Python Programming for Data Science: Introduction

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

• To learn the basic aspects of Python programming for data science.
• To gain an appreciation for the end-to-end process of obtaining data, processing it, through to presenting results.

• To be able to build a simple data processing pipeline by the end of the course.

Learning takes place on a weekly schedule. At the start of each weekly unit, students are provided with learning materials on our online platform, including one hour of pre-recorded video, often supplemented by guided readings and educational resources. These learning materials prepare students for a one-hour live webinar with an expert tutor at the end of each weekly unit which they attend in small groups. Webinars are held on Microsoft Teams, and provide the opportunity for students to respond to discussion prompts and ask questions. The blend of weekly learning materials that can be worked through flexibly, together with a live meeting with a tutor and their peers, maximise learning and engagement through interaction in a friendly, supportive environment.

At the end of the course, the student will be able to write procedural code using the Python language and tools to:

• import data from local and/or remote sources and preprocess it;
• extract significant information from the gathered data;
• visualise the relevant features extracted from the data;

After attending this course, students will know

• 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. (e.g. for sentimental analysis, semantic search or machine learning algorithms).

Students will be asked to submit a portfolio of exercises for their coursework assignment. I will give the first exercise midway through the course for early submission, the second to be completed at the end 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.

You will be set independent formative and summative work for this course. Formative work will be submitted for informal assessment and feedback from your tutor, but has no impact on your final grade. The summative work will be formally assessed as pass or fail.

Experience in using a programming or scripting language is beneficial. The basic elements of programming using the Python programming language will be introduced throughout the course. However, each student should consider that this course requires a certain amount of homework (2–3 hours per week) to familiarise with the concepts explained during the class. This is especially true for students who are not familiar with programming. This is a course on data science, so I will discuss some mathematical concepts even though I will try to keep these to a minimum. Expect some exposition to (1) linear algebra (e.g. matrices operations), (2) statistics, and (3) calculus. 

The Department's short online courses are taught at FHEQ Level 4, i.e. first year undergraduate level. FHEQ level 4 courses require approximately 10 hours study per week, therefore a total of about 100 study hours.

English Language Requirements

We do not insist that applicants hold an English language certification, but warn that they may be at a disadvantage if their language skills are not of a comparable level to those qualifications listed on our website. If you are confident in your proficiency, please feel free to enrol. For more information regarding English language requirements please follow this link: https://www.conted.ox.ac.uk/about/english-language-requirements

Before attending this course, prospective students will know:

• 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.