Python Programming for Data Science: Intermediate

If you are in receipt of a UK state benefit, you are a full-time student in the UK or a student on a low income, you may be eligible for a reduction of 50% of tuition fees. Please see the below link for full details:

Concessionary fees for short courses

• Explore the landscape of contemporary machine learning (ML) and deep learning.
• Learn how to use a variety of machine-learning algorithms to extract features from the data using Python libraries.
• Familiarise with the concepts of overfitting and regularisation in ML.
• Gain insights on how to face scaling issues in a 'big data' scenario.

Each week's session will consist of pre-recorded lectures and hands-on programming exercises, class discussions and interactive programming demonstrations by the lecturer/ 

A the end of the course the students will be able to:

• choose the right ML task and evaluation metric for a given ML problem and select a set of ML models to be trained;
• set up a data pre-processing pipeline for data science and machine learning algorithms;
• use Python machine learning tools (namely scikit-learn, TensorFlow and PyTorch) to build up ML models, train and evaluate them on a test set;
• evaluate whether a model overfits or underfits the data and act accordingly (e.g. opportunely regularise and overfitting model);
• to identify the appropriate and most performant model for a given task and tune appropriately the hyperparameters (parameters that cannot be learned by the model).

Students will be asked to submit a portfolio of exercises for their coursework assignment. The first exercise will be given mid-way through the course, and the second due after the completion of the course. 

In order to complete the assignment (and in order to get the full benefit from the course) students will need access to a computer capable of running the open source software used in the course and access to the Internet. Only a limited amount of class time will be allocated to working on the assignment, so students should ensure that they have access to a computer outside of class.

Coursework is an integral part of all weekly classes and everyone enrolled will be expected to do coursework in order to benefit fully from the course. Only those who have registered for credit will be awarded CATS points for completing work the required standard.

Students must submit a completed Declaration of Authorship form at the end of term when submitting your final piece of work. CATS points cannot be awarded without the aforementioned form - Declaration of Authorship form

The Department's Weekly Classes are taught at FHEQ Level 4, i.e. first year undergraduate level, and you will be expected to engage in a significant amount of private study in preparation for the classes. This may take the form, for instance, of reading and analysing set texts, responding to questions or tasks, or preparing work to present in class.

Credit Accumulation and Transfer Scheme (CATS)

To earn credit (CATS points) you will need to register and pay an additional £30 fee per course. You can do this by ticking the relevant box at the bottom of the enrolment form or when enrolling online. Students who register for CATS points will receive a Record of CATS points on successful completion of their course assessment.

Students who do not register for CATS points during the enrolment process can either register for CATS points prior to the start of their course or retrospectively from the January 1st after the current full academic year has been completed. If you are enrolled on the Certificate of Higher Education you need to indicate this on the enrolment form but there is no additional registration fee.

Before attending this course, prospective students will know:

• All the requirements and topics covered in the "Python Programming for Data Science - Introduction" course, i.e:
• The fundamentals of linear algebra: what is a matrix and how matrix addition and multiplication are performed
• The following fundamental concepts of statistics: mean, median, variance and standard deviation, interquartile range
• The fundamentals of algebra: real and complex numbers, exponential and logarithm, and trigonometric functions
• How to perform fundamental Python operations such as variable creation, numerical operations on scalar, vectors and matrices, iteration through a collection, manipulation of elements in a collection.
• How to use NumPy and pandas to import a dataset and extract important statistics from it using techniques such as split-apply-combine (for example, finding the mean, median or max of a quantitative variable for each category in a categorical variable) 
• Given a dataset, how to select the appropriate visualisation graph depending on the information to be conveyed, and use the matplotlib library to draw it and add title, captions and figure legends.
• How to create and add state and behaviour to a class in Python
• How to use nltk to preprocess a text and convert it to a numerical representation that can be manipulated by information retrieval algorithms.
• What is, at least conceptually or visually, a derivative and a gradient.