Introduction to Data Science with Python

Short Course - 15 credits at SCQF level 9
Introduction to Data Science with Python

This short course introduces students to the main techniques and issues involved in Data Science, introducing and using Python as a tool for working with data. The course covers ethical and legal issues alongside the practical application of the data science lifecycle stages (data cleaning, transformation, analysis, visualisation and reporting).

This course has one study option

Mode of Attendance

  • Online Learning

Mode of Study

  • Part Time

Start Date


Course Length

10 weeks


  • The Data Science lifecycle; frameworks for data science projects, and data strategies.
  • Professional, ethical and legal issues involved within data analysis; data bias.
  • Data exploration, data preparation and data cleaning methods.
  • Data summarisation, data transformation and data visualisation techniques.
  • Introduction to and use of Python libraries to process and analyse a range of data types, and to apply data science algorithms to datasets.
  • Statistical techniques for data analysis: summary statistics; visualising data distributions; regression; correlation; clustering; classification.

On completion of this short course, students will be able to:

  • Develop awareness of the data science lifecycle and data strategies.
  • Develop awareness of the professional, ethical and legal issues within data analysis.
  • Apply methods for cleaning and preparing data for analysis.
  • Apply statistical and visualisation techniques to a variety of datasets.
  • Apply algorithms to extract information from a dataset.
  • Effectively communicate results through appropriate conclusions and visualisation.

Upskilling Courses

In partnership with the Scottish Funding Council (SFC), our online upskilling short courses have been developed in response to feedback from businesses regarding their people and skills needs and are therefore helpful for individuals considering their employment options as well as organisations looking to upskill their employees. Find out more:




Modules and delivery order may change for operational purposes. The University regularly reviews its courses. Course content and structure may change over time. See our course and module disclaimer for more information.

This short course can be completed in 10 weeks. With 8 weeks to complete the online materials and exercises, and 2 weeks to apply techniques learned to a small capstone project.


8 weeks of teaching/learning activity as follows:

  • Recorded Lectures: approximately 2 hours/week in total, presented as short bite-sized (20-30 minute) lessons
  • Practical exercises: a range of guided exercises applying the techniques and principles covered in lectures learning to use and apply Python to load, analysis and visualise data.


  • Regular formative quizzes to check your understanding and progress.
  • A capstone project to bring together elements of the course, applying steps of the data science lifecycle to a dataset (which could optionally be workplace based) and presenting the analysis and conclusions in the form of a short report.

Independent Study

  • Materials and exercises are available online, allowing participants to study flexibly and independently at time and place to fit around existing work and life commitments.
  • Online tutor support and recorded code-along solutions to practical exercises.

Staff Delivering on This Course

The School has a strong history of successful, data-rich, industry and research projects in AI, applying Data analytics, Data Mining, Machine Learning, Evolutionary Computing and Optimisation techniques to areas such as energy, health care, transport, high performance computing, medicine and tourism.

Staff have developed and delivered a number of courses in the field at all levels, from BSc (Hons) to MSc to PhD. Current courses include the MSc in Data Science, the MSc IT with Business Intelligence and the BSc (Hons) in Data Science.

Staff are recognised for their Teaching and Support, with a number of staff receiving STAR awards year on year.


Academic Support

The Inclusion Centre advises and supports students who disclose a sensory or mobility impairment, chronic medical condition, mental health issue, dyslexia and other specific learning differences. Applicants are encouraged to arrange a pre-entry visit to discuss any concerns and to view the facilities.

Online Learning & Support

All online learning students, benefit from using our collaborative virtual learning environment, CampusMoodle. You will be provided with 24/7 online access to your learning material and resources, along with the ability to interact with your class members and tutors for discussion and support.

Study Skills Support

The Study Support Team provides training and support to all students in:

  • Academic writing
  • Study skills (note taking, exam techniques, time management, presentation)
  • Maths and statistics
  • English language
  • Information technology support

Library Support

The Library offers support for your course, including the books, eBooks, and journals you will need. We also offer online reading lists for many modules, workshops and drop-ins on searching skills and referencing, and much more.


Data Science and Analytics is a fast-growing area of investment for businesses and organisations who want to obtain a competitive advantage by:

  • Extracting and understanding the knowledge implicitly contained in data.
  • Using this knowledge to strategically make and justify improved business decisions, e.g.
    • Predict outcomes.
    • Diagnose faults.
    • Reduce lead/response time.

Python is used extensively in business and industry, and experience in using Python is a valuable and sought after skill.
Problem solvers who can draw value from business data are highly sought after for solving an ever increasing number of challenges in a data-rich world. Individuals with practical data analytics skills are well placed to make a difference in the creative solving of real-world problems.


There are no prerequisites for this course.


Academic Year 2022/23

  • Course fees will be met in full for students who qualify for Scottish Funding Council funding. To qualify for SFC funding, applicants must be resident in Scotland.
  • £350 entire course - Applicants who are not eligible for SFC funding or are currently receiving SAAS/SFC funding for other courses.

Additional Costs

The following course-related costs are not included in the course fees:


For new intakes course fees are reviewed and published annually for each mode of delivery. Tuition fees are fixed for the duration of a course at the rate confirmed in the offer letter.  For further information see:


Have a question about the course? Get in touch with the team and we'll do our best to help.

Get in touch

Taking this course, you will have access to some of the world's best facilities. 

We've invested more than £100 million in the development of our campus, new facilities and new resources.

University Wide Facilities

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