NCHNAL6122 Predictive Analytics Using Python Course Descriptor

Course Summary

This course introduces the end-to-end data-driven statistical modelling and predictive modelling approach in Python with applications and case studies. Includes all the data and modelling steps in a full modelling cycle; exploratory data analysis and data cleansing for outlier imputation and data normalisation; commonly applied modelling techniques such as classification, linear regression, and logistic regression; and modelling steps such as model training, validation, and testing.

Course Aims

• Trains students in the fundamentals of machine learning and data mining.
• Trains students in the practical application of machine learning and data mining using Python.
• Allows students to explore the full data analytic workflow.

Learning Outcomes

On successful completion of the course, students will be able to:

Knowledge and Understanding

Subject Specific Skills

Transferable and Professional Skills

Teaching and Learning

This is an e-learning course, taught throughout the year.

This course can be offered as a standalone short course.

Teaching and learning strategies for this course will include: 

• On-line learning
• On-line discussion groups
• On-line assessment

Course information and supplementary materials will be available on the University’s Virtual Learning Environment (VLE).

Students are required to attend and participate in all the formal and timetabled sessions for this course. Students are also expected to manage their self-directed learning and independent study in support of the course.

The course learning and teaching hours will be structured as follows:

• Learning and teaching (6 days x 8 hours) = 48 hours 
• Independent study = 102 hours 

Indicative total learning hours for this course: 150 hours 

Assignments (see below) will be completed as part of private study.

Assessment

Formative

Students will be formatively assessed during the course by means of set assignments. These will not count towards the final degree but will provide learners with developmental feedback. 

Summative

All summative assessments will be assessed in accordance with the assessment aims set out in the programme specification.

Feedback

Students will receive formal feedback in a variety of ways: written (via email or VLE correspondence) and indirectly through online discussion groups. Students will also attend a formal meeting with their Mentor. These reviews will monitor and evaluate the learner’s progress.

Feedback is provided on summative assessment and is made available to the student either via email, the VLE or another appropriate method.

Indicative Reading

Note: Comprehensive and current reading lists for courses are produced annually in the Course Syllabus or other documentation provided to learners; the indicative reading list provided below is used as part of the approval/modification process only.

Books 

• Said, A., and Torra, V., (2019), Data Science in Practice, Cham: Springer International Publishing : Imprint: Springer
• Lutz, M. (2011), Programming Python, Beijing; Farnham: O’Reilly
• Allen, B. (2015), Think Python: How to Think Like a Computer Scientist. Farnham: O’Reilly

Journals

Students are encouraged to consult relevant journals on predictive analytics. 

Electronic Resources

Students are encouraged to consult relevant electronic resources on predictive analytics. 

Indicative Topics

• Machine Learning
• Data Mining
• Data Cleansing