Short course
Python Programming for Data Science: Introduction
Course status:
Applications being accepted
Dates and time:
30/09/2026 - 02/12/202619:00 - 21:00
Study format:
In-person weekly
Fees:
£315.00
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 an introduction to programming for data science, using the Python programming language. The course seeks to introduce the basics of the data science process, from collecting data, pre-processing it (cleaning/correcting it), performing exploratory data analyses, visualizing data, and sharing analysis results.
The course will rely on Jupyter Notebooks for interactive Python programming as they are widely used in Data Science.
Before attending this course, students will need to 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.
If you choose to register for accreditation and assessment, to complete the assignment you 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 you should ensure that you also have access to a computer outside of class.
If you are unable to attend this course in Oxford, an online version is also available.
Book this course
Book your place online using the button below.
Course starts Wednesday 30 September 2026
This is an in-person course which requires your attendance at the weekly meetings in Oxford on Wednesdays, 7.00-9.00pm.
Week 1: Introduction to Data Science. Course set-up. Intro to Python: numbers
Week 2: Python basics: built-in types, functions, and methods, if statement
Week 3: Python data structures: lists, tuples, dictionaries, and sets; loops
Week 4: Numpy
Week 5: Pandas for data science I
Week 6: Pandas for data science II
Week 7: Data visualisation: matplotlib and seaborn
Week 8: Object-oriented programming: classes, inheritance, and applications
Week 9: Data gathering and cleaning. Text pre-processing for Natural Language Processing (NLP)
Week 10: Time Series Analysis
Course aims
The course aims to give students the opportunity to learn the basic aspects of Python programming for data science and to gain an appreciation for the end-to-end process of obtaining data, processing it, and presenting results.
Course objective
Learners will be able to build a simple data processing pipeline by the end of the course.
Learning outcomes
By the end of the course, students will have been given the opportunity to have learnt how to 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:
- 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 and seaborn 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 or spaCy to preprocess a text and convert it to a numerical representation that can be manipulated by information retrieval algorithms (eg for sentimental analysis, semantic search or machine learning algorithms);
- how to perform time series analysis.
Level and demands
The Department’s Weekly Classes are taught at FHEQ Level 4, ie first year undergraduate level.
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 some mathematical concepts will be discussed but this will be kept to a minimum. Expect some exposition to (1) linear algebra (eg matrices operations), (2) statistics, and (3) calculus.
IT requirements
The course will rely on Jupyter Notebooks for interactive Python programming as they are widely used in Data Science.
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.
Teaching methods
Each weekly session will consist of lectures and hands-on programming exercises, class discussions and interactive programming demonstrations by the tutor.
Assessment methods
Only those students who have registered for assessment and accreditation, in advance of the course start date, can submit coursework/assignments for assessment.
Assessment
You will be set two pieces of work for the course. The first of 500 words is due halfway through your course. This does not count towards your final outcome but preparing for it, and the feedback you are given, will help you prepare for your assessed piece of work of 1,500 words due at the end of the course. The assessed work is marked pass or fail.
Ms Nazneen Ali
I have a Master of Technology in Data Science and Engineering, a Master’s in Electronics, and a Bachelor’s in Computer Science(Hons). With over 15 years of teaching experience, I’ve helped students grow academically and professionally. My experience includes mentoring Data Science projects, particularly in Machine Learning, Deep Learning, and Python programming.
Academic credit
Credit Accumulation Transfer Scheme (CATS Points)
Please note, students who do not register for assessment and accreditation during the enrolment process will not be able to do so after the course has begun. If you wish to gain credit from completing this course you must register to do so before the course starts.
Only those who have registered for assessment and accreditation will be awarded CATS points for completing work to the required standard. Please note that assignments are not graded but are marked either pass or fail.
Learn more about the Credit Accumulation Transfer Scheme.
If you are enrolled on the Certificate of Higher Education at the Department you need to indicate this on the enrolment form but there is no additional registration fee for assessment and accreditation.
Digital certificate of completion
Students who are registered for assessment and accreditation and pass their final assignment will also be eligible for a digital Certificate of Completion. Information on how to access the 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 attended. You will be able to download the certificate and share it on social media if you choose to do so.
Fees
| Description | Costs |
|---|---|
| Course fee (with no assessment) | £315.00 |
| Assessment and Accreditation fee | £60.00 |
Funding
If you are in receipt of a UK state benefit, or are a care-leaver in the UK, 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
Recommended reading
Recommended reading is optional and you are not required to purchase these books to study this course.
Library facilities
All weekly class students may become borrowing members of the Rewley House Library, part of the Bodleian Libraries, for the duration of their course. Prospective students whose courses have not yet started are also welcome to use the Library for reference.
- More information about the Library can be found on the Bodleian Libraries website.
- This guide for Weekly Class students also provides further information.
Preparatory reading
- Python Crash Course, Third Edition / Matthes, Eric
Module code: O26P501COW
How to enrol
Please use the ‘Book now’ button on this page.
How to register for accreditation and assessment
To be able to submit coursework and to earn credit (CATS points) for this course, if you wish to do so, you will need to register and pay an additional £60 fee. You can do this by ticking the relevant box at the bottom of the enrolment form or when enrolling online.
Students who do not register for CATS points during the enrolment process will not be able to do so after the course has begun.
If you are enrolled on the Certificate of Higher Education at the Department you need to indicate this on the enrolment form but there is no additional registration fee.