory (ST102), Elementary Statistical Theory I (ST109) or Quantitative Methods (Statistics) (ST107)

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 the formulation of a data science problem, going through collection, 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, 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 the manipulation and visualisation of data. This will include the 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.

20 hours of lectures and 15 hours of seminars in the WT.

This course will be delivered through a combination of classes and lectures totalling a minimum of 35 hours in Winter Term. This course does not include reading weeks.

Students are required to install Python and other required software on their own laptops and use their own laptops in the lectures and classes.

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

Weekly exercises will be given, using Python, various libraries and tools introduced in the course 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. Easley, David, and Jon Kleinberg. Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge university press, 2010
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. NetworkX: Software for complex networks, https://networkx.org

Coursework (30%) in the WT.
Project (70%) in the WT and ST.

Students are required to submit the solutions to 2 sets of exercises using mainly Python (accounting for 30% of the final assessment) during WT, and submit a group project at the beginning of ST. The project consists of applying data manipulation and visualisation methods to some dataset(s).