Python Programming for Data Science - Part 2

Overview

Data science is a discipline that uses scientific methods, processes and algorithms to extract meaningful information, knowledge and insights from structured and unstructured data.

The aim of this course is to provide insights on intermediate and advanced data science topics, using the Python programming language. The course will explore concepts such as machine learning, deep learning and natural language processing from a practical hands-down point of view. The focus will be on tools and methods rather than diving into the theoretical basis, in order to be appreciated by an audience with a minimal mathematical background.

Experience in using a programming or scripting language is a must. The student should master all the concepts explored in the course "Python Programming for Data Science - Part 1".

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. A limited amount of class time will be allocated to working on the class assignment, so students should ensure that they have access to a computer outside of class.

The course will rely on Jupyter Notebooks for interactive Python programming as they are widely used in Data Science.

Programme details

Course begins: 15 Apr 2024

Week 0: Course orientation

Week 1: Introduction to the course. Basic overview of Machine Learning. Linear Regression example.

Week 2: Overview of a data-science preprocessing pipeline.

Week 3: Supervised Learning: regression.

Week 4: Supervised Learning: classification.

Week 5: Decision Trees. Ensemble Methods. The Perceptron.

Week 6: Deep Learning: Feed-forward Neural Networks.

Week 7: Deep Learning: Convolutional Neural Networks (CNNs) for Image Processing. Recurrent Neural Networks (RNNs) for time series analysis.

Week 8: Dimensionality Reduction and Unsupervised Learning.

Week 9: Natural Language Processing (NLP): an overview. Word embeddings. RNNs for NLP.  Attention-based models  (Transformers).

Week 10: Autoencoders and Generative Adversarial Networks (GANs). Introduction to Reinforcement Learning.

The following Python libraries will be used during the course:

  • scikit-learn (weeks 2-5, week 8)
  • TensorFlow 2.x/keras (weeks 6, 7, 9, 10)
  • NumPy pandas, matplotlib, seaborn (throughout the course)
  • HuggingFace Transformers (week 9)

Digital Certification

To complete the course and receive a certificate, you will be required to attend and participate in at least 80% of the live sessions on the course and pass your final assignment. Upon successful completion, you will receive a link to download a University of Oxford digital certificate. Information on how to access this digital certificate will be emailed to you after the end of the course. The certificate will show your name, the course title and the dates of the course you attended. You will be able to download your certificate or share it on social media if you choose to do so.

Fees

Description Costs
Course Fee £280.00
Take this course for CATS points £10.00

Funding

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

Tutor

Ms Ellen Visscher

Ellen is completing her DPhil at the University of Oxford in statistical machine learning with applications in healthcare. Before commencing her PhD, she worked as a backend software developer using Python. She has experience teaching Python programming courses and other university courses. 

Course aims

  • Explore the landscape con 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.

Teaching methods

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

Learning outcomes

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 and TensorFlow) 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).

Assessment methods

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.

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

Application

Experience of using a programming or scripting language is a must. The student should master all the concepts explored in the course Python Programming for Data Science - Part 1 prior to enrolling on Part 2. If you have not participated in Python Programming for Data Science - Part 1 then you will need to provide details of your previous Python programming experience. We may need to come back to you seeking further information. 

To enrol, please download a PDF or Word version of the following document 

Enrolment form (editable PDF)

Enrolment form (Word)

Once completed please email the enrolment form to weeklyclasses@conted.ox.ac.uk where we will arrange your enrolment and send you an invoice for payment.

We will close for enrolments 7 days prior to the start date to allow us to complete the course set up. We will email you at that time (7 days before the course begins) with further information and joining instructions. As always, students will want to check spam and junk folders during this period to ensure that these emails are received.

To earn credit (CATS points) for your course you will need to register and pay an additional £10 fee per course. You can do this by ticking the relevant box at the bottom of the enrolment form or when enrolling online.

Level and demands

Students who register for CATS points will receive a Record of CATS points on successful completion of their course assessment.

To earn credit (CATS points) you will need to register and pay an additional £10 fee per course. You can do this by ticking the relevant box at the bottom of the enrolment form or when enrolling online.

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 at the required standard.

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.

Most of the Department's weekly classes have 10 or 20 CATS points assigned to them. 10 CATS points at FHEQ Level 4 usually consist of ten 2-hour sessions. 20 CATS points at FHEQ Level 4 usually consist of twenty 2-hour sessions. It is expected that, for every 2 hours of tuition you are given, you will engage in eight hours of private study.

Credit Accumulation and Transfer Scheme (CATS)

Selection criteria

Before attending this course, prospective students will know:

  • All the requirements and topics covered in the "Python Programming for Data Science - Part 1" 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