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Academic Handbook BSc (Hons) Applied Digital and Technology Solutions (online)

NCHNAL6122 Predictive Analytics Using Python Course Descriptor

Course Title Predictive Analytics Using Python Faculty EDGE, Innovation Unit (London)
Course code NCHNAL6122 Teaching Period This course will typically be delivered over a 6-week period.
Credit points 15 Date approved March 2021
FHEQ level 6
Compulsory for Business Analyst Specialism, or, Data Analyst Specialism
Pre-requisites None
Co-requisites None

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

K1c Have in-depth knowledge and understanding of the underlying mathematical principles and concepts of machine learning and data mining.
K2c Have extensive knowledge of the data science workflow and the importance of data cleansing in professional data science.
K3c Have in-depth knowledge of dimension reduction strategies and their use for visualisation.

Subject Specific Skills

S1c Develop and apply computer programmes to perform machine learning and data mining tasks.
S2c Use programming to manipulate, cleanse and interrogate data.
S3c Use programming to visualise the results of data analysis.

Transferable and Professional Skills

T1ci Demonstrate advanced critical thinking and problem-solving skills.
T1cii Display an advanced level of technical proficiency in written English and competence in applying scholarly terminology, so as to be able to apply skills in critical evaluation, analysis and judgement effectively in a diverse range of contexts.
T2c Approach problems in a professional, structured manner.
T3c Effectively communicate to a range of stakeholders.

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.



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. 


AE   Assessment Type Weighting Online submission Duration Length
1 Written Assignment  60% Yes 2,000 words +/- 10%,  excluding data tables
2 Set exercise  40% Yes Requiring on average 10-20 hours to complete

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


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.


  • 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


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
Title: NCHNAL6122 Predictive Analytics Using Python

Approved by: Academic Board

Location: Academic Handbook/Programme specifications and Handbooks/ Undergraduate Online Programmes/Applied BSc (Hons) Digital & Technology Solutions/Course Descriptors

Version number Date approved Date published  Owner Proposed next review date Modification (As per AQF4) & category number
3.0 December 2022 January 2023 Dr Yu-Chun Pan June 2026 Category 3: Change to Teaching and Learning Strategy; Change to English Proficiency Learning Outcome

Category 1: Corrections/clarifications to documents which do not change approved content or learning outcomes

2.1 August 2022 August 2022 Scott Wildman June 2026 Category 1: Corrections/clarifications to documents which do not change approved content or learning outcomes
2.0 January 2022 April 2022 Scott Wildman June 2026 Category 3: Changes to Learning Outcomes
1.0 March 2021 Scott Wildman March 2026
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