Introduction to Data Science with Python

Topics

• 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:

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.

Teaching

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.

Assessment

• 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.

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.