NCHNAP782 Programming for Data Science Course Descriptor

Course Summary

This course focuses on programming for data science applications. It is fast-paced; accelerating the learner from fundamental programming principles to applied data science tasks. Learners will explore the functional and object-orientated programming language Python: its libraries, data structures, algorithm efficiency and capabilities for data manipulation and analysis. Software development practices will be introduced and conceptually applied to programming design.

Course Aims

• Train learners in programming languages and techniques applicable to data engineering.
• Train learners in programming languages for commercially beneficial scientific analysis and simulation.
• Give learners the tools to identify appropriate programming approaches for solving computational problems in the workplace.

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 (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

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

Lutz, M., (2011). Programming Python, Beijing; Farnham : O’Reilly

Nelli, F., (2015). Python Data Analytics : Data Analysis and Science Using Pandas, Matplotlib, and the Python Programming Language, Berkley, CA : Apress, New York, NY: Springer

O’Regan, G. (2017). Concise Guide to Software Engineering: From Fundamentals to Application Methods, Cham : Springer

Journals

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

Electronic Resources

Learners are encouraged to consult relevant websites on programming for data science.

Indicative Topics

Learners will study the following topics: 

• Python for data analytics and data engineering
• Testing and documentation
• Functional and Object-orientated design principles
• Software engineering principles