ST115      Half Unit
Managing and Visualising Data

This information is for the 2025/26 session.

Course Convenor

Availability

Requisites

Additional requisites:

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 gathering data using API and web scraping; 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 exploratory and explanatory data analysis using various statistical plots such as histograms and boxplots, data visualisation plots for time series data, multivariate data, etc.
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.

15 hours of seminars and 20 hours of lectures in the Winter Term.

This course will be delivered through a combination of classes and lectures totalling a minimum of 35 hours in Winter Term.

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 4 exercises in the WT.

Essential Reading:

1. W. Mckinney, Python for Data Analysis, 3rd Edition, O’Reilly 2022
2. A. C. Muller and S. Guido, Introduction to Machine Learning with Python, O’Reilly, 2016
3. R. Ramakrishnan and J. Gehrke, Database Management Systems, 3rd Edition, McGraw Hill, 2002
4. A. Cairo, The truthful art: Data, charts, and maps for communication, New Riders, 2016

Additional Reading: 

1. NumPy, https://numpy.org
2. Pandas, https://pandas.pydata.org
3. Matplotlib, https://matplotlib.org
4. Seaborn: statistical data visualization, https://seaborn.pydata.org

Exam (40%), duration: 90 Minutes, reading time: 10 minutes in the Spring exam period

Project (50%) in Spring Term Week 1

Continuous assessment (10%)