Predictive Analytics Using Programming Course Descriptor

Course Summary

This course introduces the end-to-end data-driven statistical modelling and predictive modelling approach using open source programming languages such as Python with applications and case studies in business contexts. The course 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; modelling steps such as model training, validation, and testing; and a range of business analysis investigative techniques. This course trains learners with fundamental knowledge in machine learning and data mining using programming languages (such as Python). The course encompasses the entire data analytics workflow. The course will also address the barriers that exist to effective data analysis between analysts and their stakeholders and how to avoid or resolve these.

Learning Outcomes

On successful completion of the course, learners 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).

Learners are required to attend and participate in all the formal and timetabled sessions for this course. Learners 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:

• Off-the-job learning and teaching (6 days x 7 hours) = 42 hours
• On-the-job learning (12 days x 7 hours) = 84 hours (e.g. 2 days per week for 6 weeks)
• Private study (4 hours per week) = 24 hours

Total = 150 hours

Workplace assignments (see below) will be completed as part of on-the-job learning.

Assessment

Formative

Learners 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

Assessment will be in two forms:

Feedback

Learners will receive formal feedback in a variety of ways: written (via email or VLE correspondence) and indirectly through online discussion groups. Learners will also attend a formal meeting with their Academic Mentor (and for apprentices, including their Line Manager). These bi- or tri-partite reviews will monitor and evaluate the learner’s progress.

Feedback is provided on summatively assessed assignments and through generic internal examiners’ reports, both of which are posted on the VLE.

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

Learners are encouraged to consult relevant journals on predictive analytics.

Electronic Resources

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

Indicative Topics

• Machine Learning
• Data Mining
• Data Cleansing

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