Theory and Applications of Data Analytics Course Descriptor

Course Summary

This course provides an introduction to the fundamental concepts and techniques of data analytics. The course will cover programming and data analytic skills in high-level programming languages such as Python, as well as fundamental concepts of data structures and algorithms. Students will gain an understanding of the data science workflow including data collection from structured and unstructured sources building the foundations of relational and non-relational databases, multi-dimensional arrays, linear algebra transformations, hypothesis testing, regression analysis, machine learning and data visualisation. There is a particular focus on resource efficiency and sustainable development.

Course Aims

The aims of the course are to: 

• Develop familiarity with an interactive computing and development environment.
• Develop a basic understanding of arrays and vectorised computation.
• Develop and manifest an elementary understanding of data structures and their functionality, and of methods of data transformation.
• Be able to load and clean data sets, summarise and compute descriptive statistics, and plot and visualise data.

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 course has a dedicated Virtual Learning Environment (VLE) page with a syllabus and range of additional resources (e.g. readings, question prompts, tasks, assignment briefs, discussion boards) to orientate and engage you in your studies.

The scheduled teaching and learning activities for this course are:

Lectures/labs. Contact hours are typically a mix of weekly lectures and lab sessions:

Version 1: All sessions in the same sized group, or

Version 2: most of the sessions in larger groups; some of the sessions in smaller groups

Faculty hold regular ‘office hours’, which are opportunities for students to drop in or sign up to explore ideas, raise questions, or seek targeted guidance or feedback, individually or in small groups.

Students are to attend and participate in all the scheduled teaching and learning activities for this course and to manage their directed learning and independent study.

Indicative total learning hours for this course: 150

Employability Skills

• Skills in writing and analysing complex code.
• Presentation skills in presenting code accordingly.
• Skills in organisation of written and coding discourse
• Skills in being able to read, understand and comprehend the code

Assessment

Formative

Formative assessment will build on the material taught in the classroom notebooks. The material will be in the form of end of session exercises and in some cases questions and answers. Oral explanations are also part of summative assessment.

Summative

Students will be assessed during the course by means of set assignments. Assessment will be in two forms:

The examination will consist of two written coding assignments which the student will have to do to the set guidelines for coding. The written assignment 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 (including via email correspondence); oral (within one-to-one tutorials or on an ad hoc basis) and indirectly through discussion during group tutorials. Student’s will also attend the formal meeting, Collections in which they will receive constructive and developmental feedback on their  performance. 

Feedback is provided on written assignments (including essays, briefings and reports) and through generic internal examiners’ reports, both of which are posted on the University’s VLE.

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

• Joel Grus (2019), Data Science from Scratch, 2^nd ed., O’Reilly: Boston.
• Wes McKinney (2017), Python for Data Analysis, 2^nd ed., O’Reilly: Boston.
• Hadrien Jean (2020), Essential Math for Data Science, O’Reilly: Boston.

Electronic Resources

Students can visit courses on Datacamp, Coursera and Udemy to watch videos on Python Programming.

Indicative Topics

• IPython: An Interactive Computing and Development Environment
• NumPy Basics: Arrays and Vectorised Computation 
• Pandas
• Data Transformation
• Summarising and Computing Descriptive Statistics
• Plotting and Visualisation
• Advanced Pandas – Data Aggregation and Group Operations.