NCHNAP783 Data Driven Analytics Course Descriptor

Course Summary

The Data Driven Analytics course systematically explores the modern data science workflow. Learners will engage in hands-on learning using a range of software, platforms, tools and techniques. Learners will understand how to collect, store, extract, cleanse, analyse and visualise data. An emphasis of the course will be on scalable, high-performance architectures for big data storage and analytics. Learners will critically evaluate modern data science technologies and emerging trends and be able to make recommendations to business.  Learners will be able to formulate analyses questions and hypotheses and select and use the most appropriate methodology to solve complex data problems. This course will typically include hands-on training from industry standard technology, such as AWS, Azure, R and Tableau/Power BI.

Course Aims

• Train learners in how to store, manipulate, analyse and visualise data.
• Train learners in innovative, modern data analytics architectures and platforms used in organisations.
• Give learners the tools to formulate questions and hypotheses and problem solve to an advanced level. 

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: 

• Online learning
• Online discussion groups
• Online 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 (12 days x 7 hours) = 84 hours
• On-the-job learning (24 days x 7 hours) = 168 hours (e.g. 2 days per week for 12 weeks)
• Private study (4 hours per week) = 48 hours

Total = 300 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

Feedback

Learners will receive formal feedback in a variety of ways: written (via email or VLE correspondence) and indirectly through online discussion groups. Regular tri-partite reviews between the learner (apprentice), their apprenticeship advisor (provider) and workplace line manager (employer) formally monitor and evaluate the learner’s progress.

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

Schutt, R. and O’Neil, C. (2013). Doing Data Science : Straight Talk from the Frontline. Sebastapol, California : O’Reilly 

Provost, F. and Fawcett, T., (2013). Data Science for Business. O’Reilly Media

Kimball, R. (2008). The Data Warehouse Lifecycle Toolkit. Indianapolis, Ind. : Wiley

Journals

Learners are encouraged to read material from relevant journals on data analytics as directed by their Course Leader.

Electronic Resources

Learners are encouraged to consult relevant websites on data analytics.

Indicative Topics

Learners will study the following topics: 

• Data analytics
• Data storage
• Data visualisation