Programme Specification

FP3408 - Introduction to Programming and Data Science

1. Module Code

FP3408

2. Module Title

Introduction to Programming and Data Science

3. Module Level

4. Module Credit

20 CC 10 ECTS

5. Delivery Site(s)

Chester

6. Available to / Restrictions

N/A

7. Expected Student Learning Activity

N/A

Scheduled hours	Placement Hours	Independent Guided study
60	0	140

8. Attendance Guidance

Students are expected to attend all timetabled classes.

9. Module Content

Focusing on developing Python programming techniques.
Developing an understanding of different data and collection types.
Utilising control flow, loops and functions.
Working with the NumPy and Pandas libraries for Data Science
Exploring data visualisation with Seaborn and Matplotlib
Data pre-processing for machine learning
Building regression models to explore and utilise trends within data
Using a range of common techniques to measure performance of machine learning models
Exploring real life uses and practical examples of machine learning

10. Aims

To develop an understanding of programming in Python and to be able to code to a high degree of fluency.

To be able to use popular data science libraries in Python in order to be able to process and analyse real world data.

To understand the difference between regression and classification algorithms and be able to utilise their outcomes to make accurate predictions with data sets.

To develop technical skills in the Seaborn graphing library in order to show charts and graphs to visualise data and to show accuracies of models.

11. Methods of Learning and Teaching and Formative Assessment

Lectures accompanied by IT workshops, group work, in-class tests and problem-solving scenarios. Formative assessment will be ongoing and will comprise activities that enable a student to appraise their own learning and guide them towards the development of self-study.

12. Learning Outcomes

LO1 Efficiently use Python as a programming language and be able to implement core features such as classes, loops, functions and control flow in order to write a program that serves a specific purpose. Skills should be developed to structure code neatly to improve readability.

LO2 Identify the different data/collection types to accurately recognise situations where they should be used appropriately, and understand the mutability of each type.

LO3 Explore the NumPy, Pandas and Seaborn libraries and understand the roles they play in data management, pre-processing and data science. Libraries should be used together and in the process of building a model.

LO4: Develop a basic understanding of supervised regression techniques such as Linear, Logistic, Ridge and Lasso and understand how regression models can be used to predict continuous values from trends within data. A basic familiarity with the Scikit learn regression library should be developed.

13. Assessment and Reassessment Components and Weighting

13a. Assessment

	Component	Weighting %	Learning outcome(s) assessed	Assessment category
1	Programming Project	50%	3 and 4	Coursework
2	Exam 1 hour	50%	1 and 2	Practical Exam

13b. Reassessment

Reassessment will use the same mode of assessment.

14. Key References

Dawson, M. (2010). Python Programming for the Absolute Beginner, 3^rd Edition. Course Technology.

Lutz, M. (2013). Learning Python, 5^th Edition. O’Reilly Media.

Spiegelhalter, D. (2019). The Art of Statistics, Learning from Data. Pelican Books

Aurelien, G. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems. O’Reilly Media.

Wei-Meng, L. (2019). Python Machine Learning. Wiley 2019.

15. Module Leader

Dave Price-Williams

16. Module Assessment Board

Centre for Foundation Studies

17. Approved by

LTI Board of Studies

18. Date of Approval

Tue, 06 Jul 2021

Close - return to programme display page Print - launches the print options panel