NCHNAL592 Data Analytics Course Descriptor

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

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).

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 students 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 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.

Books

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

Journal

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