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

NCHNAL592 Data Analytics Course Descriptor

Course Title Data Analytics Faculty EDGE Innovation Unit London
Course code NCHNAL592 Teaching Period This course will typically be delivered over a 6-week period
Credit points 15 Date approved March 2021
FHEQ level 5
Pre-requisites None
Co-requisites None

Course Summary

This course introduces the subject of data analytics. Students will be taught how raw data is collected, stored, cleansed and interrogated in order to contribute to the needs of organisations. Four main areas of data analytics will be covered: descriptive, diagnostic, predictive and prescriptive. Students will apply industry-standard software and Python packages commonly used for data analytics, encompassing basic graphical, numerical and statistical tools. Additionally, students will have the opportunity to apply their knowledge of data analytics using industry-standard cloud-based technology e.g. using ServiceNow training.

Course Aims

  • Train students to collect, cleanse, wrangle, manipulate and interrogate data.
  • To allow students to explore and apply Python programming to manipulate and analyse data.
  • Train students to understand the issues with data and datasets and how to overcome them to ensure robust analyses.
  • To expose students to a range of datasets and data sources. 

Learning Outcomes

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

Knowledge and Understanding

K1b Have knowledge and critical understanding of analytical techniques used in the collection, manipulation, exploration and interrogation of raw data.
K2b Critically understand how to evaluate and improve the quality of data using techniques such as data cleansing and wrangling.
K3b Understand and critically evaluate descriptive, diagnostic, predictive and prescriptive analytics.

Subject Specific Skills

S1b Use industry standard tools for data analysis and to create bespoke algorithms using Python.
S2b Effectively use basic graphical and numerical reporting tools.

Transferable and Professional Skills

T1bi Develop logical analysis and conceptual thinking.
T1bii Demonstrate a sound technical proficiency in written English and skill in selecting vocabulary so as to communicate effectively to specialist and non-specialist audiences.
T2b Critically evaluate different approaches to problem solving within this field of study.
T3b Effectively communicate arguments, analyses and conclusions.

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: 

  • Online learning
  • Online discussion groups
  • Online 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 students with developmental feedback. 


AE   Assessment Type Weighting Online submission Duration Length
1 Set Exercise 60% Yes Requiring on average 20-30 hours to complete
2 Written assignment 40% Yes Requiring on average 10-20 hours to complete 1,500 words +/- 10%,  excluding data tables

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 student’s progress.

Indicative Reading

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


Kotu, V., (2019), Data Science: Concepts and Practice, Morgan Kaufmann

Winston, W., (2019), Business Analytics: Data Analysis & Decision Making, South-Western

McKinney, W., (2017), Python for data analysis: data wrangling with Pandas, NumPy, and IPython, Beijing: O’Reilly


Students are encouraged to consult relevant journals on data analytics. 

Electronic Resources

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

Indicative Topics

  • Data wrangling
  • Data cleansing
  • Python for data analytics
Title: NCHNAL592 Data Analytics

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 December 2022 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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