Dr Chengchun Shi

This course is compulsory on the MSc in Data Science and MSc in Health Data Science. This course is available on the MSc in Applied Social Data Science, MSc in Geographic Data Science, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (LSE and Fudan), MSc in Statistics (Financial Statistics) (Research), MSc in Statistics (Research), MSc in Statistics (Social Statistics) and MSc in Statistics (Social Statistics) (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.

This course has a limited number of places (it is controlled access) and demand is typically very high. Priority is given to students for whom the course is compulsory; as well as students in the Department of Statistics where the course is listed as an option in their programme regulations, students on the MSc in Applied Social Data Science, and students on the MSc in Geographic Data Science. Students from outside these programmes may not get a place.

The focus of the course is on the fundamental principles and best practices for data manipulation and visualisation. The course is based on using Python as the primary programming language and various software packages. 

The first five weeks will focus on data manipulation which covers the basic concepts such as data types and data models. Students learn how to create data model instances, load data into them, and manipulate and query data. The course will cover data structures for scientific computing and their manipulation through the Python package NumPy, and high-level data structures and functions for working with structured or tabular data through the Python package Pandas. We will cover the basic concepts of relational data models and SQL query language for creating and querying database tables.

The last five weeks focus on data visualisation starting with the exploratory data analysis using various statistical plots. We will explain visualisations used for evaluation of binary classifiers such as receiver operating curve plots and precision recall plots. We will explain the principles of some dimensionality reduction methods used for visualisation of high-dimensional data points, starting with classical methods such as multidimensional scaling to more recent methods such as stochastic neighbour embedding. We will discuss the basic principles of graph data visualisation methods and different graph data layouts. The data visualisations will be materialised in code using Python packages such as matplotlib, Seaborn, and various scikit-learn modules.

The course handout is available here: https://lse-st445.github.io/. 

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

Students will be expected to produce 7 problem sets in the MT.

• Mckinney, W., Python for Data Analysis, 2nd Edition, O’Reilly 2017
• Muller, A. C. and Guido, S., Introduction to Machine Learning with Python, O’Reilly, 2016
• Geron, A., Hands-on Machine Learning with Scikit-Learn & TensorFlow, O’Reilly, 2017
• Ramakrishnan, R. and Gehrke, J., Database Management Systems, 3rd Edition, McGraw Hill, 2002
• Obe, R. and Hsu, L., PostgreSQL Up & Running, 3rd Edition, O’Reilly 2017
• Robinson, I., Webber, J. and Eifrem, E., Graph Databases, 2nd Edition, O’Reilly 2015
• Wickham, Hadley. Ggplot2: Elegant Graphics for Data Analysis, Springer, 2009
• Murray, S., Interactive Data Visualisation for the Web, O'Reilly, 2013
• Matplotlib, https://matplotlib.org
• Seaborn: statistical data visualization https://seaborn.pydata.org
• Sci-kit learn, Machine learning in Python, http://scikit-learn.org

Project (70%) and continuous assessment (30%) in the MT.

Three of the problem sets submitted by students weekly will be assessed (30% in total). Each problem set will have an individual mark of 10% and submission will be required in MT Weeks 5, 8 and 10. In addition, there will be a take-home exam (70%) in the form of a final project in which they will demonstrate the ability to manage data and visualise it through  effective statistical graphics using principles they have learnt on the course. This may be done by publishing the visualisation and code to a GitHub repository and GitHub pages website.

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