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.
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.
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.
Essential Reading: W. Mckinney, Python for Data Analysis, 2nd Edition, O?Reilly 2017 A. C. Muller and S. Guido, Introduction to Machine Learning with Python, O?Reilly, 2016 A. Geron, Hands-on Machine Learning with Scikit-Learn & TensorFlow, O?Reilly, 2017 R. Ramakrishnan and J. Gehrke, Database Management Systems, 3rd Edition, McGraw Hill, 2002 Additional Reading:? NumPy, https://numpy.org/ Python Data Analysis Library, https://pandas.pydata.org/ Matplotlib, https://matplotlib.org Seaborn: statistical data visualization https://seaborn.pydata.org Sci-kit learn, Machine learning in Python, http://scikit-learn.org 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.